An appraisal of statistical and probabilistic models in highway pavements

Year 2024, Volume: 8 Issue: 2, 300 - 329, 30.04.2024

Jonah Agunwamba , Michael Toryila Tiza , Fidelis Okafor

https://doi.org/10.31127/tuje.1389994

Abstract

Accurate performance prediction is crucial for safe and efficient travel on highway pavements. Within pavement engineering, statistical models play a pivotal role in understanding pavement behavior and durability. This comprehensive study critically evaluates a spectrum of statistical models utilized in pavement engineering, encompassing mechanistic-empirical, Weibull distribution, Markov chain, regression, Bayesian networks, Monte Carlo simulation, artificial neural networks, support vector machines, random forest, decision tree, fuzzy logic, time series analysis, stochastic differential equations, copula, hidden semi-Markov, generalized linear, survival analysis, response surface methodology and extreme value theory models. The assessment meticulously examines equations, parameters, data prerequisites, advantages, limitations, and applicability of each model. Detailed discussions delve into the significance of equations and parameters, evaluating model performance in predicting pavement distress, performance assessment, design optimization, and life-cycle cost analysis. Key findings emphasize the critical aspects of accurate input parameters, calibration, validation, data availability, and model complexity. Strengths, limitations, and applicability across various pavement types, materials, and climate conditions are meticulously highlighted for each model. Recommendations are outlined to enhance the effectiveness of statistical models in pavement engineering. These suggestions encompass further research and development, standardized data collection, calibration and validation protocols, model integration, decision-making frameworks, collaborative efforts, and ongoing model evaluation. Implementing these recommendations is anticipated to enhance prediction accuracy and enable informed decision-making throughout highway pavement design, construction, maintenance, and management. This study is anticipated to serve as a valuable resource, providing guidance and insights for researchers, practitioners, and stakeholders engaged in asphalt engineering, facilitating the effective utilization of statistical models in real-world pavement projects.

Keywords

Model validation, Pavement engineering, Performance evaluation, Predictive modelling, Statistical models

References

• Hoang, N. D., & Nguyen, Q. L. (2019). A novel method for asphalt pavement crack classification based on image processing and machine learning. Engineering with Computers, 35, 487-498. https://doi.org/10.1007/s00366-018-0611-9
• Ricardo Archilla, A., & Madanat, S. (2001). Statistical model of pavement rutting in asphalt concrete mixes. Transportation Research Record, 1764(1), 70-77. https://doi.org/10.3141/1764-08
• Ahammed, M. A., & Tighe, S. L. (2008). Statistical modeling in pavement management: Do the models make sense?. Transportation research record, 2084(1), 3-10. https://doi.org/10.3141/2084-01
• Chu, C. Y., & Durango-Cohen, P. L. (2008). Empirical comparison of statistical pavement performance models. Journal of Infrastructure Systems, 14(2), 138-149. https://doi.org/10.1061/(ASCE)1076-0342(2008)14:2(138)
• Losa, M., Bacci, R., & Leandri, P. (2008). A statistical model for prediction of critical strains in pavements from deflection measurements. Road Materials and Pavement Design, 9(sup1), 373-396. https://doi.org/10.1080/14680629.2008.9690175
• Hussan, S., Kamal, M. A., Hafeez, I., Ahmad, N., Khanzada, S., & Ahmed, S. (2020). Modelling asphalt pavement analyzer rut depth using different statistical techniques. Road Materials and Pavement Design, 21(1), 117-142. https://doi.org/10.1080/14680629.2018.1481880
• Dylla, H., Asadi, S., Hassan, M., & Mohammad, L. N. (2013). Evaluating photocatalytic asphalt pavement effectiveness in real-world environments through developing models: a statistical and kinetic study. Road Materials and Pavement Design, 14(sup2), 92-105. https://doi.org/10.1080/14680629.2013.812839
• Ong, G. P., Flora, W., Noureldin, A. S., & Sinha, K. C. (2008). Statistical modeling of pavement raveling using texture measurements, 08-0382.
• Ghashghaei, H. T., & Hassani, A. (2016). Investigating the relationship between porosity and permeability coefficient for pervious concrete pavement by statistical modelling. Materials Sciences and Applications, 7(02), 101-107. https://doi.org/10.4236/msa.2016.72010
• Caliendo, C., Guida, M., & Parisi, A. (2007). A crash-prediction model for multilane roads. Accident Analysis & Prevention, 39(4), 657-670. https://doi.org/10.1016/j.aap.2006.10.012
• Fassman, E. A., & Blackbourn, S. (2010). Urban runoff mitigation by a permeable pavement system over impermeable soils. Journal of Hydrologic Engineering, 15(6), 475-485. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000238
• Onar, A., Thomas, F., Choubane, B., & Byron, T. (2006). Statistical mixed effects models for evaluation and prediction of accelerated pavement testing results. Journal of Transportation Engineering, 132(10), 771-780. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:10(771)
• Attoh-Okine, N. O. (1999). Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance. Advances in Engineering Software, 30(4), 291-302. https://doi.org/10.1016/S0965-9978(98)00071-4
• Jia, L., Sun, L., & Yu, Y. (2008). Asphalt pavement statistical temperature prediction models developed from measured data in China. In Plan, Build, and Manage Transportation Infrastructure in China, 723-732. https://doi.org/10.1061/40952(317)70
• McNeil, S., & Hendrickson, C. (1981). Three Statistical Models of Pavement Management Based on Turnpike Data with an Application to Roadway Cost Allocation.
• Drumm, E. C., Boateng-Poku, Y., & Johnson Pierce, T. (1990). Estimation of subgrade resilient modulus from standard tests. Journal of Geotechnical Engineering, 116(5), 774-789. https://doi.org/10.1061/(ASCE)0733-9410(1990)116:5(774)
• Prozzi, J. A., & Madanat, S. M. (2000). Using duration models to analyze experimental pavement failure data. Transportation Research Record, 1699(1), 87-94. https://doi.org/10.3141/1699-12
• Salem, O., AbouRizk, S., & Ariaratnam, S. (2003). Risk-based life-cycle costing of infrastructure rehabilitation and construction alternatives. Journal of Infrastructure Systems, 9(1), 6-15. https://doi.org/10.1061/(ASCE)1076-0342(2003)9:1(6)
• Hajek, J. J., & Bradbury, A. (1996). Pavement performance modeling using canadian strategic highway research program bayesian statistical methodology. Transportation Research Record, 1524(1), 160-170. https://doi.org/10.1177/0361198196152400119
• Alland, K., Vandenbossche, J. M., & Brigham, J. (2017). Statistical model to detect voids for curled or warped concrete pavements. Transportation Research Record, 2639(1), 28-38. https://doi.org/10.3141/2639-04
• Anastasopoulos, P. C., & Mannering, F. L. (2011). An empirical assessment of fixed and random parameter logit models using crash-and non-crash-specific injury data. Accident Analysis & Prevention, 43(3), 1140-1147. https://doi.org/10.1016/j.aap.2010.12.024
• Peng, T., Wang, X. L., & Chen, S. F. (2013). Pavement performance prediction model based on Weibull distribution. Applied Mechanics and Materials, 378, 61-64. https://doi.org/10.4028/www.scientific.net/AMM.378.61
• Aliha, M. R. M., & Fattahi Amirdehi, H. R. (2017). Fracture toughness prediction using Weibull statistical method for asphalt mixtures containing different air void contents. Fatigue & Fracture of Engineering Materials & Structures, 40(1), 55-68. https://doi.org/10.1111/ffe.12474
• Thomas, O., & Sobanjo, J. (2013). Comparison of Markov chain and semi-Markov models for crack deterioration on flexible pavements. Journal of Infrastructure Systems, 19(2), 186-195. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000112
• Meegoda, J. N., & Gao, S. (2014). Roughness progression model for asphalt pavements using long-term pavement performance data. Journal of Transportation Engineering, 140(8), 04014037. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000682
• Dong, Q., & Huang, B. (2014). Evaluation of influence factors on crack initiation of LTPP resurfaced-asphalt pavements using parametric survival analysis. Journal of Performance of Constructed Facilities, 28(2), 412-421. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000409
• Rezaei, A., & Masad, E. (2013). Experimental-based model for predicting the skid resistance of asphalt pavements. International Journal of Pavement Engineering, 14(1), 24-35. https://doi.org/10.1080/10298436.2011.643793
• Tsai, B. W., Harvey, J. T., & Monismith, C. L. (2003). Application of Weibull theory in prediction of asphalt concrete fatigue performance. Transportation Research Record, 1832(1), 121-130. https://doi.org/10.3141/1832-15
• Yi, J., Shen, S., Muhunthan, B., & Feng, D. (2014). Viscoelastic–plastic damage model for porous asphalt mixtures: Application to uniaxial compression and freeze–thaw damage. Mechanics of Materials, 70, 67-75. https://doi.org/10.1016/j.mechmat.2013.12.002
• Rezaei, A., Masad, E., & Chowdhury, A. (2011). Development of a model for asphalt pavement skid resistance based on aggregate characteristics and gradation. Journal of Transportation Engineering, 137(12), 863-873. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000280
• Adamu, M., Mohammed, B. S., Liew, M. S., & Alaloul, W. S. (2019). Evaluating the impact resistance of roller compacted concrete containing crumb rubber and nanosilica using response surface methodology and Weibull distribution. World Journal of Engineering, 16(1), 33-43. https://doi.org/10.1108/WJE-10-2018-0361
• Sun, Z., Xu, H., Tan, Y., Lv, H., & Assogba, O. C. (2019). Low-temperature performance of asphalt mixture based on statistical analysis of winter temperature extremes: A case study of Harbin China. Construction and Building Materials, 208, 258-268. https://doi.org/10.1016/j.conbuildmat.2019.02.131
• Cai, X., Fu, L., Zhang, J., Chen, X., & Yang, J. (2020). Damage analysis of semi-flexible pavement material under axial compression test based on acoustic emission technique. Construction and Building Materials, 239, 117773. https://doi.org/10.1016/j.conbuildmat.2019.117773
• Zollinger, D. G., & McCullough, B. F. (1994). Development of Weibull reliability factors and analysis for calibration of pavement design models using field data. Transportation Research Record, 1449, 18-25.
• Sathyanarayanan, S., Shankar, V., & Donnell, E. T. (2008). Pavement marking retroreflectivity inspection data: a Weibull analysis. Transportation Research Record, 2055(1), 63-70. https://doi.org/10.3141/2055-08
• Coleri, E., Tsai, B. W., & Monismith, C. L. (2008). Pavement rutting performance prediction by integrated Weibull approach. Transportation Research Record, 2087(1), 120-130. https://doi.org/10.3141/2087-13
• Chen, X., Wu, S., & Zhou, J. (2014). Strength values of cementitious materials in bending and tension test methods. Journal of Materials in Civil Engineering, 26(3), 484-490. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000846
• AlShareedah, O., Nassiri, S., & Dolan, J. D. (2019). Pervious concrete under flexural fatigue loading: Performance evaluation and model development. Construction and Building Materials, 207, 17-27. https://doi.org/10.1016/j.conbuildmat.2019.02.111
• Roy, U., Albatayneh, O., & Ksaibati, K. (2023). Pavement marking practices, standards, applications, and retroreflectivity. Transportation Research Record, 2677(2), 564-576. https://doi.org/10.1177/03611981221107920
• Mills, L. (2010). Hierarchical Markov chain Monte Carlo and pavement roughness model. [Doctoral dissertation, University of Delaware].
• Ganeshan, R. (1989). A pavement performance model based on the Markov process. [Doctoral dissertation, University of Massachusetts at Amherst].
• Edulakanti, T. (2004). Pavement Performance Forecasting Using Markov Chain Process. [Doctoral dissertation, University of Toledo].
• Moreira, A. V., Tinoco, J., Oliveira, J. R., & Santos, A. (2018). An application of Markov chains to predict the evolution of performance indicators based on pavement historical data. International Journal of Pavement Engineering, 19(10), 937-948. https://doi.org/10.1080/10298436.2016.1224412
• Piryonesi, S. M., & El-Diraby, T. E. (2020). Data analytics in asset management: Cost-effective prediction of the pavement condition index. Journal of Infrastructure Systems, 26(1), 04019036. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000512
• Mers, M., Yang, Z., Hsieh, Y. A., & Tsai, Y. (2023). Recurrent neural networks for pavement performance forecasting: review and model performance comparison. Transportation Research Record, 2677(1), 610-624. https://doi.org/10.1177/03611981221100521
• Yang, J., Gunaratne, M., Lu, J. J., & Dietrich, B. (2005). Use of recurrent Markov chains for modeling the crack performance of flexible pavements. Journal of Transportation Engineering, 131(11), 861-872. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:11(861)
• Frangopol, D. M., Kallen, M. J., & Noortwijk, J. M. V. (2004). Probabilistic models for life‐cycle performance of deteriorating structures: review and future directions. Progress in Structural Engineering and Materials, 6(4), 197-212. https://doi.org/10.1002/pse.180
• Fuentes, L., Camargo, R., Arellana, J., Velosa, C., & Martinez, G. (2021). Modelling pavement serviceability of urban roads using deterministic and probabilistic approaches. International Journal of Pavement Engineering, 22(1), 77-86. https://doi.org/10.1080/10298436.2019.1577422
• Elhadidy, A. A., El-Badawy, S. M., & Elbeltagi, E. E. (2021). A simplified pavement condition index regression model for pavement evaluation. International Journal of Pavement Engineering, 22(5), 643-652. https://doi.org/10.1080/10298436.2019.1633579
• Attoh-Okine, N. O., Cooger, K., & Mensah, S. (2009). Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling. Construction and Building Materials, 23(9), 3020-3023. https://doi.org/10.1016/j.conbuildmat.2009.04.010
• Zhang, W., & Durango-Cohen, P. L. (2014). Explaining heterogeneity in pavement deterioration: Clusterwise linear regression model. Journal of Infrastructure Systems, 20(2), 04014005. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000182
• Luo, Z. (2013). Pavement performance modelling with an auto-regression approach. International Journal of Pavement Engineering, 14(1), 85-94. https://doi.org/10.1080/10298436.2011.617442
• Kim, S. H., & Kim, N. (2006). Development of performance prediction models in flexible pavement using regression analysis method. KSCE Journal of Civil Engineering, 10, 91-96. https://doi.org/10.1007/BF02823926
• Lethanh, N., Kaito, K., & Kobayashi, K. (2015). Infrastructure deterioration prediction with a Poisson hidden Markov model on time series data. Journal of Infrastructure Systems, 21(3), 04014051. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000242
• Qiao, F., Nabi, M., Li, Q., & Yu, L. (2020). Estimating light-duty vehicle emission factors using random forest regression model with pavement roughness. Transportation Research Record, 2674(8), 37-52. https://doi.org/10.1177/0361198120922997
• Ashrafian, A., Taheri Amiri, M. J., Masoumi, P., Asadi-shiadeh, M., Yaghoubi-chenari, M., Mosavi, A., & Nabipour, N. (2020). Classification-based regression models for prediction of the mechanical properties of roller-compacted concrete pavement. Applied Sciences, 10(11), 3707. https://doi.org/10.3390/app10113707
• Bianchini, A., & Bandini, P. (2010). Prediction of pavement performance through neuro‐fuzzy reasoning. Computer‐Aided Civil and Infrastructure Engineering, 25(1), 39-54. https://doi.org/10.1111/j.1467-8667.2009.00615.x
• Owusu-Ababio, S. (1995). Modeling skid resistance for flexible pavements: a comparison between regression and neural network models. Transportation Research Record, 1501, 60-71.
• Gong, H., Sun, Y., Shu, X., & Huang, B. (2018). Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials, 189, 890-897. https://doi.org/10.1016/j.conbuildmat.2018.09.017
• Jiménez, L. A., & Mrawira, D. (2012). Bayesian regression in pavement deterioration modeling: revisiting the AASHO road test rut depth model. Infraestructura Vial, 14(25), 28-35. https://doi.org/10.15517/iv.v14i25.3926
• Ghasemi, P., Aslani, M., Rollins, D. K., Williams, R. C., & Schaefer, V. R. (2018). Modeling rutting susceptibility of asphalt pavement using principal component pseudo inputs in regression and neural networks.
• Yu, J., Xiong, C., Zhang, X., & Li, W. (2018). More accurate modulus back-calculation by reducing noise information from in situ–measured asphalt pavement deflection basin using regression model. Construction and Building Materials, 158, 1026-1034. https://doi.org/10.1016/j.conbuildmat.2017.10.022
• Puppala, A. J., Hoyos, L. R., & Potturi, A. K. (2011). Resilient moduli response of moderately cement-treated reclaimed asphalt pavement aggregates. Journal of Materials in Civil Engineering, 23(7), 990-998. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000268
• Makendran, C., Murugasan, R., & Velmurugan, S. (2015). Performance prediction modelling for flexible pavement on low volume roads using multiple linear regression analysis. Journal of Applied Mathematics, 192485. https://doi.org/10.1155/2015/192485
• Fwa, T. F., & Chandrasegaran, S. (2001). Regression model for back-calculation of rigid-pavement properties. Journal of Transportation Engineering, 127(4), 353-355. https://doi.org/10.1061/(ASCE)0733-947X(2001)127:4(353)
• Gao, L., Aguiar-Moya, J. P., & Zhang, Z. (2012). Bayesian analysis of heterogeneity in modeling of pavement fatigue cracking. Journal of Computing in Civil Engineering, 26(1), 37-43. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000114
• Liu, L., & Gharaibeh, N. G. (2014). Bayesian model for predicting the performance of pavements treated with thin hot-mix asphalt overlays. Transportation Research Record, 2431(1), 33-41. https://doi.org/10.3141/2431-05
• Tabatabaee, N., & Ziyadi, M. (2013). Bayesian approach to updating Markov-based models for predicting pavement performance. Transportation Research Record, 2366(1), 34-42. https://doi.org/10.3141/2366-04
• Golroo, A., & Tighe, S. L. (2012). Pervious concrete pavement performance modeling using the Bayesian statistical technique. Journal of Transportation Engineering, 138(5), 603-609. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000363
• Onar, A., Thomas, F., Choubane, B., & Byron, T. (2007). Bayesian degradation modeling in accelerated pavement testing with estimated transformation parameter for the response. Journal of Transportation Engineering, 133(12), 677-687. https://doi.org/10.1061/(ASCE)0733-947X(2007)133:12(677)
• Han, D., Kaito, K., Kobayashi, K., & Aoki, K. (2016). Performance evaluation of advanced pavement materials by Bayesian Markov Mixture Hazard model. KSCE Journal of Civil Engineering, 20, 729-737. https://doi.org/10.1007/s12205-015-0375-3
• Yu, B., & Lu, Q. (2013). Bayesian model for tyre/asphalt pavement noise. In Proceedings of the Institution of Civil Engineers-Transport, 166(4), 241-252. https://doi.org/10.1680/tran.11.00040
• Kumar, U., Ahmadi, A., Verma, A. K., & Varde, P. (Eds.). (2015). Current trends in reliability, availability, maintainability and safety: an industry perspective. Springer.
• Osorio-Lird, A., Chamorro, A., Videla, C., Tighe, S., & Torres-Machi, C. (2018). Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management. Structure and Infrastructure Engineering, 14(9), 1169-1181. https://doi.org/10.1080/15732479.2017.1402064
• Çakmak, R., & Dündar, A. (2023). Design and implementation of a real-time demonstration setup for dynamic highway tunnel lighting control research studies. Turkish Journal of Engineering, 7(1), 33-41. https://doi.org/10.31127/tuje.1013374
• Mills, L. N., Attoh-Okine, N. O., & McNeil, S. (2012). Hierarchical Markov chain Monte Carlo simulation for modeling transverse cracks in highway pavements. Journal of Transportation Engineering, 138(6), 700-705. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000383
• Chaudhari, A., & Vasudevan, H. (2022). Reliability based design optimization of casting process parameters using Markov chain model. Materials Today: Proceedings, 63, 602-606. https://doi.org/10.1016/j.matpr.2022.04.189
• Hong, F., & Prozzi, J. A. (2006). Estimation of pavement performance deterioration using Bayesian approach. Journal of Infrastructure Systems, 12(2), 77-86. https://doi.org/10.1061/(ASCE)1076-0342(2006)12:2(77)
• Mohan, A., & Poobal, S. (2018). Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal, 57(2), 787-798. https://doi.org/10.1016/j.aej.2017.01.020
• Giacomoni, M. H., & Joseph, J. (2017). Multi-objective evolutionary optimization and Monte Carlo simulation for placement of low impact development in the catchment scale. Journal of Water Resources Planning and Management, 143(9), 04017053. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000812
• Li, N., Xie, W. C., & Haas, R. (1996). Reliability-based processing of Markov chains for modeling pavement network deterioration. Transportation Research Record, 1524(1), 203-213. https://doi.org/10.1177/0361198196152400124
• Yu, B., Wang, S., & Gu, X. (2018). Estimation and uncertainty analysis of energy consumption and CO2 emission of asphalt pavement maintenance. Journal of Cleaner Production, 189, 326-333. https://doi.org/10.1016/j.jclepro.2018.04.068
• Mohd Hasan, M. R., Hiller, J. E., & You, Z. (2016). Effects of mean annual temperature and mean annual precipitation on the performance of flexible pavement using ME design. International Journal of Pavement Engineering, 17(7), 647-658. https://doi.org/10.1080/10298436.2015.1019504
• Dilip, D. M., & Sivakumar Babu, G. L. (2013). Methodology for pavement design reliability and back analysis using Markov chain Monte Carlo simulation. Journal of Transportation Engineering, 139(1), 65-74. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000455
• Dizaj, E. A., Padgett, J. E., & Kashani, M. M. (2021). A Markov chain-based model for structural vulnerability assessmentof corrosion-damaged reinforced concrete bridges. Philosophical Transactions of the Royal Society A, 379(2203), 20200290. https://doi.org/10.1098/rsta.2020.0290
• Mallick, R. B., Jacobs, J. M., Miller, B. J., Daniel, J. S., & Kirshen, P. (2018). Understanding the impact of climate change on pavements with CMIP5, system dynamics and simulation. International Journal of Pavement Engineering, 19(8), 697-705. https://doi.org/10.1080/10298436.2016.1199880
• Li, N., Haas, R., & Xie, W. C. (1997). Development of a new asphalt pavement performance prediction model. Canadian Journal of Civil Engineering, 24(4), 547-559. https://doi.org/10.1139/l97-001
• Althaqafi, E., & Chou, E. (2022). Developing bridge deterioration models using an artificial neural network. Infrastructures, 7(8), 101. https://doi.org/10.3390/infrastructures7080101
• Anyala, M., Odoki, J. B., & Baker, C. J. (2014). Hierarchical asphalt pavement deterioration model for climate impact studies. International Journal of Pavement Engineering, 15(3), 251-266. https://doi.org/10.1080/10298436.2012.687105
• Irfan, M., Khurshid, M. B., Bai, Q., Labi, S., & Morin, T. L. (2012). Establishing optimal project-level strategies for pavement maintenance and rehabilitation–A framework and case study. Engineering Optimization, 44(5), 565-589. https://doi.org/10.1080/0305215X.2011.588226
• Abdallah, I., Melchor-Lucero, O., Ferregut, C., & Nazarian, S. (2000). Artificial neural network models for assessing remaining life of flexible pavements. Texas Department of Transportation.
• Ceylan, H. (2002). Analysis and design of concrete pavement systems using artificial neural networks. [Doctoral dissertation, University of Illinois at Urbana-Champaign].
• Utsev, T., Tiza, T. M., Mogbo, O., Singh, S. K., Chakravarti, A., Shaik, N., & Singh, S. P. (2022). Application of nanomaterials in civil engineering. Materials Today: Proceedings, 62, 5140-5146. https://doi.org/10.1016/j.matpr.2022.02.480
• Flood, I., & Kartam, N. (1998). Artificial neural networks for civil engineers: Advanced features and applications. ASCE Publications.
• Çubukçu, E. A., Demir, V., & Sevimli, M. F. (2022). Digital elevation modeling using artificial neural networks, deterministic and geostatistical interpolation methods. Turkish Journal of Engineering, 6(3), 199-205. https://doi.org/10.31127/tuje.889570
• Badawy, S., & Chen, D. H. (2020). Recent Developments in Pavement Engineering. Springer International Publishing. https://doi.org/10.1007/978-3-030-34196-1
• Anupam, K., Papagiannakis, A. T., Bhasin, A., & Little, D. (Eds.). (2020). Advances in Materials and Pavement Performance Prediction II: Contributions to the 2nd International Conference on Advances in Materials and Pavement Performance Prediction (AM3P 2020), 27-29 May, 2020, San Antonio, TX, USA. CRC Press.
• Ai, D., Jiang, G., Kei, L. S., & Li, C. (2018). Automatic pixel-level pavement crack detection using information of multi-scale neighborhoods. IEEE Access, 6, 24452-24463. https://doi.org/10.1109/ACCESS.2018.2829347
• Gopalakrishnan, K., & Kim, S. (2011). Support vector machines approach to HMA stiffness prediction. Journal of Engineering Mechanics, 137(2), 138-146. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000214
• Ziari, H., Maghrebi, M., Ayoubinejad, J., & Waller, S. T. (2016). Prediction of pavement performance: Application of support vector regression with different kernels. Transportation Research Record, 2589(1), 135-145. https://doi.org/10.3141/2589-15
• Kargah-Ostadi, N., & Stoffels, S. M. (2015). Framework for development and comprehensive comparison of empirical pavement performance models. Journal of Transportation Engineering, 141(8), 04015012. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000779
• Nitsche, P., Stütz, R., Kammer, M., & Maurer, P. (2014). Comparison of machine learning methods for evaluating pavement roughness based on vehicle response. Journal of Computing in Civil Engineering, 28(4), 04014015. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000285
• Bashar, M. Z., & Torres-Machi, C. (2021). Performance of machine learning algorithms in predicting the pavement international roughness index. Transportation Research Record, 2675(5), 226-237. https://doi.org/10.1177/0361198120986171
• Cao, R., Leng, Z., Hsu, S. C., & Hung, W. T. (2020). Modelling of the pavement acoustic longevity in Hong Kong through machine learning techniques. Transportation Research Part D: Transport and Environment, 83, 102366. https://doi.org/10.1016/j.trd.2020.102366
• Zhang, A., Wang, K. C., Li, B., Yang, E., Dai, X., Peng, Y., ... & Chen, C. (2017). Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network. Computer‐Aided Civil and Infrastructure Engineering, 32(10), 805-819. https://doi.org/10.1111/mice.12297
• Abdelaziz, N., Abd El-Hakim, R. T., El-Badawy, S. M., & Afify, H. A. (2020). International Roughness Index prediction model for flexible pavements. International Journal of Pavement Engineering, 21(1), 88-99. https://doi.org/10.1080/10298436.2018.1441414
• Guo, X., & Hao, P. (2021). Using a random forest model to predict the location of potential damage on asphalt pavement. Applied Sciences, 11(21), 10396. https://doi.org/10.3390/app112110396
• Pan, Y., Zhang, X., Cervone, G., & Yang, L. (2018). Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10), 3701-3712. https://doi.org/10.1109/JSTARS.2018.2865528
• Ehsani, M., Moghadas Nejad, F., & Hajikarimi, P. (2023). Developing an optimized faulting prediction model in Jointed Plain Concrete Pavement using artificial neural networks and random forest methods. International Journal of Pavement Engineering, 24(2), 2057975. https://doi.org/10.1080/10298436.2022.2057975
• Cordero, J. M., Borge, R., & Narros, A. (2018). Using statistical methods to carry out in field calibrations of low cost air quality sensors. Sensors and Actuators B: Chemical, 267, 245-254. https://doi.org/10.1016/j.snb.2018.04.021
• Gong, H., Sun, Y., Mei, Z., & Huang, B. (2018). Improving accuracy of rutting prediction for mechanistic-empirical pavement design guide with deep neural networks. Construction and Building Materials, 190, 710-718. https://doi.org/10.1016/j.conbuildmat.2018.09.087
• Zhan, Y., Li, J. Q., Liu, C., Wang, K. C., Pittenger, D. M., & Musharraf, Z. (2021). Effect of aggregate properties on asphalt pavement friction based on random forest analysis. Construction and Building Materials, 292, 123467. https://doi.org/10.1016/j.conbuildmat.2021.123467
• Karballaeezadeh, N., Mohammadzadeh S, D., Moazemi, D., Band, S. S., Mosavi, A., & Reuter, U. (2020). Smart structural health monitoring of flexible pavements using machine learning methods. Coatings, 10(11), 1100. https://doi.org/10.3390/coatings10111100
• Yang, M. Y., & Förstner, W. (2011). A hierarchical conditional random field model for labeling and classifying images of man-made scenes. In 2011 IEEE international conference on computer vision workshops (ICCV Workshops), 196-203. https://doi.org/10.1109/ICCVW.2011.6130243
• Guo, R., Fu, D., & Sollazzo, G. (2022). An ensemble learning model for asphalt pavement performance prediction based on gradient boosting decision tree. International Journal of Pavement Engineering, 23(10), 3633-3646. https://doi.org/10.1080/10298436.2021.1910825
• Huang, C. L., Hsu, N. S., Liu, H. J., & Huang, Y. H. (2018). Optimization of low impact development layout designs for megacity flood mitigation. Journal of Hydrology, 564, 542-558. https://doi.org/10.1016/j.jhydrol.2018.07.044
• Zhou, G., & Wang, L. (2012). Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation. Transportation Research Part C: Emerging Technologies, 21(1), 287-305. https://doi.org/10.1016/j.trc.2011.10.007
• Abo-Hashema, M. A., & Sharaf, E. A. (2009). Development of maintenance decision model for flexible pavements. International Journal of Pavement Engineering, 10(3), 173-187. https://doi.org/10.1080/10298430802169457
• Zhan, Y., Liu, C., Deng, Q., Feng, Q., Qiu, Y., Zhang, A., & He, X. (2022). Integrated FFT and XGBoost framework to predict pavement skid resistance using automatic 3D texture measurement. Measurement, 188, 110638. https://doi.org/10.1016/j.measurement.2021.110638
• Xiao, J., Kulakowski, B. T., & EI-Gindy, M. (2000). Prediction of risk of wet-pavement accidents: Fuzzy logic model. Transportation Research Record, 1717(1), 28-36. https://doi.org/10.3141/1717-05
• Saltan, M., Saltan, S., & Şahiner, A. (2007). Fuzzy logic modeling of deflection behavior against dynamic loading in flexible pavements. Construction and Building Materials, 21(7), 1406-1414. https://doi.org/10.1016/j.conbuildmat.2006.07.004
• Karaşahin, M., & Terzi, S. (2014). Performance model for asphalt concrete pavement based on the fuzzy logic approach. Transport, 29(1), 18-27. https://doi.org/10.3846/16484142.2014.893926
• Moazami, D., Behbahani, H., & Muniandy, R. (2011). Pavement rehabilitation and maintenance prioritization of urban roads using fuzzy logic. Expert Systems with Applications, 38(10), 12869-12879. https://doi.org/10.1016/j.eswa.2011.04.079
• Mariani, M. C., Bianchini, A., & Bandini, P. (2012). Normalized truncated Levy walk applied to flexible pavement performance. Transportation Research Part C: Emerging Technologies, 24, 1-8. https://doi.org/10.1016/j.trc.2012.01.006
• Onyelowe, K. C., Alaneme, G. U., Onyia, M. E., Bui Van, D., Diomonyeka, M. U., Nnadi, E., ... & Onukwugha, E. (2021). Comparative modeling of strength properties of hydrated-lime activated rice-husk-ash (HARHA) modified soft soil for pavement construction purposes by artificial neural network (ANN) and fuzzy logic (FL). Jurnal Kejuruteraan, 33(2), 365-384. https://doi.org/10.17576/jkukm-2021-33(2)-20
• Sundin, S., & Braban‐Ledoux, C. (2001). Artificial intelligence–based decision support technologies in pavement management. Computer‐Aided Civil and Infrastructure Engineering, 16(2), 143-157. https://doi.org/10.1111/0885-9507.00220
• Al-Haddad, A. H. A., & Al-Haydari, I. S. J. (2018). Modeling of flexible pavement serviceability based on the fuzzy logic theory. Journal of Transportation Engineering, Part B: Pavements, 144(2), 04018017.
• Luis, M. P. J., & Inés, B. C. G. (2018). Fuzzy Logic Based Modeling for Pavement Characterization. In Materials for Sustainable Infrastructure: Proceedings of the 1st GeoMEast International Congress and Exhibition, Egypt 2017 on Sustainable Civil Infrastructures 1, 27-45. https://doi.org/10.1007/978-3-319-61633-9_3
• Nihan, N. L., & Holmesland, K. O. (1981). Use of Box and Jenkıns time-serıes analysıs to ısolate the impact of a pavement improvement policy (No. 819).
• Farajzadeh, J., Fard, A. F., & Lotfi, S. (2014). Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model. Water Resources and Industry, 7, 38-48. https://doi.org/10.1016/j.wri.2014.10.003
• Tiza, M. T., Jirgba, K., Sani, H. A., & Sesugh, T. (2022). Effect of thermal variances on flexible pavements. Journal of Sustainable Construction Materials and Technologies, 7(3), 220-230. https://doi.org/10.47481/jscmt.1136848
• Hashemloo, B. (2008). Forecasting pavement surface temperature using time series and artificial neural networks. [Master's thesis, University of Waterloo].
• de Feo, F. (2021). The averaging principle for non-autonomous slow-fast stochastic differential equations and an application to a local stochastic volatility model. Journal of Differential Equations, 302, 406-443. https://doi.org/10.1016/j.jde.2021.09.002
• Tiza, T. M., Mogbo, O., Singh, S. K., Shaik, N., & Shettar, M. P. (2022). Bituminous pavement sustainability improvement strategies. Energy Nexus, 6, 100065. https://doi.org/10.1016/j.nexus.2022.100065
• Aguiar-Moya, J. P., Vargas-Nordcbeck, A., Leiva-Villacorta, F., & Loría-Salazar, L. G. (Eds.). (2016). The roles of accelerated pavement testing in pavement sustainability: engineering, environment, and economics. Springer.
• Srinivas, S., Menon, D., & Meher Prasad, A. (2006). Multivariate simulation and multimodal dependence modeling of vehicle axle weights with copulas. Journal of Transportation Engineering, 132(12), 945-955. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:12(945)
• Ma, X., Luan, S., Ding, C., Liu, H., & Wang, Y. (2019). Spatial interpolation of missing annual average daily traffic data using copula-based model. IEEE Intelligent Transportation Systems Magazine, 11(3), 158-170. https://doi.org/10.1109/MITS.2019.2919504
• Donev, V., & Hoffmann, M. (2019). Condition prediction and estimation of service life in the presence of data censoring and dependent competing risks. International Journal of Pavement Engineering, 20(3), 313-331. https://doi.org/10.1080/10298436.2017.1293264
• Pulugurtha, S. S., Kusam, P. R., & Patel, K. J. (2012). Assessment of the effect of pavement macrotexture on interstate crashes. Journal of transportation engineering, 138(5), 610-617. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000357
• Chou, J. S. (2009). Generalized linear model-based expert system for estimating the cost of transportation projects. Expert Systems with Applications, 36(3), 4253-4267. https://doi.org/10.1016/j.eswa.2008.03.017
• Bhandari, S., Luo, X., & Wang, F. (2023). Understanding the effects of structural factors and traffic loading on flexible pavement performance. International Journal of Transportation Science and Technology, 12(1), 258-272. https://doi.org/10.1016/j.ijtst.2022.02.004
• Yu, J., Chou, E. Y., & Luo, Z. (2007). Development of linear mixed effects models for predicting individual pavement conditions. Journal of Transportation Engineering, 133(6), 347-354. https://doi.org/10.1061/(ASCE)0733-947X(2007)133:6(347)
• Cafiso, S., Di Graziano, A., Di Silvestro, G., La Cava, G., & Persaud, B. (2010). Development of comprehensive accident models for two-lane rural highways using exposure, geometry, consistency and context variables. Accident Analysis & Prevention, 42(4), 1072-1079. https://doi.org/10.1016/j.aap.2009.12.015
• Wang, Y., Mahboub, K. C., & Hancher, D. E. (2005). Survival analysis of fatigue cracking for flexible pavements based on long-term pavement performance data. Journal of Transportation Engineering, 131(8), 608-616. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:8(608)
• Hunaidi, O. (1998). Evolution-based genetic algorithms for analysis of non-destructive surface wave tests on pavements. NDT & e International, 31(4), 273-280. https://doi.org/10.1016/S0963-8695(98)00007-3
• Gharaibeh, N. G., & Darter, M. I. (2003). Probabilistic analysis of highway pavement life for Illinois. Transportation Research Record, 1823(1), 111-120. https://doi.org/10.3141/1823-13
• Dong, Q., & Huang, B. (2015). Failure probability of resurfaced preventive maintenance treatments: Investigation into long-term pavement performance program. Transportation Research Record, 2481(1), 65-74. https://doi.org/10.3141/2481-09
• Dong, Q., Chen, X., Dong, S., & Ni, F. (2021). Data analysis in pavement engineering: An overview. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22020-22039. https://doi.org/10.1109/TITS.2021.3115792
• Senadheera, S. P., & Zollinger, D. G. (1994). Framework for incorporation of spalling in design of concrete pavements. Transportation Research Record, (1449), 114-122.
• Chen, H., Barbieri, D. M., Zhang, X., & Hoff, I. (2022). Reliability of calculation of dynamic modulus for asphalt mixtures using different master curve models and shift factor equations. Materials, 15(12), 4325. https://doi.org/10.3390/ma15124325
• Zheng, L., Sayed, T., & Essa, M. (2019). Validating the bivariate extreme value modeling approach for road safety estimation with different traffic conflict indicators. Accident Analysis & Prevention, 123, 314-323. https://doi.org/10.1016/j.aap.2018.12.007
• Tabatabaei, S. A. H., Delforouzi, A., Khan, M. H., Wesener, T., & Grzegorzek, M. (2019). Automatic detection of the cracks on the concrete railway sleepers. International Journal of Pattern Recognition and Artificial Intelligence, 33(09), 1955010. https://doi.org/10.1142/S0218001419550103
• Little, D. N., Allen, D. H., & Bhasin, A. (2018). Modeling and design of flexible pavements and materials. Berlin: Springer.
• Kim, Y. R. (2008). Modeling of asphalt concrete. ASCE Press; McGraw-Hill, Reston, VA.
• El-Badawy, S., & Abd El-Hakim, R. (2018). Recent Developments in Pavement Design, Modeling and Performance: Proceedings of the 2nd GeoMEast International Congress and Exhibition on Sustainable Civil Infrastructures, Egypt 2018–The Official International Congress of the Soil-Structure Interaction Group in Egypt (SSIGE).
• Henry, J. J., & Wambold, J. C. (Eds.). (1992). Vehicle, tire, pavement interface (Vol. 1164). ASTM International.
• Hosseini, A. (2019). Data-Driven Modeling of In-Service Performance of Flexible Pavements, Using Life-Cycle Information. [Doctoral dissertation, Temple University].
• Kahraman, F., & Sugözü, B. (2019). An integrated approach based on the taguchi method and response surface methodology to optimize parameter design of asbestos-free brake pad material. Turkish Journal of Engineering, 3(3), 127-132. https://doi.org/10.31127/tuje.479458
• Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., & Escaleira, L. A. (2008). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76(5), 965-977. https://doi.org/10.1016/j.talanta.2008.05.019
• Campatelli, G., Lorenzini, L., & Scippa, A. (2014). Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel. Journal of Cleaner Production, 66, 309-316. https://doi.org/10.1016/j.jclepro.2013.10.025
• Ferreira, S. C., Bruns, R. E., Ferreira, H. S., Matos, G. D., David, J. M., Brandão, G. C., ... & Dos Santos, W. N. L. (2007). Box-Behnken design: An alternative for the optimization of analytical methods. Analytica Chimica Acta, 597(2), 179-186. https://doi.org/10.1016/j.aca.2007.07.011
• Sibalija, T. V., & Majstorovic, V. D. (2012). An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence. Journal of Intelligent Manufacturing, 23, 1511-1528. https://doi.org/10.1007/s10845-010-0451-y
• Kim, C., & Choi, K. K. (2008). Reliability-based design optimization using response surface method with prediction interval estimation. Journal of Mechanical Design, 130(12). https://doi.org/10.1115/1.2988476
• Lee, S. H., Kim, H. Y., & Oh, S. I. (2002). Cylindrical tube optimization using response surface method based on stochastic process. Journal of Materials Processing Technology, 130, 490-496. https://doi.org/10.1016/S0924-0136(02)00794-X
• Ma, H., Sun, Z., & Ma, G. (2022). Research on compressive strength of manufactured sand concrete based on response surface methodology (RSM). Applied Sciences, 12(7), 3506. https://doi.org/10.3390/app12073506
• Tiza, M. T., Okafor, F., & Agunwamba, J. Application of Scheffe's Simplex Lattice Model in concrete mixture design and performance enhancement. Environmental Research and Technology, 7. https://doi.org/10.35208/ert.1406013