Figures (3)  Tables (4)

    • Figure 1. 

      Methodology framework.

    • Figure 2. 

      Number of papers investigated vs year of publication.

    • Figure 3. 

      Classification of reviewed prediction models. ANN: Artificial Neural Network, BPNN: Back Propagation Neural Network, DNN: Deep Neural Network, GBDT: Gradient Boostine Decision Tree, GONN: Genetically Optimized Neural Network, GP: Genetic Programming, LSSVR: Least Squares Support Vector Regression, ML-Lasso: Machine Leaming regularzed regression with Lasso, ML-LR: Machine Leaming Liear Regression, MLPNN: Machine Leaming Propagation Neural Network, RNN: Recurrent Neural Network.

    • ReferencePavement typesFindingsApproach
      Ramadan & Beckedahl, 2017[35]AsphaltHighlights discrepancies between lab and real-world asphalt pavement performance. Evolving properties due to traffic and environment pose challenges. Current methods fall short. A new incremental approach is proposed, addressing material variations over time.Mechanistic
      Norouzi et al, 2017[36]AsphaltDevelopment of a Layered Viscoelastic Critical Distresses (LVECD) model to predict fatigue performance. This study applied the LVECD program to 18 pavements in the US and Canada. Comparing simulations with field observations, the study found strong agreement in fatigue damage trends, validating LVECD's accuracy in predicting crack initiation and propagation.
      Shah et al., 2013[37]Flexible pavementsOverall Pavement Condition Index (OPCI) for a selected network of urban roads in Noida city (India). Four key performance indices were calculated, including distress, roughness, structural capacity, and skid resistance. By combining these indices, the OPCI was created, offering a reliable indicator of pavement condition.
      Albuquerque et al., 2022[38]Flexible pavementsUrban Pavement Condition Index (UPCI) was developed using multiple regression with pavement defects and Current Serviceability Value (CSV). The UPCI was compared with other PCIs, showing variability and analyzed through statistical tests.Empirical
      Al-Suleiman et al., 2020[39]Flexible pavementsRelationship between maintenance costs and pavement deterioration rate studying 1.5 years' data to understand the link between maintenance costs and pavement deterioration
      Joni et al., 2020[40]Flexible pavementsStrong model for predicting pavement roughness (IRI) based on visible pavement distress data from 83 flexible pavement sections. Factors such as polished aggregate, potholes, alligator cracking, patching, raveling, and corrugation were considered. The model, created through stepwise multiple linear regression in SPSS.
      Harikeerthan et al., 2020[41]Flexible pavementsDeveloped Pavement Deterioration Models and a Relative Deterioration Index using data from Automated and Manual Field Evaluation methods on Bangalore city roads (India). Findings highlighted the dominant impact of roughness on road deterioration in selected categories.
      Alaswadko et al., 2019[42]Sealed granular pavementsDeveloped robust multilevel roughness models for sealed granular pavements using data from 40 highways (2300 km). Key predictors included traffic loading, subgrade soil potential, climate, drainage, and initial pavement strength. Time was the most significant predictor, followed by initial pavement strength and traffic loading.
      Mamlouk et al., 2018[43]Flexible pavementsRelationship between pavement ride quality (roughness) and rut depth and accident rate on highways. Found that the accident rate remained stable until pavement roughness exceeded 210 inches/mile or rut depth reached 0.4 inches. Beyond these thresholds, the crash rate increased significantly.
      Hassan et al., 2017[44]Chip/spray seal, Geotextile seal, Stone mastic asphalt, Open graded asphalt,
      Ultra-thin asphalt      		Enhancing the identification and prioritization of resurfacing needs through comparative analysis of deterioration models for five types of bituminous surfaces. Surface condition data, transformed into Surface Inspection Rating (SIR), was analyzed using regression, logistic regression, and Markov chains. Results showed similar predictions and deterioration rates across the approaches for most surfacing types.
      Sylvestre et al., 2019[45]Flexible pavementsIncorporating frost heave in long-term roughness performance prediction models. The result presented illustrate that a significant increase in long-term IRI deterioration rate, usually caused by a more variable subgrade soil, is likely to contribute to the rehabilitation of the pavements up to four years before the end of the pavement service life.
      Wang et al., 2017[23]Jointed plain concrete pavement (JPCP)Comparative analysis of multivariate non-linear regression, artificial neural network and Markov chain models for faulting-based pavement performance prediction. MNLR needs recalibration, ANN requires more data, and MC, though promising for limited data, lacks quantitative correlation. Future research should blend model strengths for improved accuracy.
      Sultana et al., 2016[46]Flexible pavementsExamined flood-affected roads' data and proposed a model showing rapid post-flood pavement deterioration. Findings align with observations post-Hurricanes Katrina and Rita in New Orleans (US).
      Ziari et al., 2016[47]Flexible pavementsComparative analysis of group method of data handling and ANN in terms of their capabilities. Nine input variables were studied, focusing on traffic, environmental changes, and pavement structures, with IRI. Results showed that ANNs accurately predicted pavement condition in short and long terms, while Group Method of Data Handling (GMDH) models did not achieve acceptable accuracy.
      Luo, 2014[48]Flexible pavementsPredictive models and a composite distress index for pavement management and preservation projects. Using MLR and ANN models, the research recommends MLR models due to their simplicity and robust performance. An Analytic Hierarchy Process (AHP) was utilized to create a composite distress index, helping Kentucky Transportation Cabinet (KYTC) prioritize projects based on 11 distress indices.
      Shahini et al., 2014[49]Flexible pavementsIntegrating the impact of severe events like snow storms and floods on road infrastructures achieving over 90% accuracy using LTPP and National Oceanic and Atmospheric Administration (NOAA) data.
      Sreedevi et al., 2014[4]Flexible pavementsMaintenance Priority Index (MPI) for six sections of State Highway SH-1, considering factors like pavement condition, riding quality, traffic, and land use. Significant relationships between pavement distress and roughness were established using MLR.
      Anyala et al., 2014[24]AsphaltPredictive model considering climate, traffic, materials, and pavement design factors to assess the impact of climate change on road pavement rutting. Developed using Bayesian regression and Monte Carlo simulations, the model provides probabilistic estimates for rut depth progression and maintenance costs.
      Prasad et al., 2013[50]Rural Roads
      (Flexible Pavement)      		Relationship between pavement roughness and surface distresses focusing on roughness and its relation to safety and driving ease using IRI to measure roughness. Bump Integrator, calibrated with MERLIN, collected roughness data.
      Owolabi et al., 2012[51]Flexible pavementsPavement performance models for Nigeria (Africa) and similar developing countries to predict deterioration rates. Key parameters affecting Pavement Condition Score (PCS) and IRI were identified using Stepwise Regression. Depth of ruts and area of potholes impacted PCS, while number of patches, length of cracks, and depth of ruts affected IRI.
      Chen & Zhang, 2011[52]AsphaltExploring the suitability of four IRI-based deterministic deterioration prediction models including the NCHRP and Dubai models, using NMDOT PMS and LTPP data in New Mexico (US). NCHRP and Dubai models prove effective, while Al-Omari—Darter and NMDOT models lack statistical reliability. Additionally, a survival curve probabilistic model for pavement service life prediction is introduced, with traffic loading approach yielding the most accurate results.
      Sidess et al., 2022[53]Flexible pavementsPredictive model for IRI deterioration, calibrated using pavement structural factors like structural number, asphalt thickness, subgrade strength, and environmental conditions. Results, compared with road measurements in diverse climate zones managed by Netivei Israel (NETI), exhibit strong correlation.Mechanistic-Empirical
      Gupta, 2019[54]Rural Hilly RoadsDevelopment of Rural Road Maintenance Priority Index (RRMPI) for rural road networks in hilly terrain areas of India on a scale of 0-100, which efficiently assesses pavement conditions. RRMPI was used to select maintenance strategies for 12 rural road stretches in Himachal Pradesh, ensuring cost-effective and targeted maintenance efforts.
      Katicha et al., 2016[55]Flexible pavementsConsidering pavement age and Modified Structural Index (MSI) and accurately predicts average critical condition index of pavement sections. The model with MSI was 50,000 times more accurate. Lowering MSI from 1 to 0.6 for a 7-year-old pavement reduced the Critical Condition Index (CCI) from 79 to 70.
      Hasan et al., 2016[56]Flexible pavementsCompared empirical and mechanistic-empirical approaches in flexible pavement design across 76 locations in 13 US states. Findings revealed significant impacts of mean annual temperature and precipitation on various pavement distresses, except for the IRI, which remained unaffected.
      Jung & Zollinger, 2011[57]Jointed plain concrete pavementFaulting model calibrated using a erosion test involving the Hamburg wheel-tracking device and Long-Term Pavement Performance (LTPP) data.

      Table 1. 

      Summary of deterministic prediction models in the reviewed papers.

    • ReferencePavement typesFindingsApproach
      Pantuso et al., 2021[69]Flexible pavementsImprove prediction accuracy by combining the model's degradation estimate with real-world observations using negative binomial regression based on pavement age. Various road type models were compared with traditional methods, and the linear empirical. Results showed substantial improvements, reducing mean square error by 33% (interstate), 36% (primary), and 41% (secondary roads) compared to measured conditions without additional modeling.Bayesian
      Issa& Abu Eisheh, 2019[70]Flexible pavementsCreate a predictive model for pavement condition to optimize road maintenance and rehabilitation plans. By assessing pavement sections visually and using the Pavement Condition Index (PCI), the study suggests early prediction (within 5-10 years) enables cost-effective preventive maintenance actions, like crack sealing and overlay, optimizing limited budgets for road maintenance.Markov chain
      Gursoy, 2019[71]GeneralDevelop network-level pavement deterioration prediction models in the absence of key input variables like traffic loading, freeze-thaw cycles, snow plowing, construction quality, pavement thickness, and age.Markov chain
      Rose et al., 2018[72]Low volume roadsInclude uncertainties in low-volume road pavement behavior that deterministic models overlook. Focused on distresses like raveling, potholes, and edge failures. Rare load-associated distresses indicated drainage and construction quality issues as main deterioration causes. The study analyzed distress progression with age and established probabilities for distress occurrences.Gamma, exponential & inverse-Gaussian
      Soncim et al., 2018[73]AsphaltSuggest a method to predict IRI when historical pavement condition data is lacking. Transition probability matrices, incorporating factors like traffic density and climate, were established. The models, based on the International Roughness Index, highlighted variations in pavement behavior tied to factors such as traffic and climate, as per expert experiences.Markov chain
      Saha et al., 2017[74]GeneralEnhance Colorado DOT's deterioration prediction methods by factoring in degradation process uncertainties alongside deterministic techniques. Longitudinal, fatigue, and rut indices deteriorated slowly, while transverse and ride indices showed faster deterioration. The models achieved high accuracy (R2 > 0.84).Markov chain
      Abaza, 2017[75]Rehabilitated pavementIntroduces two empirical Markovian models for predicting transition probabilities in rehabilitated pavements: one for staged-homogeneous transitions and another for non-homogeneous transitions. These models calculate deterioration probabilities based on original pavement data and adjust for increased traffic loads and decreased pavement strength.Markov chain
      Abaza, 2016[76]Flexible pavementsSimplified Markov model predicted future pavement conditions efficiently by dividing the analysis period into staged-time periods. Deterioration probabilities, influenced by increasing traffic and pavement degradation, were estimated using C constants, determined through a trial-and-error approach.Markov chain
      Moghaddass et al., 2015[77]GeneralPredicting remaining useful life in mechanical systems, vital for cost-effective maintenance which focuses on multistate degradation, common in real-world scenarios. Addressing interval-censored data at fixed inspection points, the research develops accurate methods for parameter estimation and essential reliability measures.Markov chain
      D. Chen et al., 2014[78]Flexible pavementsDeveloped a precise data cleansing method for pavement condition data and established the superiority of the sigmoidal model in predicting pavement performance. It introduced an innovative approach for constructing piecewise linear distress models. Despite a reduced dataset, the study met analytical requirements due to meticulous data merging.Ordinal logistic
      Khan et al., 2014[79]GeneralDiscusses various methods, including non-homogeneous transition probability matrices, for deriving Road Deterioration (RD) models. The study presents new RD models considering flooding effects and optimal Maintenance (M) and Rehabilitation (R) strategies.Markov chain
      Anyala et al., 2014[24]GeneralPredictive model considering climate, traffic, materials, and pavement design factors to assess the impact of climate change on road pavement rutting. Developed using Bayesian regression and Monte Carlo simulations, the model provides probabilistic estimates for rut depth progression and maintenance costs.Bayesian
      Thomas &Sobanjo, 2013[80]Flexible pavementsPresents a flexible semi-Markov model for pavement deterioration, utilizing Weibull distribution for condition state durations which accommodates non-exponential durations. Monte Carlo simulations reveal the semi-Markov model's superiority in capturing actual flexible pavement deterioration patterns compared to the Markov model in specific casesMarkov chain
      Gao et al., 2012[81]Flexible pavementsInclude a random term in the hazard function to enhance fatigue cracking estimates by accounting for unobserved variations. The model's efficiency is demonstrated using the LTPP database.Bayesian
      C. Chen & Zhang, 2011[52]GeneralExploring the suitability of four IRI-based deterministic deterioration prediction models including the NCHRP and Dubai models, using NMDOT PMS and LTPP data in New Mexico (US). NCHRP and Dubai models prove effective, while Al-Omari—Darter and NMDOT models lack statistical reliability. Additionally, a survival curve probabilistic model for pavement service life prediction is introduced, with traffic loading approach yielding the most accurate results.Kaplan-Meier
      Abaza, 2011[82]Flexible pavementsStochastic approach to estimate flexible pavement thickness, integrating traditional and stochastic factors, notably initial and terminal transition probabilities. A discrete-time Markov model used these probabilities to predict pavement distress ratings. Empirical models for low volume roads were developed, considering relevant design factors and employing indicators like the area under the performance curve and average distress rating.Markov chain
      Kobayashi et al., 2010[83]GeneralMethod for predicting road section deterioration using Markov transition probability models and hazard models. Road states are categorized into ranks, and deterioration processes are analyzed through exponential hazard models, considering non-uniform inspection intervals.Markov chain & Weibull hazard
      Abaza et al. 2009[84]GeneralPredict pavement remaining strength by adjusting initial strength using layer capacity factors. Initial strength is determined by indicators like gravel equivalent or structural number.Markov chain
      Pulugurta et al., 2009[85]GeneralMarkov prediction model using ODOT's pavement condition database in which transition matrices were modified through imputation techniques.Markov chain
      Abaza et al., 2007[86]GeneralPredicts pavement conditions using initial and transition probabilities which creates long-term restoration plans balancing performance and budget.Markov chain
      Ortiz-García et al., 2006[87]GeneralThree methods, based on historical data, regression curves, and yearly condition distributions. Despite minor deviations, the third method consistently produced distributions similar to the original data.Empirical and Markov chain
      Yang et al., 2005[88]GeneralDynamic Markov chain approach, incorporating a logistic model for explicit transition probabilities. By capturing crack state transitions and randomness, it provides a more suitable and efficient method for pavement deterioration modeling.Markov chain
      Shahin, 2005[89]GeneralPractical guidance for cost-effective pavement management, covering project and network-level strategies, cost analysis, equipment selection, and rehabilitation techniques with a focus on the PCI procedure.Markov chain
      Abaza, 2005[90]Flexible PavementOverlay design models for flexible pavements by assessing surface conditions over time. Performance curves link surface condition to service life or load applications, allowing compensation for performance loss.Markov chain
      Abaza et al., 2003[91]Flexible PavementApproach to flexible pavement design has been developed, considering anticipated performance and life-cycle cost. By optimizing the terminal serviceability index, this method ensures cost-effective designs, challenging existing AASHTO recommendations.Markov chain
      Hong et al., 2003[92]GeneralNovel probabilistic approach for predicting pavement performance, considering uncertainties in traffic, environment, material properties, and pavement geometry whish aligns well with established pavement deterioration models (OPAC and AASHTO).Markov chain
      Ferreira et al., 2002[93]GeneralCost-effective pavement management model using deterministic performance estimates for cracking, rutting, disintegration, and roughness. The approach employs a modified PSI and genetic algorithms for optimization.Genetic algorithm
      Mishalani et al., 2002[94]GeneralAlternative approach using probabilistic duration models to capture condition evolution over time. The method estimates state transition probabilities from these duration models, addressing the shortcomings of existing techniques.Markov chain
      Ferreira et al., 1999[95]GeneralDetailed comparison of numerous pavement performance models such as regression analysis, Bayesian methodology, Markov process, nonhomogeneous Markov process, and semi-Markov process.Regression, Markov, Semi-Markov, Bayesian
      Li et al., 1996[96]GeneralMethod for accurate pavement deterioration prediction, vital for repair planning using advanced techniques, avoiding subjective opinions or extensive data and calculates transition probability matrices and pavement condition probabilities for various stages.Markov chain
      Madanat et al., 1995[97]GeneralRobust econometric method based on ordered probit techniques to estimate infrastructure deterioration models and transition probabilities from condition rating data which treats facility deterioration as a latent variable, addresses the discrete nature of condition ratings, and explicitly links deterioration to relevant variables,Markov chain
      Wang et al., 1994[98]GeneralRefined Network Optimization System (NOS) by developing new matrices using current data and employing the Chapman-Kolmogorov method to establish long-term pavement behavior. The modified matrices, incorporating accessibility rules, enhanced prediction accuracy.Markov-process-based transition
      Butt et al. 1987[99]GeneralPavement Condition Index (PCI) and age-based model, dividing PCI into ten states over 6-year. Transition matrices, determined through nonlinear programming, enable accurate prediction.Markov chain
      Golabi et al. 1982[100]GeneralPMS to produce optimal maintenance policies. The model integrates management policy decisions, budgetary policies, environmental factors, and engineering decisions.Markov chain
      Karan et al. 1976[101]GeneralMethod to prioritize urban pavement improvements which outlines a management framework and introduces a serviceability performance concept validated through street testing. The study suggests criteria for necessary improvements, a performance prediction method, and a network priority programming scheme for urban pavements.Markov chain

      Table 2. 

      Summary of probabilistic prediction models in the reviewed papers.

    • ReferencePavement typesFindingsApproach
      Guo et al., 2022[105]AsphaltEnhance pavement performance estimation, offering a dependable maintenance reference.GBDT
      Alatoom& Al-Suleiman, 2022[106]AsphaltANN models outperform regression models in accurately predicting IRI.ANN
      Haddad et al., 2022[107]Flexible pavementsPredictive rutting curves to estimate road deformations based on traffic, climate, and performance factors.DNN
      Issa et al., 2022[108]GeneralA cost-effective model utilizing machine learning techniques was developed to assess Palestinian pavement conditions. Traditional visual inspections were replaced with a hybrid model, combining classical machine learning and neural networks.ANN
      Sudhan et al., 2020[109]Flexible pavementsPavement deterioration prediction models for low-volume roads in Kerala, India, using system dynamics and Powersim Studio version 10. Fourteen roads were analyzed, and the models were validated against field data. Results show that system dynamics is effective for developing accurate pavement deterioration prediction models.Powersim Studio
      Choi & Do, 2020[110]Flexible pavementsAn algorithm was developed to predict the condition of road sections for a year based on time series data. Optimized sequence lengths reduced errors by 58.3-68.2%, achieving high prediction accuracy (0.71-0.87).RNN
      Hussan et al., 2019[111]AsphaltNon-linear regression and artificial neural networks effectively modeled permanent strain, with temperature as the most significant factor. ANN outperformed regression, accurately predicting strain for SP-B graded mixtures (R2 = 0.99) and showing high overall prediction performance.ANN
      Rezaei-Tarahomi et al., 2019[112]Rigid airfield
      pavements      		Compared critical tensile stresses predicted by ANNs with 3D-FE solutions for large aircraft which demonstrated that ANNs accurately assessed top-down critical tensile stress sensitivity, suggesting their potential for airport pavement failure analysis.MLPNN
      Yao et al., 2019[113]AsphaltModels to predict pavement deterioration (rutting, roughness, skid-resistance, transverse cracking, and surface distress) achieving an average testing R-square of 0.8692. Traffic loads affected skid-resistance and transverse cracking, while pavement treatments had a high impact on crack prediction models.NN
      Chopra et al., 2018[114]Flexible pavementsGenetic Programming (GP) models to predict pavement distress on urban roads in Patiala City, Punjab, India. These models accurately forecasted cracking, raveling, pothole, rutting, and roughness progression using data from 16 roads collected between 2012 and 2015.GP
      Okuda et al., 2017[115]GeneralMethod to predict rutting depth using NN and applied dropout and gradient clipping techniques to enhance accuracy. Compared to MLR and Multi-Layer Perceptron (MLP), RNN showed superior prediction ability, with lower RMSE and higher correlation coefficient (R) with measured valuesRNN
      Marcelino et al., 2017[116]AsphaltUsed scikit-learn, a Python machine learning library, to predict asphalt pavement friction using data from 113 sections of asphalt concrete pavement across the US. Two machine learning models were developed, showing similar performance. The research emphasized the significance of initial friction in the evolution of friction over time.ML-LR &ML-Lasso
      Hamdi et al., 2017[117]Flexible pavementsCreating an ANN model for SDI prediction based on parameters like crack area, crack width, pothole, rutting, patching, and depression. The model, applied to Integrated Road Management System (IRMS) data, achieved a high correlation (R2 = 0.996%). Rutting (59.8%), crack width (29.9%), and crack area (5.0%) were identified as the most influential parameters.ANN
      Sanabria et al., 2017[22]Flexible pavementsCompared Probabilistic Neural Networks Model (PNNM) and Ordered-Probit Models (OPM) using traffic data in which PNNM proved more accurate, identifying peak hour volume and single heavy commercial average volume as significant predictors.ANN
      Amin & Amador-Jiménez, 2017[118]Flexible and rigid pavementsApplied a Backpropagation Neural (BPN) network to improve PCI predictions for Montreal City's roads. Key factors like AADT, ESALs, Structural Number (SN), pavement age, slab thickness, and ΔPCI were considered.BPNN
      Ziari et al., 2016[119]GeneralExplores Support Vector Machine (SVM) methods to predict pavement condition using five kernels and nine input variables. Results demonstrate the effectiveness of the Pearson VII Universal kernel in accurately forecasting pavement performance over its life cycle.SVM
      Karlaftis & Badr, 2015[120]AsphaltUsed a genetically optimized Neural Network model to accurately predict alligator crack initiation following pavement treatments. Utilizing data from LTPP and SPS-5, the approach established links between external factors and cracking probability.ANN
      Kargah-Ostadi & Stoffels, 2015[121]AsphaltCreating a framework to compare pavement performance modeling techniques and improving parameterization robustness, comparing machine-learning techniques using Federal Highway Administration data. Key principles were considered, and models like artificial neural networks and support vector machines were tested.ANN
      Sirvio & Hollmén, 2014[122]GeneralComparing three prediction model for road condition.
      Least Squares Support Vector Regression outperforms Radial Basis Function networks and multiple linear regression, demonstrating superior accuracy in road condition predictions.      		LSSVR
      Lee et al., 2014[123]Flexible pavementsThree ANN models were developed using deflection databases. The model considering all key parameters proved most accurate and required less training time. Complex models didn't improve results significantly. Integrating engineering and statistical knowledge led to accurate predictions, minimizing time and effort.ANN
      Chandra et al., 2013[124]Flexible pavementsNonlinear regression models are outperformed by ANNs in predicting pavement roughness. The ANN model shows 18% lower Mean Absolute Error (MAE) than the linear model and 11% lower than the nonlinear model, demonstrating its superior forecasting capability based on distress parameters.ANN
      Bosurgi et al., 2007[125]GeneralShowcased the effectiveness of a neural network-based Sideway Force Coefficient (SFC) prediction model for an Italian motorway, proving its superiority over traditional linear regression methods in analyzing road problems.ANN
      Yang et al., 2003[126]Flexible pavementsANN models to forecast pavement conditions like crack rating, ride rating, and rut rating. These models, based on Florida Department of Transportation data, accurately predict pavement conditions for up to five years.ANN
      Huang et al., 1997[127]GeneralAssessed pavement condition prediction in Kansas Department of Transportation's (KDOT) system using multiple regression and ANN. ANNs proved more effective due to the binary nature of the data, outperforming multiple regression methods.ANN
      ANN: Artificial Neural Network, BPNN: Back Propagation Neural Network, DNN: DeepNeural Network, GBDT: Gradient Boosting Decision Tree, GONN: Genetically Optimized Neural Network, GP: Genetic Programming, LSSVR: Least Squares Support Vector Regression, ML-Lasso: Machine Learning regularized regression with Lasso, ML-LR: Machine Learning Linear Regression, MLPNN: Machine Learning Propagation Neural Network, RNN: Recurrent Neural Network.

      Table 3. 

      Summary of machine learning prediction models in the reviewed papers.

    • ModelsAdvantagesDisadvantagesPotential applications
      Deterministic models• User-friendly and straightforward
      • Uses well-defined equations and models, making them transparent and understandable
      • Utilizes material properties closely tied to real pavement performance
      • Offers dependable performance forecasts
      • Considers environmental factors, varying loads, and material aging effects
      • Yields highly accurate predictions when the conditions, materials, and loads are well-known
      • Provide insights into which factors contribute to pavement performance
      • Generally stable and do not rely on probabilistic assumptions      		• Limited to predicting performance within its specific development setting
      • Offers an impractical estimation for long-term performance prediction
      • Solely relies on the mechanics of materials theory for predictions
      • Sensitive to the quality and accuracy of the input data, and minor errors in material properties, traffic loads, or environmental factors can lead to inaccurate predictions
      • Hardly accounts for real-world conditions which are often subject to significant variability that can lead to conservative predictions.
      • Often simplify or ignore complex interactions between different factors affecting pavement performance
      • Are inflexible and do not adapt well to changing conditions      		• Useful in the initial design phase of pavements, especially when the conditions and loads are well-understood
      • Valuable for quality control during pavement construction
      • Can assist in planning maintenance and rehabilitation activities based on the predicted deterioration rate
      • Provides a clear way to illustrate the fundamental mechanics of pavement behavior
      • Suitable for predicting pavement performance on simple road networks or specific sections of roads with constant traffic loads and materials
      Probabilistic models• Well-suited to handle the inherent variability in pavement performance due to factors such as traffic loads, material properties, and environmental conditions
      • Allows for quantifying risks associated with pavement performance which is valuable for decision-makers in understanding the likelihood of different performance outcomes
      • They can adapt to different scenarios and changing conditions, making them suitable for a wide range of pavement types and locations
      • Can effectively integrate data from various sources, including historical performance data, material testing, and environmental monitoring, to improve predictions
      		• Often more complex than deterministic models, requiring advanced statistical and mathematical techniques
      • A significant amount of data is often needed to build reliable models
      • Outputs of models may be less intuitive for non-technical stakeholders due to their reliance on statistical distributions and probabilities
      • Models require extensive computational resources, which can be a limitation for some applications, especially when real-time predictions are needed
      		• Models are crucial for asset management in pavement networks helping to prioritize maintenance and rehabilitation efforts based on the likelihood of pavement distress and performance degradation.
      • Models can be used for life-cycle cost analysis, allowing agencies to evaluate the cost-effectiveness of different pavement design and maintenance strategies over the long term
      • Can be used to develop performance-based specifications that set performance targets meeting the specific performance criteria
      • Models can help in assessing the vulnerability of pavements and plan for adaptation strategies based on probabilistic scenarios in environmental conditions
      • Used in research to better understand the uncertainties and variability associated with pavement performance
      Machine learning models• Uncovers complex, data-driven patterns and relationships that might not be captured by traditional analytical methods and can lead to more accurate predictions.
      • Adapts changing conditions and continuously improve their predictions as new data becomes available
      • Effectively handles and integrates various types of data, including sensor data, images, and textual information, providing a holistic view of pavement performance
      • Models can be trained, hence can automate the prediction process, reducing the need for manual intervention and potentially saving time and resources
      • Can scale to handle large datasets and can be used for predicting performance across entire pavement networks      		• Require a substantial amount of high-quality training data, and the availability and quality of such data can be a limiting factor for their application
      • Some machine learning algorithms can be complex and difficult to interpret
      • Models are prone to overfitting, where they perform well on training data but generalize poorly to new, unseen data
      • Developing and fine-tuning machine learning models can be time-consuming and resource-intensive, requiring expertise in data science and machine learning
      • Models often lack a direct physical understanding of the underlying pavement mechanics, which may limit their utility for some engineering applications
      		• Predicts the timing and type of maintenance or repair needed for specific pavement sections based on data such as distress measurements, traffic loads, and environmental conditions
      • Forecasts the future condition and performance of pavements, helping agencies plan and budget for maintenance and rehabilitation activities
      • Can be used to optimize pavement designs by analyzing various design parameters and their impact on performance, leading to cost-effective design decisions.
      • Quantifies the risks associated with different pavement scenarios, helping agencies make informed decisions about asset management and funding allocation
      • Assesses the environmental impact of pavements, including energy consumption, emissions, and sustainability, by considering various design and maintenance strategies
      • Processes a real-time sensor data to monitor pavement performance, detect distress early, and trigger maintenance actions when necessary

      Table 4. 

      Comparison between deterministic, probabilistic, and machine learning models.