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Power spectral models of stationary earthquake-induced ground motion process considering site characteristics

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  • In this article, several spectral models describing the stationary stochastic process of earthquake ground motion are explored and compared. The Hu-Zhou spectrum, which is regarded as an improved model of the Kanai-Tajimi spectrum, is concerned. It is proven that the earthquake-induced ground acceleration process described by the Hu-Zhou spectrum is a twice filtered white noise process in essence, and two filters for modifying low-frequency components and moderate- and high-frequency components respectively are investigated. A total of 1946 strong earthquake records at different sites were employed to determine the parameters of spectral models, including the Kanai-Tajimi spectrum, the Clough-Penzien spectrum and the Hu-Zhou spectrum. The results showed that the Hu-Zhou spectrum fits well with the actual observed ground motions over the whole frequency range, and that it is not only distinct in physical meaning and concise in mathematical expression, but also reasonable in practice.
  • Huangqin tea (HQT, Fig. 1), also called Huangjin tea in Chinese, has a long history of consumption in China[1]. It is a healthy herbal tea crafted from the aerial parts of Scutellaria baicalensis Georgi, S. scordifolia Fisch, S. amoena C. H. Wrigh, and S. viscidula Bung[24]. Unlike traditional tea made from Camellia sinensis (L.) O. Kuntze, HQT lacks stimulating components and is renowned for clearing heat and dry dampness, eliminating toxins, promoting digestion, and soothing fire[1,5]. It can be enjoyed both as a hot beverage and as a cold drink, and modern research suggests that HQT exhibits several pharmacological properties, such as anti-inflammatory, chemopreventive effects of colorectal cancer[6], anti-aging[7], cardiovascular protection, hypoglycemic effect, hypolipidemic effect, anti-tumor, anti-bacterial, anti-influenza virus, and enhance human resistance[812]. The predominant compounds found in HQT are flavonoids and essential oils and its potential health benefits are likely attributed to the presence of distinctive flavonoids, including isocarthamidin-7-O-β-D-glucuronide, carthamidin-7-O-β-D-glucuronide, apigenin-7-O-β-D-glucopyranside, chrysin-7-O-β-D-glucuronide, scutellarin, baicalin, wogonoside, and chrysin[1318].

    Figure 1.  The aerial parts of S. baicalensis Georgi, dried and brewed Huangqin tea.

    HQT is made using simple ingredients and has a straightforward preparation method. Traditionally, the above-ground portions of Scutellaria species have been utilized for crafting HQT, with the differentiation between stems and leaves remaining unessential. During the hot summer season (July to August), the aerial parts (stem, leaves, flowers) of these species of Scutellaria from a single source are collected and cut into small sections, which are then directly dried for later use. Alternatively, freshly harvested branch and leaf sections may undergo a transformative process through multiple rounds of steaming and subsequent drying within a steamer. Once suitably conditioned, they find their place within sealed containers, poised for extended storage periods. The brewing ritual commences by incorporating 7−8 g of this tea into 2 L of water, with subsequent replenishments of water at intervals 2−3 times. As the brewing ritual concludes, HQT graces the senses with a resplendent golden infusion—earning it another name, 'Huangjin tea'.

    HQT boasts a range of health advantages and is frequently gathered from the upper sections of Scutellaria species plants cultivated in the vicinity, intended for tea consumption. In recent years, large planting bases have been established in several regions, such as Beijing, Inner Mongolia Wuchuan, Yakeshi, Hebei Chengde, Shanxi Province, and Shandong Province, to comprehensively develop S. baicalensis resources. HQT has gained more attention and is now produced by specialized operating companies in various grades, such as bulk tea, bagged tea, and unique tea culture[12].

    To provide up-to-date information on the chemical composition, bioactivity, and safety aspects of the Scutellaria genus from HQT, this review paper has collected related literature from various databases, such as PubMed, Web of Science, Sci Finder, Scopus, Baidu Scholar, China National Knowledge Internet (CNKI), Wanfang and Weipu Data. This paper aims to draw attention to the need for further research and application of HQT in preventing and managing certain chronic diseases.

    HQT can be derived from several Scutellaria species. S. baicalensis is the most extensively cultivated, with a significant biomass in its aerial parts. Consequently, the primary source of HQT is the aerial part of S. baicalensis, and research focused on these aerial parts is also the most extensive.

    The aerial part and the root of S. baicalensis share similarities in their primary constituents, notably the significant presence of flavonoids, which are regarded as the main active components. However, there are specific chemical differences between the above-ground and underground parts of S. baicalensis. Our preliminary experiments have revealed the disparities in chemical composition between these two parts[18]. Notably, the root of S. baicalensis finds its primary use in medicinal applications, characterized by notably higher expression levels of specific 4′-deoxyflavones compared to the aerial organs. In contrast, the above-ground parts of the plant are employed to prepare tea. This differentiation in utilizing these plant components reflects the accumulated wisdom of generations cultivated through extensive periods of tasting and experience.

    This section provided an in-depth review of the chemical composition of the plant from which HQT is derived. Additionally, a specific emphasis on reviewing the aerial parts of S. baicalensis, which serve as the primary source of HQT has been placed.

    In the 1970s, researchers began to study the chemical composition of the aerial parts of S. baicalensis. The flavonoids are the most abundant chemical components in the aerial parts (flowers, stems, and leaves), and about 54 flavonoid compounds were identified (Table 1, Fig. 2). Most of them are flavonoids, dihydro flavonoids, and glycosides. The main glycoside-forming sugars are arabinose, glucose, and glucuronide, with the most abundant glucuronide glycosides. The optimization of extraction processes can yield a remarkable total flavonoid content of up to 5% in the stems and leaves of S. baicalensis[19].

    Table 1.  Flavonoids of HQT.
    NumberNameFormulaSpeciesRef.
    12',5-Dihydroxy-3',6,7,8-tetramethoxyflavoneC19H18O8S. baicalensis[17]
    22',6-DihydroxyflavoneC15H10O4S. baicalensis[16]
    33',4',5,5',7-PentamethoxyflavoneC20H20O7S. baicalensis[16]
    44',5-Dihydroxy-3',5',6,7-tetramethoxyflavoneC19H18O8S. baicalensis[22,27]
    54',5-Dihydroxy-7-methoxyflavanoneC16H14O5S. baicalensis[16]
    65,4′-Dihydroxy-6,7,8,3′-tetramethoxyflavoneC19H18O8S. baicalensis[17]
    75,2′-Dihydroxy-6,7,8-trimethoxyflavoneC18H16O7S. baicalensis[17]
    85,2′-Dihydroxy-6,7,8,3′-tetramethoxyflavoneC19H18O8S. baicalensis[17]
    95,2′-Dihydroxy-7,8-dimethoxyflavoneC17H14O6S. baicalensis[17]
    105,2′-Dihydroxy-7,8,6′-trimethoxyflavoneC18H16O7S. baicalensis[17]
    115,6,7,3',4'-Pentahydroxyflavone-7-O-glucuronideC21H20O13S. baicalensis[13]
    125,6,7,4'-TetrahydroxydihydroflavoneC15H12O6S. baicalensis[17]
    135,6,7-Trihydroxy-4'-methoxyflavoneC16H12O6S. baicalensis[15]
    145,7-Dihydroxy-6-methoxyflavanonC16H14O5S. baicalensis[17]
    155,7,4′-Trihydroxy-6-methoxyflavanoneC16H14O6S. baicalensis[22]
    165,7,4'-TrihydroxyflavanoneC15H12O6S. baicalensis[13]
    17(2S)-5,7,8,4'-Tetrahydroxyflavanone 7-O-β-D-glucuronopyranosideC21H20O12S. baicalensis[21]
    18(2S)-5,6,7,4'-Tetrahydroxyflavanone 7-O-β-D-glucuronopyranosideC21H20O12S. baicalensis[21]
    196-Hydroxyluteolin-7-O-glucuronideC21H18O13S. baicalensis[13]
    207-MethoxychrysinC16H14O4S. baicalensis[16]
    21ApigeninC15H10O5S. baicalensis, S. amoena,
    S. scordifolia, S. viscidula
    [17,2125]
    22Apigenin-4'-glucopyransideC21H20O10S. baicalensis[17]
    23Apigenin-6-C-glucoside-8-C-arabinosideC26H28O14S. baicalensis[13]
    24Apigenin-7-O-β-D-glucopyransideC21H20O10S. baicalensis[17]
    25Apigenin-7-O-β-D-glucuronideC21H18O11S. baicalensis, S. amoena,
    S. scordifolia
    [13,23,24]
    26Apigenin-7-O-methylglucuronideC22H20O11S. baicalensis[28]
    27BaicaleinC15H10O5S. baicalensis, S. amoena,
    S. scordifolia, S. viscidula
    [2225]
    28Baicalein-7-O-D-glucopyransideC21H20O10S. baicalensis[22]
    29BaicalinC21H18O11S. baicalensis, S. amoena,
    S. scordifolia, S. viscidula
    [16,2325]
    30CarthamidinC15H12O6S. baicalensis[13,20]
    31Carthamidin-7-O-β-D-glucuronideC21H20O12S. baicalensis[22]
    32ChrysinC15H10O4S. baicalensis, S. amoena,
    S. scordifolia
    [13,2124]
    33Chrysin-7-O-β-D-glucuronideC21H20O9S. baicalensis, S. amoena[23,29]
    34DihydrobaicalinC21H20O11S. baicalensis[22,28]
    35Dihydrooroxylin AC21H20O11S. baicalensis[13]
    36GenkwaninC16H12O5S. baicalensis[16]
    37IsocarthamidinC15H10O6S. baicalensis[20,28]
    38Isocarthamidin-7-O-β-D-glucuronideC21H20O12S. baicalensis[28]
    39IsoschaftsideC26H28O14S. baicalensis[13,16]
    40IsoscutellareinC15H10O6S. baicalensis[21,30]
    41Isoscutellarein 8-O-β-D-glucuronideC21H18O12S. baicalensis[21]
    42Kaempferol 3-O-β-D-glucopyranosideC21H20O11S. baicalensis[13,28]
    43LuteolinC15H10O6S. baicalensis[22,30]
    44Norwogonin-7-O-glucuronideC21H18O12S. baicalensis, S. amoena[13,17,23]
    45Oroxylin AC16H12O5S. baicalensis, S. amoena[17,23]
    46Oroxylin A-7-O-D-glucopyransideC22H22O10S. baicalensis[22,28]
    47Oroxylin A-7-O-β-D-glucuronideC22H20O11S. baicalensis, S. amoena[13,23]
    48PinocembrinC16H12O5S. baicalensis[13]
    49Pinocembrin-7-O-glucuronideC21H20O11S. baicalensis[13,16,22]
    50SalvigeninC18H16O6S. baicalensis[21]
    51ScutellareinC15H10O6S. baicalensis[28]
    52ScutellarinC21H18O12S. baicalensis, S. amoena,
    S. scordifolia, S. viscidula
    [2325,29]
    53WogoninC16H12O5S. baicalensis, S. amoena,
    S. scordifolia, S. viscidula
    [2125]
    54WogonosideC22H20O11S. baicalensis, S. viscidula[13,25]
     | Show Table
    DownLoad: CSV
    Figure 2.  Chemical structure of flavonoids in HQT.

    In 1976, Takido et al.[20] isolated two flavanone derivatives, carthamidin and isocarthamidin, for the first time as natural products from the leaves of S. baicalensis. Later, Yukinori et al.[21] identified two new flavanones, (2S)-5,7,8,4'-tetrahydroxyflavanone 7-O-β-D-glucuronopyranoside and (2S)-5,6,7,4'-tetrahydroxyflavanone 7-O-β-D-glucuronopyranoside), in the leaves of S. baicalensis. Eight compounds of chrysin, wogonin, apigenin, salvigenin, scutellarein, isoscutellarein, apigenin 7-O-glucuronide, and isoscutellarein 8-O-glucuronide were also isolated. Wang et al.[15] used column chromatography to isolate seven flavonoids (wogonin, chrysin, 5,6,7-trihydroxy-4'-methoxyflavone, carthamindin, isocarthamidin, scutellarein, and chrysin 7-O-β-D-glucuronide) from a water extract of the leaves of S. baicalensis. Liu et al.[13] identified 21 flavonoids in the stems and leaves of S. baicalensis by HPLC-UV/MS and NMR, and found one flavonone (5,6,7,3',4'-Pentahydroxyflavone-7-O-glucuronide) was a new compound. Zhao[17] firstly isolated 5,6,7,4′-tetrahydroxyflavanone 7,5,7-dihydroxy-6-methoxyflavanone, oroxylin A, 5,4′-dihydroxy-6,7,8,3′-tetramethoxyflavone, 5,2′-dihydroxy-6,7,8,3′-tetramethoxyflavone, 5,2′-dihydroxy-7,8,6′-trimethoxyflavone, 5,2′-dihydroxy-7,8-dimethoxyflavone, 5,2′-dihydroxy-6,7,8-trimethoxyflavone, apigenin 4'-β-D-glucopyranoside, and apigenin-7-β-D-glucopyranoside from the aerial parts of S. baicalensis. Ma[22] firstly isolated 5,7,4'-trihydroxy-6-methoxyflavone, 5,4'-dihydroxy-6,7,3',5'-tetramethoxyflavone, from stems and leaves of S. baicalensis. Wang et al.[16] isolated 5,4'-dihydroxy-7-methoxyflavanone, genkwanin, 7-methoxychrysin, 3',4',5,5',7-pentamethoxyflavone from 60% ethanol extracts for stems and leaves of S. baicalensis for the first time. Also, the compounds of carthamidin-7-O-β-D-glucuronide, oroxylin A-7-O-β-D--glucuronide, and chrysin were isolated from this plant for the first time.

    The concentration of these chemical components in HQT varies depending on the plant part utilized. Employing the HPLC-DAD method, Shen et al.[18] established that the aerial parts (stems, leaves, and flowers) of S. baicalensis are rich in flavonoids, resembling the roots in composition but exhibiting significant disparities in content. The contents of isocarthamidin-7-O-β-D-glucuronide (106.66 ± 22.68 mg/g), carthamidin-7-O-β-D-glucuronide (19.82 ± 11.17 mg/g), and isoscutellarein-8-O-β-D-glucuronide (3.10 ±1.73 mg/g) were the highest in leaves. The content of apigenin-7-O-β-D-glucopyranoside (18.1 ± 4.85 mg/g) and chrysin-7-O-β-D-glucuronide (9.82 ± 5.51 mg/g) were the highest in flowers. HQT has a high content proportion of flavone glycosides, which is closely related to the activity of HQT. The concentrations of the nine main flavonoids in HQT infusions were measured using HPLC. The content of isocarthamidin-7-O-β-D-glucuronide (52.19 ± 29.81 mg/g) was the highest; carthamidin-7-O-β-D-glucuronide (31.48 ± 6.82 mg/g), chrysin-7-O-β-D-glucuronide (10.65 ± 0.40 mg/g) and apigenin-7-O-β-D-glucopyranside (5.39 ± 0.92 mg/g) were found at moderate levels in HQT samples. As for flavone aglycones, scutellarin (12.77 ± 1.14 mg/g), baicalin (1.88 ± 0.48 mg/g), isoscutellarein-8-O-β-D-glucuronide (2.84 ± 0.60 mg/g), wogonoside (0.23 ± 0.02 mg/g) and chrysin (0.03 ± 0.01 mg/g) has lower content in HQT[6].

    Although there are few studies on the chemical constituents of the aerial parts of S. amoena, S. scordifolia, and S. viscidula it has been shown that the compounds of the aerial parts are similar to S. baicalensis. The aerial parts of S. amoena contain the compounds of baicalein, baicalin, oroxylin A, oroxylin A-7-O-β-D-glucuronide, wogonin, chrysin, chrysin-7-O-β-D-glucuronide, norwogonin, 5,7-dihydroxy-6,8-dimethoxyflavone, scutellarin[23]. Zhang et al.[24] identified compounds of chrysin, wogonin, baicalein, apigenin, apigenin-7-O-β-D-glucoside, baicalin, and scutellarin in whole plants of S. scordifolia. The stems and leaves of S. viscidula all contain compounds of wogonoside, apigenin, baicalein, wogonin, baicalin, and scutellarin. The contents of baicalein, wogonoside, wogonin, and apigenin in the stem of S. viscidula were higher than those in the stem of S. baicalensis. In the leaves of the two species, the content of scutellarin was higher, while the content of other compounds was lower[25]. The content of scutellarin in S. viscidula was stem (2.30%) > leaf (1.78%) > flowers (0.38%)[26].

    The aerial parts of S. baicalensis are rich in essential oils, and the taste of HQT is closely related to this. The flowers of S. baicalensis are thought to have a Concord grape aroma, while HQT has a bitter flavor with distinctive herbal notes. Extensive analysis has identified 145 components in the essential oil obtained from the aerial parts of S. baicalensis. These components span various chemical classes, such as alkanes, carboxylic acids, fatty acids, monoterpenes/oxygenated monoterpenes, sesquiterpenes triterpenoids and Vitamins (Supplemental Table S1), which have demonstrated their efficacy in combatting bacteria, reducing inflammation and inhibiting tumor growth[2731]. Among these, major constituents include germacrene D (5.4%−39.3%), β-caryophyllene (29.0%), caryophyllene (18.9%), eugenol (18.4%), caryophyllene (15.2%), caryophyllene oxide (13.9%), (E)-β-caryophyllene (11.6%), 5-en-3-stigmasterol (11.3%), carvacrol (9.3%), thymol (7.5%), vitamin E (7.4%), neophytadiene (7.3%), γ-elemene (6.2%), 1-octen-3-ol (6.1%), allyl alcohol (5.5%), bicyclogermacrene (4.8%), myristicin (4.7%), acetophenone (4.6%), α-amyrin (4.6%), β-amyrin (4.4%), germacrene d-4-ol (4.3%), spathulenol (4.2%), β-pinene (4.1%), α-humulen (4.0%), 1-vinyl-1-methyl-2-(1-methylvinyl)-4-(1-methylethylidene)-cyclohexane (4.0%) are found in the aerial parts of S. baicalensis from different places[2731].

    Takeoka et al.[27] identified 64 components in volatile components of S. baicalensis flowers by solid-phase microextraction and analyzed them by GC and GC-MS. These flowers were collected at San Francisco State University (USA). Among the flower volatiles, the content of β-caryophyllene, germacrene D, δ-cadinene, γ-muurolene, and γ-cadinene were more than 3%. The essential oil obtained from the stem of S. baicalensis is mainly composed of diphenylamine, 2,2-methylenebis (6-tert-butyl-4-methylphenol), bornyl acetate, β-caryophyllene, germacrene D and 1-octen-3-ol.[32]. Gong et al.[28] analyzed and identified the specific chemical constituents of the aerial parts of S. baicalensis by using GC-MS technology and identified 37 compounds in total, such as allyl alcohol, acetophenone, caryophyllene, α-humulene, germacrene D, and γ-elemene. The plant material was collected in the Qinling Mountains in China. Lu et al.[29] found a big difference in essential oil components between the aerial and root of S. baicalensis from Kunming Botanical Garden, Yunnan Province (China). The aerial part of S. baicalensis mainly contained enols and sterols such as neophytadiene and vitamin E. However, it has the same compounds as the roots, such as nerolidol, hexadecanoic acid, 1,2-benzenedicarboxylic acid, squalene, stigmast-4-en-3-one, and partial alkanes. Recently, Wang et al.[31] found the essential oil level of the aerial parts of S. baicalensis was 0.09% (v/w, based on fresh weight) while its density was 0.93 g/mL, and obtained 31 components accounting for 97.64% of the crude essential oil, including sesquiterpenoid, monoterpenoids, phenylpropanoids, and others. It is also reported that the major components of the essential oil from the aerial parts of S. baicalensis were myristicin, eugenol, caryophyllene, caryophyllene oxide, germacrene D, spathulenol, and β-pinene, with eugenol as the most abundant. The sample of the aerial parts were harvested from Tangshan City (China). The composition of S. baicalensis essential oils varies according to the plant part used, geographical location, and growing conditions.

    Zgórka & Hajnos[33] identified the phenolic acid compounds of aerial parts of S. baicalensis by solid-phase extraction and high-speed countercurrent chromatography: p-coumaric acid, ferulic acid, p-hydroxybenzoic acid, and caffeic acid. Chirikova & Olennikov[34] found that the aerial part of S. baicalensis contains 11 kinds of saturated fatty acids and nine kinds of unsaturated fatty acids, among which the palmitic acid content is the highest. Chlorogenic acid, fernlic acid, protocatechuic acid, vanillic acid, rosmarinic acid, caffeic acid, p-hydroxybenzoic acid, and p-coumaric acid were also detected.

    Zhao [17] isolated four sterol compounds: β-sitosterol-3-O-β-D-glucoside, α-apinasterol, β-sitosterol, and four ester compounds: methoxyphaeophorbide, p-hydroxybenethyl ethanol hexadecanoic methyl ester, ethoxyphaeophorbide, and n-octadecanol, lutein from the aerial parts of S. baicalensis.

    It is reported that flavonoids and diterpenes are the two main groups of active constituents in the genus Scutellaria. However, only one diterpene (scutebaicalin) was identified in the stems and leaves of S. baicalensis[35].

    By atomic absorption spectrophotometry, Yuan et al.[36] determined the contents of 11 metal elements in different parts of S. baicalensis. It was found that the leaves and stems of S. baicalensis were rich in Mg, K, Cr, Ni, Co, Fe, Mn, and Pb. Meanwhile, Yan et al.[37] developed an inductively coupled plasma mass spectrometry method and determined 23 kinds of inorganic elements in the stems and leaves of S. baicalensis from eight regions. Although there were no differences in the types of inorganic elements in the stems and leaves of S. baicalensis from the different areas, the content of these elements varied significantly. Among these elements, Fe, Zn, Cu, Mn, Cr, Co, Ni, Sr, B, and Ni were essential human body elements. The content of Al (516.83 μg/g) and Fe (700.62 μg/g) was the highest, while the content of B (31.54 μg/g), Ti (23.10 μg/g), Mn (65.64 μg/g), Sr (62.27 μg/g), and Ba (89.68 μg/g) was relatively high.

    Olennikov et al.[38] studied the water-soluble polysaccharides from the aerial parts of S. baicalensis from Russia and found that the polysaccharides from S. baicalensis gradually accumulated before flowering and progressively decreased after flowering.

    Yan et al.[39] found that the stems and leaves of S. baicalensis were rich in amino acids, and there was no difference in the kinds of amino acids among different producing areas, but there was a significant difference in the contents of amino acids. The content of proline, threonine, glutamic acid, lysine, glutamine, and arginine was higher, and the content of methionine, hydroxyproline, and citrulline was low.

    Several studies have focused on the functional properties of HQT, with increasing attention given to the aerial parts of S. baicalensis as the main raw material for HQT production. S. baicalensis stems and leaves flavonoids (SSF) are considered the functional components of HQT. Modern pharmacology has shown that the flavonoids extracted from the stem and leaf of S. baicalensis have been found to possess anti-inflammatory, anti-bacterial, antiviral, antipyretic and analgesic, anti-tumor, hepatoprotective, antioxidant, hypoglycemic, hypolipidemic, detoxification, myocardial ischemia protection, brain injury protection, and immunomodulatory effects. However, few studies have been conducted on the individual flavonoid compounds in the total flavonoid extract from S. baicalensis. Therefore, this paper aims to summarize and supplement the current research on the functional properties of HQT and its primary raw material (S. baicalensis) extract.

    Injury and infection could lead to inflammation, which plays a key role in the accelerated pathogenesis of immune-mediated disease[40]. Tong et al.[41], Zhou et al.[42], and Zhao et al.[43] found that S. baicalensis stem-leaf total flavonoid (SSTF) could inhibit acute exudative inflammation caused by xylene, glacial acetic acid, and egg white and also have a significant inhibitory effect on chronic inflammatory of granulation tissue hyperplasia. Wang et al.[44] observed the effect of SSTF on the aerocyst synovitis of the rat model and found that it could reduce capillary permeability, reduce the aggregation of neutrophils and basophils in tissues, reduce histamine, bradykinin, and other substances that increase vascular permeability, which is conducive to the recovery of vascular permeability in inflammation. Studies have shown that the SSTF significantly inhibits specific and non-specific inflammatory responses and can regulate the body's cellular and humoral immune functions. The mechanism of action is closely related to the effective reduction of capillary permeability, inhibition of PGE2 and NO synthesis in vivo, reduction of TNF-α expression, and reduction of inflammatory exudation[45,46]. SSTF (200 mg/kg) could balance the CD+4 Tlymphocyte subsets Th1/Th2 cells and the related cytokines IL-10 and IFN-γ in the rheumatoid arthritis model[47]. SSTF (17.5, 35 and 70 mg/kg for 38 d) could significantly improve the impairment of relearning ability and retention ability on memory impairment and nerve inflammation in chronic cerebral ischemia rats, which might be due to the inhibition of the proliferation of astrocyte and balanced the expression of the inflammatory factors in the brain[48]. Besides, the extract of S. baicalensis stem-leaf shows anti-inflammation effects both in vitro and in vivo. In cultured macrophage cells (RAW 264.7), the extract of S. baicalensis stem-leaf showed a strong anti-inflammation effect, which inhibited the expression of IL-1β. Similarly, it suppressed the LPS-induced transcriptional activity mediated by NF-κB in fish aquaculture[49]. S. baicalensis stems and leaves (3, 6, and 12g /kg, gavage for 7 d, once per day) have anti-inflammatory effects on 2% carrageenan-induced acute pleurisy in rats, and the mechanism of action may be related to the reduction of the production of inflammatory factors and the down-regulation of TRPV1 signaling protein[50]. The combination of S. baicalensis stems- Polygonum cuspidatum (3.5, 7, and 14 g/kg, gavage for 7 d) has a protective effect on lipopolysaccharide-induced acute lung injury rats, and its mechanism may be related to down-regulating the expression of TRPV1 and inhibiting the levels of TNF-α and IL-1β in inflammatory cells[50]. It is also reported that S. baicalensis stem-leaf combined with Morus alba (4, 8, and 16 g/kg/d, gavage, 10 d) has a protective effect on rats with acute pneumonia induced by lipopolysaccharide, and the mechanism might be related to the reduction of inflammatory factors and the down-regulation of TRPV1 signaling pathway[51]. Besides, the network pharmacology showed that S. baicalensis stem-leaf could prevent and control COVID-19 by intervening in 30 targets and 127 pathways, potentially preventing and treating inflammation caused by COVID-19[52].

    Based on the studies, S. baicalensis stem-leaf extract shows promising anti-inflammatory properties. These effects are likely mediated through a combination of factors, including the modulation of immune responses, reduction of inflammatory mediators, and potential interactions with signaling pathways like TRPV1. However, it's important to note that while these studies provide valuable insights, further research, including clinical trials, is needed to establish the full extent of its benefits and its potential for therapeutic applications in humans.

    Scutellaria baicalensis stem and leaf aqueous extract exhibit different degrees of inhibition of 36 strains from 13 kinds of bacteria, such as Staphylococcus aureus, Staphylococcus, Streptococcus pneumoniae, alpha-hemolytic streptococcus, beta-hemolytic streptococcus and Escherichia coli. This shows that anti-bacterial activity against Staphylococcus aureus is strong (MIC50 0.94 g/L, MBC 0.94 g/L). In vivo (217 mg/kg), it protects against the death of mice infected by Staphylococcus aureus and shows a certain dose-dependence[53]. Zhang et al.[54] found that the stem and leaves of S. baicalensis against Staphylococcus aureus and Shigella dysenteriae with MIC values of 1 and 4 mg/mL, respectively. Besides, it is reported that the water extract of the aerial part of S. baicalensis could inhibit the growth of several common pathogenic bacteria in aquacultures, such as Aeromonas hydrophila, Edwardsiella tarda, Vibrio alginolyticus and V. harveyi[49].

    Zhao et al.[55] found that the active part of the stem and leaf of S. baicalensis could inhibit the cytopathic effect caused by 10 kinds of viruses such as Coxsackie B virus, influenza virus, parainfluenza virus, adenovirus, respiratory syncytial virus, and herpes simplex virus. It is suggested that the active parts of the stem and leaf of S. baicalensis can be used for the prevention and treatment of influenza virus, parainfluenza virus, coxsackievirus, and other related infectious diseases.

    These findings indicate that S. baicalensis stem and leaf extract possess anti-bacterial and antiviral properties, making it a potentially valuable natural resource for combating infections caused by various pathogens. However, while these results are promising, further research, including clinical trials, would be necessary to fully establish the effectiveness and safety of using S. baicalensis extract for preventing or treating bacterial and viral infections, including COVID-19.

    In a series of studies, Tong et al.[41] demonstrated that SSTF at a dosage of 20 mg/kg significantly reduced body temperature in rats with fever induced by subcutaneous injection of a 10% dry yeast suspension. Zhang et al.[56] conducted research on the antipyretic effect of scutellarin, an extract from S. baicalensis stems and leaves, in febrile rabbits and observed an antipyretic substantial impact induced by pyrogen. Yang et al.[57] conducted several animal experiments, where they discovered that intraperitoneal injection of effective doses of SSTF (42.2 and 84.4 mg/kg) exhibited a specific inhibitory effect on infectious fever in experimental animals. Moreover, intraperitoneal injection of appropriate SSTF doses (30.1, 60.3, and 120.6 mg/kg), as found in Yang et al.'s experiments[58], effectively inhibited the pain response in experimental animals. Furthermore, Zhao et al.[59] noted that SSTF exhibited a certain inhibitory effect on the pain response in experimental animals subjected to chemical and thermal stimulation.

    These findings suggest that S. baicalensis stem and leaf extract may have antipyretic and analgesic effects, particularly its total flavonoid component. These effects could be beneficial for managing fever and providing pain relief. However, as with any natural remedy, further research, including controlled clinical trials in humans, is necessary to fully understand the effectiveness, safety, and optimal dosing of S. baicalensis extract for these purposes.

    Amyloid protein (Aβ) has been widely recognized as the initiator of Alzheimer's disease (AD)[60]. The SSTF can improve cognitive function and delay the process of dementia. SSTF has been found to exert neuroprotective effects in AD animal models through various mechanisms. Ye et al.[61] demonstrated that oral administration of SSTF (50 mg/kg) could effectively improve cognitive function and reduce neuronal injury in Aβ25-35-3s -induced memory deficit rats. The underlying mechanisms may involve inhibiting oxidative stress and decreasing gliosis[62,63]. Furthermore, SSTF was shown to reduce Aβ-induced neuronal apoptosis by regulating apoptosis-related proteins Bax and Bcl-2[64]. Subsequent studies further validated the neuroprotective effects of SSTF. Cheng et al.[65] found that SSTF treatment inhibited neuronal apoptosis and modulated mitochondrial apoptosis pathway in composited Aβ rats. Ding & Shang[66] found that the SSF improves neuroprotection and memory impairment in rats due to its inhibition of hyperphosphorylation of multilocus Tau protein in rat brains. SSTF has also been found to exert neurogenesis-promoting effects by regulating BDNF-ERK-CREB signaling[12] and activating the PI3K-AKT-CREB pathway[67].

    In further studies, Ding et al.[11] proposed that the effect of SSF on promoting neurogenesis and improving memory impairment may be related to the regulation of abnormal expression of Grb2, SOS1, Ras, ERK, and BDNF molecules in the BDNF-ERK-CREB signaling pathway. Zhang et al.[68] found that SSF (25, 50, and 100 mg/kg) could significantly modulate okadaic-induced neuronal damage in rats, which provides a basis for evaluating SSF as a means to reduce tau hyperphosphorylation and Aβ expression in Alzheimer's disease. Cao et al.[69] found that SSTF (100 mg/kg, 60 d) may alleviate tau hyperphosphorylation-induced neurotoxicity by coordinating the activity of kinases and phosphatase after a stroke in a vascular dementia rat model. Gao et al.[70] demonstrated that the stems and leaves of S. baicalensis (SSF, 25, 50, and 100 mg/kg/d, 43 d) could inhibit the hyperphosphorylation of tau in rats' cerebral cortex and hippocampus induced by microinjection of okadaic acid, which may be related to the activities of protein kinase CDK5, PKA and GSK3β. Furthermore, Liu et al.[67] demonstrated that SSF (35, 70, and 140 mg/kg/d, 43 d) improved composited Aβ-induced memory impairment and neurogenesis disorder in rats through activated the PI3K-AKT-CREB signaling pathway and up-regulated the mRNA and protein expression of TRKB, PI3K, AKT, CREB and IGF2. More recently, a new study demonstrated that SSF (35, 70, and 140 mg/kg) alleviated myelin sheath degeneration in composited Aβ rats, potentially modulating sphingomyelin metabolism[71]. Collectively, these findings suggest that SSTF holds therapeutic potential for AD by targeting multiple Alzheimer's pathogenesis-related processes.

    Li et al.[72] confirmed that SSTF (5 mg/kg) could improve the behaviors and the numbers of dopaminergic neurons in the substantia nigra in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced Parkinson's disease in mice, and these beneficial activities appear to be associated with the reduction of the level of serum malondialdehyde.

    These studies suggest that S. baicalensis stem and leaf extract have the potential to exert neuroprotective effects in various neurodegenerative conditions, including Alzheimer's disease and Parkinson's disease. These effects could be attributed to its ability to modulate oxidative stress, apoptosis, signaling pathways, and protein hyperphosphorylation. However, as with any potential therapeutic agent, further research is needed to establish the full extent of its benefits, optimal dosages, and mechanisms of action, as well as its potential applications in human patients.

    In recent years, ischemic cerebrovascular disease has seriously threatened human health. Cerebral ischemia is one of the leading causes of death. It can occur in focal or global ischemia, with most cases associated with ischemic stroke[73]. Neuronal protection against oxidative damage has been proposed as a potential therapeutic strategy to avoid damage during ischemic stroke[74]. It is reported that SSTF can reduce neuronal apoptosis and free radical damage caused by heart and brain ischemia. Zhao et al.[75] have found that the pretreatment of SSTF (100 mg/kg/d) can protect the ischemia-reperfusion myocardium by enhancing the activity of the anti-oxidative enzyme, inhibiting lipid peroxidation and attenuating the oxygen-free radicals-mediated damage to the myocardium in rats. In further studies, Zhao et al.[76] proposed that SSTF (50, 100, or 200 mg/kg/d, 7 d) pretreatment could alleviate the neuronal damage incurred by ischemia-reperfusion, demonstrating a neuroprotective effect in focal ischemia-reperfusion rat model, which may involve the prohibition of the apoptosis of the neurons. Yu et al.[77] confirmed that SSTF (17.5, 35, and 70 mg/kg/d, 7 d) could attenuate cardiomyocyte apoptosis during ischemia reperfusion injury by down-regulating the protein expression of the JAK2 gene. Qin et al.[78] found that SSF (17.5, 35, and 70 mg/kg/d, 38 d) can decrease the expression of the NMDAR in hippocampus, and increase the expression of VEGF in the cerebral cortex of chronic cerebral ischemia rats.

    Focal cerebral ischemia-reperfusion can result in neuronal loss but strongly promotes activation and proliferation of hippocampal glial cells. Losing hippocampal neurons is considered one of the basic pathological mechanisms of cognitive impairment[79]. Zhao et al.[79] found that the pretreatment with SSTF (100 and 200 mg/kg) could improve neurological function after focal cerebral ischemia-reperfusion injury, with preventive and protective effects. Shang et al. found flavonoids from S. baicalensis (35−140 mg/kg) could attenuate neuron injury and improve learning and memory behavior in rats with cerebral ischemia/reperfusion[80]. In further studies, Kong et al.[81] found that the mechanisms of the protective effects on the brain against cerebral ischemia/reperfusion injury of SSTF may involve decreasing the content of brain water, increasing microvascular recanalization, reducing the apoptosis of hippocampal neurons, and attenuating free radical damage. Bai et al.[82] proposed that SSTF (100 mg/kg/d, 7 d) could protect the neurological function in rats following I/R injury by alleviating the damage to the ultrastructure of cerebral cortex neurons and synapse. Yan et al.[83] found that SSTF (100 mg/kg/d, 7 d) pretreatment can exert preventive, protective effects on cerebral tissue by relieving brain edema, decreasing neural damage, promoting microvascular repatency, and increasing enzyme activity. It has been reported that the SSTF may protect neurons and their synaptic structures in multiple ways, but whether this mechanism enhances the resistance of neurons to damage or increases the repair function remains to be further explored.

    Essential hypertension is a common chronic cardiovascular disease, which can lead to multiple target organ damage, such as heart, brain, and blood vessels. It is a risk factor for coronary heart disease, heart failure, and other cardiovascular diseases. It is reported that SSTF (17.5, 35.0, and 70 0 mg/kg, 8 weeks) can inhibit myocardial remodeling in primary hypertensive rats, and the medium dose exerts the best inhibitory effect, and the mechanism may be related to inflammatory response induced by inhibiting the NF-κB signaling pathway[84].

    S. baicalensis stem and leaf extract have the potential to provide neuroprotective effects in conditions related to ischemic cerebrovascular disease and hypertension. Its ability to modulate oxidative stress, inflammation, and apoptotic pathways appears to contribute to its beneficial effects. However, further research is needed to fully understand the mechanisms and optimal usage of SSTF for these therapeutic purposes.

    Aging is associated with the deterioration of physiological function and the decline of cognitive ability[85]. It is reported that the alcohol extracts from roots, stems, leaves, and flowers of S. baicalensis (400 mg/kg, 7 weeks) could regulate the content of differential metabolites in urine samples of D-gal-induced aging-model rats to different degrees and play a certain role in improving the metabolic disorders of aging rats[7]. A further study investigated the anti-aging effects and potential mechanisms of S. baicalensis leaves and flower extract. S. baicalensis leaves (400 and 800 mg/kg, 7 weeks) have an anti-aging effect, which can improve the acquired alopecia, slow response, and other characteristics of aging rats, increase the spontaneous activity of aging rats, and reduce the damage of lipid peroxidation and glycosylation induced by D-galactose[86]. The S. baicalensis flowers extract (400 and 800 mg/kg, 7 weeks) could effectively reverse the cognitive decline and oxidative stress injury and alleviate liver pathological abnormalities in the D -galactose-induced aging rats, which are involved in the glutamine-glutamate metabolic pathway[85].

    S. baicalensis extracts, particularly those from leaves and flowers, may have anti-aging properties by regulating metabolic disorders, improving cognitive function, reducing oxidative stress, and alleviating aging-related physiological abnormalities. However, further research is necessary to fully understand the mechanisms underlying these effects and to determine the potential of these extracts for human applications in addressing age-related issues.

    The SSTF (200 mg/kg d, 35 d) can reduce the joint damage of collagen-induced arthritis mice and balance the CD+4 T lymphocyte subsets Th17 and Treg cells[87]. Besides, on the multiple sclerosis model, the SSTF (100, 200 mg/kg d, 16 d) displayed a protective effect on experimental autoimmune encephalomyelitis rats through a balance of the CD+4 Tlymphocyte subsets Th17 and Treg cells[88]. Zhang et al.[89] have found that SSTF attenuated EAE disease severity, accompanied by enhanced Treg frequency and level of Treg-associated cytokines (IL-10 and TGF-β), as well as downregulated Th17 frequency and expression of Th17-related cytokines (IL-17 and IL-23).

    It is reported that the essential oils from the aerial parts of S. baicalensis showed toxicity against booklice (Liposcelis bostrychophila) with an LC50 of 141.37 μg/cm2. The components of myristate, caryophyllene, eugenol, and caryophyllene oxide displayed dramatic toxicity against the L. bostrychophila, with LC50 values of 290.34, 104.32, 85.75, and 21.13 μg/cm2, respectively[31].

    Guo & Xu[90] have found that SSTF could inhibit the proliferation of Hela cells obviously (p< 0.01). Tang et al.[10] have found that SSTF could play an anti-colon cancer role by up-regulated the expressions of Cleaved Caspase-3 and the ratio of Bax/Bcl-2 (p < 0.05 and 0.01) and significantly down-regulated the expressions of MMP-2 and MMP-9 in HCT116 cells (p < 0.05 and 0.01). Recently, Shen et al.[6] studied the chemopreventive effects of HQT against AOM-induced preneoplastic colonic aberrant crypt foci in rats and found HQT inhibits AOM-induced aberrant crypt foci formation by modulating the gut microbiota composition, inhibiting inflammation and improving metabolomic disorders.

    Cardiovascular disease is one of the most important threats to human health. Hyperlipidemia is a major risk factor for atherosclerosis, which can cause various cardiovascular and cerebrovascular diseases. Different doses of SSTF (50, 100, and 200 mg/kg) can effectively reduce the body weight increase of rats with hypertriglyceridemia, reduce the serum levels of triglyceride(TG), total cholesterol(TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), which indicate that SSTF has the effect of regulating blood lipid[91].

    Oxidative stress is important in developing tissue damage in several human diseases[92]. The antioxidant capacities of separated organs (flower, leaf, stem, and root) of S. baicalensis were conducted by DPPH, ABTS+, and RP methods, respectively. The results showed that the antioxidant activity of the root (66.9 ± 0.3, 121.6 ± 0.5, and 80.2 ± 0.4 μg/mL) was the highest, followed by the leaf (68.4 ± 1.3, 128.2 ± 2.1, and 135.8 ± 2.0 μg/mL), stem (127.8 ± 3.1, 199.2 ± 1.7, and 208.2 ± 8.3 μg/mL) and flower (129.8 ± 6.3, 285.7 ± 4.7, and 380.3 ± 14.2 μg/mL)[93]. The antioxidant activities of the extracts of the aerial part of S. baicalensis and separated organs were conducted by DPPH assay and reducing power method with the antioxidant ability 7.73–8.83 mg TE/g DW and 51.48–306.09 mg TE/g DW, respectively. The content of total polyphenols and total flavonoids were significantly positively correlated with the reducing power[94]. Liu et al.[95] found that the flavonoids extracted from the stems and leaves of S. baicalensis (SSF, 18.98, 37.36, and 75.92 μg/mL) could protect rat cortical neurons against H2O2-induced oxidative injury in a dose-dependent manner. Cao et al. found that SSTF could alleviate the damage of human umbilical vascular endothelial cells injured by H2O2 and reduce their apoptosis, which may be related to the increasing level of Bcl-2[96].

    Preventive treatment with SSTF (50, 100, and 200 mg/kg) could significantly inhibit the blood glucose increase induced by alloxan in mice, and SSTF treatment could reduce the blood glucose level in diabetic mice. Both the prevention group and the treatment group could increase the activity of serum superoxide dismutase and decrease the content of malondialdehyde[97]. Liu et al.[98] found that SSTF (75 and 150 mg/kg) could significantly reduce blood glucose and blood lipid and improve insulin resistance in type 2 diabetic rats with hyperlipidemia.

    Yang et al.[99] found that SSTF (35 mg/kg/d, 8 weeks) could resist hepatic fibrosis by inhibiting the expression of α-smooth muscle actin in Hepatic Stellate Cells. In vivo, it is reported that SSTF (50, 100, and 200 mg/kg) could significantly reduce alanine transaminase activities in serum, increase the expression of superoxide dismutase and reduce the content of malondialdehyde in acute hepatic injury mice induced by carbon tetrachloride and ethanol[100].

    These findings suggest that S. baicalensis stem and leaf extract hold promise in promoting various aspects of health, including immune modulation, antioxidant activity, anti-tumor effects, cardiovascular health, oxidative stress protection, and more. However, further research, including clinical studies, is necessary to better understand the full therapeutic potential and safety of these effects in human applications.

    Flavonoids are considered the main active components in HQT. As the active substance basis of HQT, the safety of SSTF has also been investigated. After 90 d of oral administration of SSTF (0.5, 1, and 2 g/kg) to rats, no abnormal changes were observed in all indexes, and no delayed toxic reactions or obvious toxic reactions were observed, indicating that the toxicity of SSTF is low[101]. The LD50 value of SSTF was 14.87 g/kg is equivalent to 68.5 times the maximum dose in the pharmacodynamic test of mice, and the experiment confirms the safety of oral administration of the SSTF. Intraperitoneal injection of SST showed certain toxicity in mice, with an LD50 value of 732.11 mg/kg[102]. Liu et al.[103] conducted a systematic safety assessment experiment on the aqueous extract of S. baicalensis stem and leaves based on the China National Standard 'Guidelines for the Safety Evaluation of Food Toxicology (GB15193-2014)'. The results indicated that S. baicalensis stem and leaves are non-toxic, non-teratogenic, and non-mutagenic. Acute toxicity tests in mice revealed a Maximum Tolerated Dose of 15.0 g/kg. A 90-d feeding trial showed no changes in toxicological damage in animals, even at a high dosage of 8.333 g/kg (equivalent to 100 times the recommended human daily intake), suggesting the safety and non-toxicity of consuming S. baicalensis stem and leaves. In addition, HQT has long been used in folklore, and no toxicity has been reported.

    These findings collectively indicate that HQT is generally safe for consumption. However, as with any herbal product, it's important to follow recommended dosages and consult healthcare professionals, especially for individuals with pre-existing health conditions or medications.

    In recent years, HQT, a non-Camellia tea with a long history in China, has attracted attention due to its diverse pharmacological activities. Among various Scutellaria species, S. baicalensis is the most extensively studied and cultivated source for HQT. The aerial parts, including flowers, stems, and leaves, serve as the principal source of HQT preparation. HQT are rich in flavonoids and volatile components with various beneficial effects. To date, about 295 compounds have been identified from HQT, including approximately 54 flavonoid compounds and 145 volatile components identified online. The current research on the activity of HQT primarily focuses on flavonoid compounds, with limited studies on the larger quantity of volatile oil compounds.

    Additionally, the processing and brewing techniques used to prepare HQT may influence the bioactivity of its flavonoid content, although few studies have investigated this. More research is necessary to optimize the processing and brewing techniques to maximize the health benefits of HQT. Comparative studies reveal that the aerial parts of S. baicalensis, while sharing similarities with the roots, contain varying flavonoid compositions. Limited research on S. scordifolia, S. amoena, and S. viscidula, indicates the presence of comparable flavonoid compounds in their aerial parts. Although individual flavonoids like baicalin, wogonin, and scutellarin have demonstrated various therapeutic properties, it is essential to consider the synergistic effects of these compounds when consumed together in the form of tea. These findings contribute to laying the groundwork for quality assessment of HQT and offer insights into potential health benefits.

    HQT is mainly derived from the aerial parts of S. baicalensis. Recent studies have increasingly recognized the pharmacological value of the aerial parts of S. baicalensis. Preliminary pharmacological studies have shown that the aerial parts of S. baicalensis may possess beneficial activities in antioxidant, anti-tumor, antiviral, anti-bacterial, protection of ischemia-reperfusion injured neural function, neuroprotective effects against brain injury, and blood lipid regulation. These findings suggest that the value of using HQT may be attributed to these pharmacological activities. Although HQT is generally safe for consumption, further investigation is required to understand its safety profile, particularly in special populations such as pregnant or lactating women, children, and individuals with pre-existing medical conditions. Additionally, potential interactions between the flavonoids in HQT and conventional medications must be examined to ensure their safe and effective use with pharmaceutical treatments. In addition, although the safe dose of HQT on rodents has been studied, the safe dose for humans has yet to be determined.

    In conclusion, these initial research results support the potential health benefits of HQT and encourage more in-depth studies on its raw materials. Further studies are necessary to elucidate the synergistic effects of the flavonoids in HQT, optimize the processing and brewing techniques for maximum bioactivity, and investigate the safety profile and potential interactions with conventional medications. A comprehensive understanding of HQT will contribute to developing evidence-based recommendations for promoting health and well-being.

    The authors confirm contribution to the paper as follows: Conceptualization and writing: Quan Y, Li Z, Meng X, Li P, Shen J; Figure and table modification: Quan Y, Li Z, Meng X, Li P; review and editing: Wang Y, He C, Shen J. All authors reviewed the results and approved the final version of the manuscript.

    The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

    This research was funded by the Shandong Provincial Natural Science Foundation, China (ZR2022QH147, ZR2022QH165) and the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (No. ZYYCXTD-D-202005).

  • The authors declare that they have no conflict of interest.

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  • Cite this article

    Chen B, Sun G, Li H. 2022. Power spectral models of stationary earthquake-induced ground motion process considering site characteristics. Emergency Management Science and Technology 2:11 doi: 10.48130/EMST-2022-0011
    Chen B, Sun G, Li H. 2022. Power spectral models of stationary earthquake-induced ground motion process considering site characteristics. Emergency Management Science and Technology 2:11 doi: 10.48130/EMST-2022-0011

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Power spectral models of stationary earthquake-induced ground motion process considering site characteristics

Emergency Management Science and Technology  2 Article number: 11  (2022)  |  Cite this article

Abstract: In this article, several spectral models describing the stationary stochastic process of earthquake ground motion are explored and compared. The Hu-Zhou spectrum, which is regarded as an improved model of the Kanai-Tajimi spectrum, is concerned. It is proven that the earthquake-induced ground acceleration process described by the Hu-Zhou spectrum is a twice filtered white noise process in essence, and two filters for modifying low-frequency components and moderate- and high-frequency components respectively are investigated. A total of 1946 strong earthquake records at different sites were employed to determine the parameters of spectral models, including the Kanai-Tajimi spectrum, the Clough-Penzien spectrum and the Hu-Zhou spectrum. The results showed that the Hu-Zhou spectrum fits well with the actual observed ground motions over the whole frequency range, and that it is not only distinct in physical meaning and concise in mathematical expression, but also reasonable in practice.

    • Due to the influence of fault mechanisms, focal characteristics, propagation medium, propagation path and site characteristics, strong ground motion is considered as a random process in time and space, resulting in the seismic response of the structure as a random process[1,2]. Complex influencing factors make it difficult to simulate and accurately predict the strong ground motion with deterministic models. It is necessary to establish a reasonable random model of ground motion to study the statistical characteristics[3,4]. The power spectral density function (PSD) is used to describe the frequency domain distribution law of energy in the process of strong ground motion[5]. It can also provide statistical characteristic standards for synthetic random ground motion samples. It is an important tool to describe the random characteristics of strong ground motion.

      Housner[1] first proposed to use the stationary stochastic process model to describe the ground motion. The model assumes that the seismic ground acceleration is a stationary white noise. Subsequently, Kainai[6] and Tajimi[7] proposed a Gaussian filtered white noise model (K-T model). The model assumes the ground motion to be a stationary random process and ground as SDOF system. To date, seismologists and engineers have been committed to the modeling of engineering ground motion, and put forward a variety of ground motion models. The existing models for the stochastic simulation of earthquake ground motions are classified into two main categories. The first category, usually referred to as ‘source-based’ models, comprise physical models that are heavily dependent on seismological principles and describe the fault rupture mechanism and resulting propagation of seismic waves[812]. The second category consists of models developed to generate simulated waveforms either similar to a target seismic record, forming the ‘site-based’ model category[13,14], or compatible to a designated response spectrum, constituting the ‘spectrum compatible’ model category[15,16]. These models can be generalized as stationary stochastic models and non-stationary stochastic models.

      Most stationary stochastic models are obtained by concatenating different forms of linear filters on the basis of the K-T model[1724]. Recently, Muscolino et al.[25] analyzed the spectral content of a large set of accelerograms recorded on rigid soil deposits. Then, ground motion acceleration was modeled as a zero-mean stationary Gaussian random process. With the accumulation of earthquake damage, engineers generally realize that earthquake ground motion is non-stationary in both time and frequency domains. Temporal non-stationarity refers to the variation in the intensity of the ground motion in time, whereas the spectral non-stationarity refers to the time variation of the frequency content[26,27]. The existing non-stationary ground motion models mainly include: the filtered white noise model[26], the filtered Poisson pulse model[28], the autoregressive moving average model (ARMA)[2931], and the spectral representation method[3235]. However, a one dimensional horizontal component stationary model is the basis of complex ground motion models such as multidimensional model[3640], spatial model[4144], and non-stationary model[4547]. The evolutionary PSD function introduced by Priestley[32,48] is most widely used in non-stationary stochastic processes. The function is also based on the stationary random process, and the time-varying intensity envelope function is added[49]. Liu's research[50] shows that the frequency content of earthquakes may be different in the initial and intermediate stages. However, in the strong earthquake stage, the frequency content of the earthquake is roughly unchanged[51]. When analyzing the seismic response of linear structures, the stationary stochastic model can generally achieve satisfactory accuracy. Although this approach is approximate, it has been proven by practice that the stationary stochastic model can indeed solve some major problems in the seismic analysis and design of some engineering structures.

      In this paper, the physical meaning of the Hu-Zhou model is interpreted, and the frequency parameter to restrain the low frequency content of earthquake ground motions is discussed. The autocorrelation function of the Hu-Zhou model is deduced by the state space method. These results will provide a basis for random response analysis of the seismic structures in time domain. Furthermore, taking the Hu-Zhou model as an example, the process of solving the parameters of power spectrum model by least square method is introduced in detail. Finally, 1946 seismic records from different sites and different fault distances were selected. The parameters of the Kanai-Tajimi spectral model, the Clough-Penzien spectral model and the Hu-Zhou spectral model were fitted using the least-squares method. The obtained power spectrum parameters can adapt to the seismic design codes worldwide, and are of great significance to improve the seismic performance and toughness of urban and rural building structures.

    • The power spectral density function of stationary Gaussian process with the power spectrum of Kanai-Tajimi is expressed as:

      SKT(ω)=ω4g+(2×βg×ωg×ω)2(ω2gω2)2+(2×βg×ωg×ω)2×S0 (1)

      Where, S0 is the constant spectral intensity of the rock motions; ɷg and βg represent the frequency and damping ratio characteristic of the site respectively.

      Housner & Jennings[52] suggested ɷg = 15.6 rad/s and βg = 0.64 for hard site conditions. Based on the Fourier spectrum analysis of 247 actual seismic records, Moayyad & Mohraz[51] obtained three types of power spectrum curves: soft ground, mediate ground and hard ground. Based on this data, Sues et al.[53] obtained the specific parameter values of K-T spectrum under three different site types. Figure 1a shows the parameters of K-T spectrum under three site types. It is not difficult to see that from soft ground to firm ground, the dominant frequency of the site gradually increases, while the damping ratio of the site gradually decreases. The power spectrum curve of firm ground contains more frequency components.

      Figure 1. 

      Introduction of K-T spectrum. (a) K-T spectrum parameters given by Housner et al.[52] and Sues et al.[53] according to site type. (b) Physical mechanism interpretation.

      The K-T model assumes that the movement process of rock caused by earthquake is an ideal white noise process with zero mean value, and the overburden layer is simplified as a linear single degree of freedom system (a second-order linear low-pass filter). The filter equations are Eqs (2) and (3). It is also called the filtered white noise model. The physical mechanism of the K-T model are shown in Fig. 1b.

      ¨u+2βgωg˙u+ω2gu=¨U(t) (2)
      ¨ug(t)=¨u(t)+¨U(t)=2βgωg˙uω2gu (3)

      The model has clear physical significance. That is, it fully considers the filtering effect of site soil layer on rock motion, and the spectral characteristics are more in line with the actual site. Therefore, the K-T model has become one of the most widely used stochastic stationary models of strong ground motion. However, the model also has some obvious defects. Specifically:

      (1) The K-T model overestimates the low-frequency components of ground motion, which may give unreasonable results when used in the random seismic response analysis of low-frequency structures.

      (2) The K-T model has singular points at zero frequency and does not satisfy the continuous quadratic integrability condition. The variance of ground velocity and ground displacement derived from it is infinite.

      (3) The K-T model assumes that the ground acceleration of rock is the Gaussian white noise. It can't adequately reflect the spectral characteristics of rock motion.

    • Clough & Penzien[18] proposed a method to modify the low-frequency energy of the K-T spectral model, hereinafter collectively referred to as the C-P model. The model is as follows:

      SCP(ω)=SKT(ω)×ω4(ω2fω2)2+(2×βf×ωf×ω)2 (4)

      Where, ɷf is the frequency of the second filter layer, which should be smaller than ɷg, and the recommended value is ɷf = 0.1 − 0.2 ɷg; βf is the damping ratio of the second filter layer, which can be the same as βg.

      The model has a strong inhibitory effect on low frequency and can be used to simulate the bimodal frequency of ground motion. Figure 2a shows that the value of βf affects the appearance of two peaks in the C-P model. When βf is greater than 0.6, there is only a single peak in the power spectrum. Figure 2b explains the physical mechanism of the C-P model, which is considered to be the result of re-filtering the K-T model with a second-order high-pass filter.

      Figure 2. 

      Introduction of the C-P model. (a) The shape of PSD under different model parameters. (b) Interpretation of the physical mechanism.

      The C-P spectral model also has some flaws. It has many parameters, and there are four poles in the integration of autocorrelation function. It is complex to solve this process by using the residue theorem[54]. Although in theory, the residue can always be obtained and then the autocorrelation function can be obtained, the result is very complex. In fact, for engineering applications, as long as a mathematical function can reasonably describe the frequency domain energy distribution of strong earthquake ground motion, and the structural dynamic response under the action of the corresponding stochastic process conforms to the general law, this function can be used as the power spectrum model of strong earthquake ground motion. Obviously, with the same fitting ability, such a function should be as simple as possible. Otherwise, these constants not only make the analysis of a complex structure complicated but are also difficult to determine from the statistics of past earthquake records[55].

    • Hu & Zhou[17] proposed a method to modify the low-frequency energy of the K-T spectral model, hereinafter collectively referred to as the Hu model. The model is as follows:

      S(ω)=SKT(ω)×ω6ω6+ω6c (5)

      where, S0, ɷg and βg have the same meaning as in the K-T model. ɷc is the factor of low frequency control, which is used to eliminate the unreasonable phenomenon that the K-T spectrum contains zero frequency component. Hu suggested that the value of ɷc is 2.0 rad/s.

      Compared with the K-T model, the Hu model is considered to use the third-order high-pass filter to further filter the K-T model. The specific physical significance is that the first filtering of the model weakens the high-frequency content of white noise, enlarges the frequency content near ɷg, and the second filtering weakens the low-frequency content of white noise. The Hu model modifies only over the low frequency range of the K-T model and is in good accordance over the range of high frequency. Obviously, the velocity and displacement variance of the ground motion are convergent due to the Hu model. Therefore, the Hu model can not only retain the advantages of the K-T model but also eliminate the drawbacks of the K-T model.

      The rock motion is assumed as the white noise process due to the K-T model, obviously this is not in accordance with the realities in physics. In fact, the acceleration of the rock motion induced by an earthquake must be the color noise process with certain characteristics. Assuming it can be expressed by the following equation:

      S¨U(t)(ω)=ω6ω6+ω6c×S0 (6)

      It can be proved that the PSD of the ground acceleration üg(t) obtained from the filtered rock motion Ü(t) by Eqs (2) and (3) has the same form with the expression of the Hu model. The Hu model may be considered an improvement of the K-T spectrum, and can be interpreted physically that the rock acceleration process with the PSD function defined by Eq. (6) is filtered by a linear single-degree-of-freedom system with natural frequency ɷg and damping ratio βg, as a result, it will be lead to a stochastic process with the Hu spectrum.

      Figure 3a shows that the factor of low frequency control only weakens the spectral amplitude in the low-frequency range and does not inhibit the medium and high frequencies. The larger the factor of low frequency control, the more obvious the weakening of spectral amplitude. In order to prevent the low-frequency content amplitude from being underestimated, which will affect the seismic response analysis results of long-period structures, the factor of low frequency control ɷc is taken as 1.0 rad/s in this paper. Figure 3b shows the physical process of the Hu model. The PSD of rock acceleration process Ü(t) is described by Eq. (6), and the spectral density of ground acceleration process üg(t) is the form of the Hu model represented in Eq. (5).

      Figure 3. 

      Introduction of the Hu model. (a) The shape of PSD under different factors of low frequency control. (b) Physical mechanism interpretation.

    • Figure 4 shows the comparison of the K-T model, the C-P model and the Hu model. The spectral curve of the K-T model is obviously singular in the point of zero frequency, which does not meet the continuous twice integrable condition. In terms of low-frequency suppression, the C-P model and the Hu model handle this well. The spectral curves of the three models almost coincide in the medium and high frequency region. It is proved again that the Hu model only changes the energy distribution of white spectrum in the low frequency range, and the energy distribution of medium and high frequency is completely consistent with the K-T model. Further careful observation of the low-frequency region shows that both the C-P model and the Hu model improve the defect problem of the K-T model at zero frequency, but the C-P model has an excessively strong inhibitory effect at low frequencies. The Hu model protects the frequency range of most engineering structures, prevents underestimation of the power spectrum amplitude in the low frequency range, and only suppresses the spectral amplitude in the extremely high frequency range, which is more reasonable in physical mechanism.

      Figure 4. 

      Comparison of the K-T model, the C-P model and the Hu model.

    • Since the Hu spectrum is the result of filtered color noise process, what are the properties of rock acceleration Ü(t)? There are two spectral parameters, ɷc and S0 in Eq. (6). Figure 5 shows the relationship between the two spectral parameters and the amplitude of the power spectrum.

      Figure 5. 

      The filtered white noise process on rock.

      Observing Fig. 5, the PSD of the rock acceleration only has differences with white noise in the lower range of frequencies, and they are compatible with each other over the medium and high frequency range. So the model of the stochastic rock motion with the spectrum given by Eq. (6) is the modification to white noise model by reducing only the lower frequency contents of the motion. The modified limits are controlled by the factor ɷc, and the frequency content of the white noise are modified during the approximate range from zero to 2ɷc.

      Considering the following filter equations[56]:

      ¨U(t)=y(t) (7)
      y(t)+ω3cy=p(t) (8)

      In which P(t) is the white noise process with spectral intensity S0.

      Let P(t) = eiωt and y = Hyp(iω)eiωt. Substituting P(t) and y(t) into Eq. (8) and considering the condition eiωt ≠ 0 gives the transfer function:

      Hyp(iω)=1iω3+ω3c (9)

      Then the spectral density function of y(t) is given by:

      Sy(ω)=|Hyp(iω)|2Sp(ω)=1ω6+ω6cS0 (10)

      Considering the relationship:

      Sy(ω)=ω6Sy(ω)=ω6ω6+ω6cS0 (11)

      The PSD function of Ü(t) is deduced as:

      S¨U(ω)=Sy(ω)=ω6ω6+ω6cS0 (12)

      It is clear that Eq. (12) is the same as Eq. (6). Therefore the rock motion process is a filtered white noise process and the stochastic process with the Hu spectrum is the result that the filtered white noise process is filtered again, and it is the twice filtered white noise process.

      In Eq. (6), S0 represents the intensity of the rock acceleration, which depends on the energy released during the earthquake and can be determined by the mean value of the peak ground accelerations. The parameter ɷc limits the range of low frequency reduction, and the more this frequency parameter, the less the low frequency content of earthquake ground motion, so it may be related to the fault mechanisms. In generally, the high frequency contents of the rock motion are abundant when the earthquake occurs, and they are usually reduced by soil filters and some contents with long periods will be amplified in the process of propagation. With this in mind, it is not only concise in the mathematic expression that the rock acceleration model given by Eq. (6) only modifies the frequency contents during the lower range and holds basically the frequency characteristics of the white noise spectrum during the medium and high frequency range, but also physically reasonable, because the influences of the high frequency contents of the rock motion have not been very strong when the motions are propagated at the site.

    • The statistic characteristics of the random ground motion process with the Hu spectrum are described by the PSD function given in Eq. (5) in the frequency domain, and the statistic characteristics in time domain can be described by the correlation function[56]. Because the Hu model is a twice filtered Gaussian white noise process, the time properties of the rock acceleration can be obtained by using the filter Eq. (7) and (8). Introducing the state space vectors, Eq. (8) is rewritten as:

      [A]{˙z}+[B]{z}={Fr}p(t) (13)

      In which:

      {z}={z1z2z3}={y˙y¨y},[A]=[001010100],[B]=[ω3c00001010],{Fr}={100} (14)

      Because [A] and [B] are symmetric, the characteristic equation of the Eq. (13) is:

      ([A]λj+[B]){φj}={0}(j=1,2,3) (15)

      The complex eigenvalues may be solved from Eq. (15) as:

      λ1=ωc,λ2=1+3i2ωc,λ3=13i2ωc (16)

      Substituting Eq. (16) into Eq. (15) leads to the complex modes of the system:

      {φ1}={1λ1λ21},{φ2}={1λ2λ22},{φ3}={1λ3λ23} (17)

      It can be proved that the complex modes are weighted orthogonal with respect to the matrix [A] and [B]. The orthogonality may be expressed as:

      {φj}T[A]{φk}={φj}T[B]{φk}=0(jk){φj}T[B]{φj}=λj{φj}T[A]{φj} (18)

      The response {z} of the system can be expressed as the superposition of the modal contributions:

      {z}=3j=1{φj}hj (19)

      Substituting Eq. (19) in Eq. (13) and pre multiplying each term in this equation by {φj}T. Because of the orthogonality conditions of the complex modes, the uncoupled equation for each mode can be obtained:

      ˙hjλjhj=ηjpj=1,2,3 (20)

      Where:

      ηj={φj}T{Fr}{φj}T[A]{φj}=13λ2j (21)

      The solution to the Eq. (20) may be solved as:

      hj(t)=0ηjeλjτp(tτ)dτ (22)

      The correlation function of the complex modal contributions hj and hk (j,k = 1,2,3) is defined as:

      Rhjhk(τ)=E[hj(t)hk(t+τ)] (23)

      where the asterisk * denotes the complex conjugate of vector. Substituting Eq. (22) in Eq. (23) and changing the orders of expected value and integral calculations gives:

      Rhjhk(τ)=ηjηk00eλju+λkvRp(τ+uv)dvdu(j,k=1,2,3) (24)

      Where RP(τ) = 2πS0δ(τ), which is the correlation function of the white noise process.

      According to the Eq. (19) and Eq. (20), the responses of the system are given by:

      ¨y(t)=z3(t)=3j=1λ2jhj(t) (25)

      The correlation function of the response is:

      R¨y(τ)=3j=13k=1λ2j(λk)2Rhjhk(τ) (26)

      Substituting Eq. (24) in Eq. (26) gives:

      R¨y(τ)=193j=13k=100eλju+λkvRp(τ+uv)dvdu (27)

      Considering the following relationship:

      Ry(τ)=d2dτ2R¨y(τ) (28)

      The correlation function of the filtered white noise process in rock is solved as:

      R¨U=Ry=πωcS03[eωc|τ|+eωc2|τ|(cos32ωcτ3sin32ωc|τ|)] (29)
    • The correlation function of the Hu spectrum can be obtained by random vibration analysis to the single-degree-of-freedom system subject to the seismic excitation with the PSD function of Eq. (6) or correlation function of Eq. (29).

      The filter equations can be rewritten as:

      [M]{˙u}+[K]{u}={Fg}¨U(t) (30)

      where Ü(t) is the filtered white noise process with the correlation function given by Eq. (29); {u} = {x, }T a state vector; [M], [K] and {Fg} the mass matrix, stiffness matrix and direction vector, respectively.

      [M]=[2βgωg110],[K]=[ω2g001],{Fg}={10} (31)

      The modal expansion of displacement vector {u} can be expressed as:

      {u}=2j=1{γj}qj (32)

      Where {γj} and qj(t) are the jth complex mode and modal coordinate, respectively.

      The correlation function of ground acceleration is expressed as:

      Ra(τ)=ω4gRx(τ)+4β2gω2gR˙x(τ)b±b24ac2a (33)

      In which:

      Rx(τ)=2j=12k=1Rqjqk(τ) (34)
      R˙x(τ)=2j=12k=1rjrkRqjqk(τ) (35)

      Where γj is the jth complex frequency, r1,2=βgωg±iωD,ωD=ωg1β2g.

      The correlation function of complex modal response is:

      Rqjqk(τ)=(1)j+k4ω2D00erju+rkvR¨U(τ+uv)dvdu(j,k=1,2τ0) (36)

      Substituting Eq. (29) in Eq. (36), and carrying out the integral calculation leads to:

      Rqjqk(τ)=πS0ωc(1)j+k24ω2D1rj+rk(αjkeωcτ+βjkeμτ+κjkeμτ+ρjkepkτ)(j,k=1,2τ0) (37)

      The coefficients in Eq. (37) are:

      αjk=2(rk+rj)(rk+ωc)(rjωc),βjk=s(1rkμ+1rj+μ),κjk=s(1rkμ+1rj+μ),ρjk=s(1rk+μ+1rkμ)+s(1rk+μ+1rkμ)+4ωc(rk)2+ω2c,s=1+3i,μ=(12+32i)ωc (38)

      Substituting Eq. (37) in Eq. (34) and Eq. (35), using Euler transform gives:

      Rx(τ)=πωcS012ω2D[b1eωc|τ|+eωc2|τ|(b2cos32ωcτb3sin32ωc|τ|)+eβgωg|τ|(b4cosωDgτb5sinωDg|τ|)] (39)
      R˙x(τ)=πωcS012ω2D[c1eωc|τ|+eωc2|τ|(c2cos32ωcτc3sin32ωc|τ|)+eβgωg|τ|(c4cosωDgτc5sinωDg|τ|)] (40)

      In which the coefficients are respectively given as:

      b1=4(1β2g)ω2g(ω2g+ω2c)24β2gω2gω2c (41)
      b2=4ω2g(1β2g)[ω4g2ω2gω2c(2β2g1)2ω4c](ω8gω4gω4c+ω8c)+2ω2gω2c(2β2g1)[ω4g+2ω2gω2c(2β2g1)+ω4c] (42)
      b3=43ω4g(1β2g)[ω2g+2ω2c(2β2g1)](ω8gω4gω4c+ω8c)+2ω2gω2c(2β2g1)[ω4g+2ω2gω2c(2β2g1)+ω4c] (43)
      b4=2ωc(1β2g)βgωg{ω6g(4β2g1)+ω4gω2c(32β4g24β2g+3)+ω2gω4c(8β2g3)+2ω6c(ω8gω4gω4c+ω8c)+2ω2gω2c(2β2g1)[ω4g+2ω2gω2c(2β2g1)+ω4c]ω2g(4β2g1)+ω2cω4g+2ω2gω2c(2β2g1)+ω4c} (44)
      b5=2ωc(1β2g)ωg{ω6g(4β2g3)+ω4gω2c(32β4g40β2g+11)+ω2gω4c(8β2g5)+2ω6c(ω8gω4gω4c+ω8c)+2ω2gω2c(2β2g1)[ω4g+2ω2gω2c(2β2g1)+ω4c]ω2g(4β2g3)+ω2cω4g+2ω2gω2c(2β2g1)+ω4c} (45)
      c1=b1ω2c,c2=b2+3b32ω2c,c3=b33b22ω2cc4=(12β2g)ω2gb42βgω2g1β2gb5,c5=(12β2g)ω2gb5+2βgω2g1β2gb4 (46)

      Substituting Eq. (39) and Eq. (40) in Eq. (33) and simplifying the expressions as:

      Ra(τ)=πωcS012(1β2g)[A1eωc|τ|+eωc2|τ|(A2cos32ωcτ+A3sin32ωc|τ|)+eβgωg|τ|(A4cosωDτ+A5sinωD|τ|)] (47)

      In which:

      A1=(ω2g+4β2gω2c)b1,A2=(ω2g+2β2gω2c)b2+23β2gω2cb3,A3=23β2gω2cb2(ω2g+2β2gω2c)b3A4=ω2g(1+4β2g8β4g)8β3gω2g1β2gb5,A5=8β3gω2g1β2gb4ω2g(1+4β2g8β4g) (48)

      Equation (47) is the expression of the correlation function of the Hu model, which is the inverse Fourier’s transform of Eq. (5).

    • Taking the Hu model as an example to introduce the determination method of power spectral model parameters. Figure 6 shows the determination process of solving the parameters of the power spectrum model by the least square method. It can be seen from Eq. (5) that the Hu model is a nonlinear function of its parameters ωg, βg, ωc and S0. Therefore, it is necessary to linearize the nonlinear function first, and then determine its parameters by the least square method.

      Figure 6. 

      The determination process of the power spectrum model parameters by the least square method.

      The square sum of the difference between the Hu power spectrum and the power spectral recorded by seismic acceleration at each frequency point can be expressed as:

      E=nk=1[Skf(ωk,B)]2 (49)

      Where, n is the number of discrete points of the power spectrum; Sk and f(ωk, B) are the spectral amplitudes at ωk of the power spectral recorded by seismic acceleration and the Hu power spectrum, respectively; B is the four parameters of the Hu power spectrum (one spectral intensity factor and three spectral parameters), which can be expressed as:

      B=[b1,b2,b3,b4]=[ωg,βg,ωc,S0] (50)

      The parameters of the Hu power spectrum can be expressed as:

      bi=b(0)i+δi(i=1,2,3,4) (51)

      Where, bi(0) is the initial approximate value of spectral parameters; δi is the correction of spectral parameters. In this way, the problem of determining the spectral parameters is transformed into the problem of determining its correction. By expanding the Hu power spectrum into the Taylor series at the initial approximation of its parameters and omitting the second-order and higher-order terms, we can get:

      f(ωk,B)=fk0+4i=1fk0biδi (52)
      fk0=f(ωk,b01,b02,b03,b04) (53)
      fk0bi=bif(ω,b1,b2,b3,b4)|ω=ωkbi=b(0)i (54)

      Substituting Eq. (52) into Eq. (49) can obtain:

      E=nk=1[Sk(fk0+fk0b1δ1+fk0b2δ2+fk0b3δ3+fk0b4δ4)]2 (55)

      Calculate the partial derivative of the correction δi in Eq. (55), and you can get:

      Eδi=2[δ1nk=1fk0b1fk0bi+δ2nk=1fk0b2fk0bi+δ3nk=1fk0b3fk0bi]+2[δ4nk=1fk0b4fk0bink=1(Skfk0)fk0bi](i=1,2,3,4) (56)

      If Eq. (55) is equal to zero, the simultaneous equations of four corrections (δi, i = 1,2,3,4) can be obtained. This system of equations can be expressed as:

      [A]{Δ}={C} (57)

      In Eq. (57), the elements in matrix [A] and vector {C} can be expressed as:

      aij=nk=1fk0bifk0bj(i,j=1,2,3,4) (58)
      ci=nk=1(Skfk0)fk0bi(i,j=1,2,3,4) (59)

      When the data point (ωk, Sk) of the power spectral recorded by seismic acceleration and the initial approximate value bi(0) of the parameters of the Hu power spectrum are given, the left end coefficient ɑij and the right end coefficient ci of the Eq. (57) can be calculated according to Eq. (58) and (59). Then, the correction amount δi of the spectral parameter can be calculated from Eq. (57), and then the spectral parameter value bi can be calculated. If the absolute value of the correction amount δi is large, the spectral parameter value bi just calculated is used to replace the initial approximate value bi(0). Then, calculate the left end coefficient ɑij and the right end coefficient ci again, and calculate the equations to obtain a new correction δi, and then obtain a new spectral parameter value bi. Repeat the above process until the absolute value of the correction amount δi is too small to be counted.

    • The most direct method to study the characteristics of ground motion is to make statistical analysis of ground motion parameters, and the premise of statistical analysis of ground motion parameters is to establish a reasonable seismic record database. At present, the seismic design codes of different countries in the world consider the influence of different site types on the design ground motion. Based on the site classification methods in NEHRP[57] and Eurocode8[58], this paper divides the site into four categories: rock, dense soil, hard soil and soft soil, as shown in Table 1.

      Table 1.  The site classification methods used in this paper.

      Site categoryDescriptionVS30 (m/s)
      IRockVS30 > 800
      IIDense sand, gravel and very
      dense soil
      800 ≥ VS30 > 360
      IIIMedium dense sand, gravel
      and dense soil
      360 ≥ VS30 > 180
      IVSoft soilVS30 ≤ 180
      VS30 represents the equivalent shear wave velocity within 30 m underground.

      Historical earthquake damage shows that structures are vulnerable to severe damage under near-fault strong earthquakes[59]. Therefore, in addition to the site category, there are also great differences between near-field and far-field ground motions. In this paper, according to the size of fault distance, the ground motion is divided into near-field motion (NF, fault distance is 0−20 km), mid far-field motion (MFF, fault distance is 20−100 km) and far-field motion (FF, fault distance is more than 100 km).

      The ground motion records are selected from the NGA-West2 database[60] released by the Pacific Earthquake Engineering Research Center (PEER) (https://ngawest2.berkeley.edu/site), all seismic records have a magnitude greater than 4.5. According to the site classification method in Table 1, 1946 seismic records were selected with different fault distances. The number of seismic records of various sites are shown in Table 2. Fourier spectral analysis is performed on these acceleration recordings to obtain PSD curves. We then calculated the average value of the PSD curves of different sites. The average curve is smoothed using the moving average algorithm. The final results of various sites are shown in Figs 710 and Table 3.

      Table 2.  Number of seismic records at different sites and fault distances.

      Seismic recordsIIIIIIIV
      NF4019620022
      MFF188172200142
      FF186200200200

      Figure 7. 

      PSD curves and average value of seismic records of I site, (a) NF; (b) MFF; (c) FF.

      Figure 8. 

      PSD curves and average value of seismic records of II site, (a) NF; (b) MFF; (c) FF.

      Figure 9. 

      PSD curves and average value of seismic records of III site, (a) NF; (b) MFF; (c) FF.

      Figure 10. 

      PSD curves and average value of seismic records of IV site, (a) NF; (b) MFF; (c) FF.

      Table 3.  Peak statistics of smoothed PSD curve at different sites and fault distances.

      Seismic
      records
      IIIIIIIV
      NF(14.44, 161.53)(10.36, 211.47)(9.42, 256.91)(6.28, 457.13)
      MFF(10.05, 18.06)(7.85, 31.34)(6.28, 59.40)(5.65, 117.00)
      FF(5.34, 4.49)(4.71 ,28.82)(3.77, 45.74)(3.77, 74.62)
      The unit of ɷ is rad/s; and the unit of PSD is cm2/s3.

      According to Figs 710 and Table 3, two conclusions can be seen intuitively: (1) Under the same site type, the frequency and amplitude of PSD decrease gradually with the fault distances from near to far, and the attenuation rate of amplitude is faster than that of the frequency. (2) At the same fault distance, with the site type from hard soil to soft soil, the frequency of the power spectrum gradually decreases, but the amplitude gradually increases, which is the result of the amplification effect of the soft soil site.

    • According to the power spectrum of actual seismic acceleration records, K-T spectrum, C-P spectrum and Hu spectrum of different site categories and different fault distances are determined by using the above nonlinear least square method (Fig. 6), results as shown in Tables 47 and Figs 1114.

      Table 4.  Parameter values of three power spectrum models for I site.

      IK-T modelC-P modelHu model
      ɷgβgS0ɷgβgS0ɷfβfɷgβgS0ɷc
      NF20.650.9484.4916.871.11101.300.186.7218.921.0291.842.14
      MFF13.440.588.7611.520.7211.900.226.3412.640.6710.092.43
      FF7.720.622.517.060.702.910.0311.527.320.682.760.88

      Table 5.  Parameter values of three power spectrum models for II site.

      IIK-T modelC-P modelHu model
      ɷgβgS0ɷgβgS0ɷfβfɷgβgS0ɷc
      NF15.731.04133.3011.301.36162.700.0322.1413.421.19148.31.56
      MFF9.880.8718.948.850.9621.230.0227.169.190.9420.440.99
      FF10.170.7216.948.430.8621.200.0610.979.220.8119.151.41

      Table 6.  Parameter values of three power spectrum models for III site.

      IIIK-T modelC-P modelHu model
      ɷgβgS0ɷgβgS0ɷfβfɷgβgS0ɷc
      NF14.660.81165.7013.100.91185.900.401.4113.780.87177.101.02
      MFF10.900.7330.644.871.2569.502.480.958.440.9340.462.54
      FF7.610.7128.016.500.8433.850.066.306.910.8031.590.97

      Table 7.  Parameter values of three power spectrum models for IV site.

      IVK-T modelC-P modelHu model
      ɷgβgS0ɷgβgS0ɷfβfɷgβgS0ɷc
      NF8.300.59225.66.840.73321.200.671.567.600.68268.301.71
      MFF7.550.5862.176.940.6873.740.501.107.220.6568.790.90
      FF5.800.4626.993.900.561020.0735.694.700.6744.262.34

      Figure 11. 

      Fitting results of three PSD models for I sites, (a) NF; (b) MFF; (c) FF.

      Figure 12. 

      Fitting results of three PSD models for II sites, (a) NF; (b) MFF; (c) FF.

      Figure 13. 

      Fitting results of three PSD models for III sites, (a) NF; (b) MFF; (c) FF.

      Figure 14. 

      Fitting results of three PSD models for IV sites, (a) NF; (b) MFF; (c) FF.

      Figures 1114 shows the comparison of fitting results of the K-T model, the C-P model and the Hu model under different sites and fault distances. The following conclusions can be drawn:

      (1) In the middle and high frequency range, the K-T model has a high degree of fit with the PSD of actual seismic records, but the fitting effect is poor in the low frequency range. Defects at zero frequency are always present.

      (2) The Hu model is in good agreement with the PSD of the seismic records in the whole frequency range, which is consistent with the actual physical model of ground motion.

      (3) As the soil layer of the site becomes softer, the predominant frequency of the power spectrum of the actual seismic records gradually decreases. Under the same soil layer, with the increase of fault distance, the predominant frequency and amplitude of the power spectrum gradually decreases, and the amplitude decays faster than the frequency.

      (4) The C-P model has many parameters, and the parameter fitting results of the second filter layer are quite different, and the regularity is not obvious enough.

      The above results show that the Hu power spectrum model can analyze the random seismic response of structures in different frequency ranges under different site conditions. It is an ideal model to describe the random characteristics of earthquake ground motion process.

    • In this study, we discuss and analyze the power spectral model of the stationary stochastic process of earthquake ground motion. The main conclusions are as follows:

      (1) The Hu model is an improved scheme to the K-T model, and essentially the filtered color noise process, thus it is definite in physical conception. The singular point in zero frequency is eliminated due to the Hu model so that the variances of the ground velocity and displacement are finite. The low frequency contents of the earthquake ground motion are modified by the low frequency control factor ɷc in the Hu model. The low frequency contents decrease with the increase of ɷc, and the Hu model can be used for the stochastic seismic response analysis of the structures with low frequency as well as medium and high frequency.

      (2) The correlation function is the important characteristic of the stationary stochastic process in time domain, by which other statistical properties can be obtained conveniently. The Hu model is a twice filtered white noise process, so the correlation function can be deduced through the filter equations in time domain. These results provide a basis for random response analysis of the seismic structures in time domain.

      (3) 1946 actual seismic records of different sites and fault distances were selected, the power spectrums and average values were calculated. The power spectrum parameters of the K-T model, the C-P model and the Hu model are fitted by the least square method, which make up for the rough division of site categories and fault distances by the existing power spectrum models.

      (4) The Hu model is in good agreement with the power spectrums of the actual seismic records in the whole frequency range, which is consistent with the actual physical model of ground motion. Compared with the C-P model, the Hu model has fewer parameters, the model parameters under different sites are more accurate, and it is suitable to describe the statistical characteristics of earthquake induced ground motion. The obtained power spectrum parameters can adapt to the seismic design codes worldwide, and are of great significance in improving the seismic performance and toughness of urban and rural building structures.

      • The authors acknowledge gratefully the partial support of the Joint Research Fund for Earthquake Science launched by the National Natural Science Foundation of China and China Earthquake Administration (Grant No. U2039208); Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX22_1322).

      • The authors declare that they have no conflict of interest.

      • Copyright: © 2022 by the author(s). Published by Maximum Academic Press on behalf of Nanjing Tech University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (14)  Table (7) References (60)
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    Chen B, Sun G, Li H. 2022. Power spectral models of stationary earthquake-induced ground motion process considering site characteristics. Emergency Management Science and Technology 2:11 doi: 10.48130/EMST-2022-0011
    Chen B, Sun G, Li H. 2022. Power spectral models of stationary earthquake-induced ground motion process considering site characteristics. Emergency Management Science and Technology 2:11 doi: 10.48130/EMST-2022-0011

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