An appraisal of statistical and probabilistic models in highway pavements

Yıl 2024, Cilt: 8 Sayı: 2, 300 – 329, 30.04.2024

Jonah Agunwamba , Michael Toryila Tiza , Fidelis Okafor

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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Yıl 2024, Cilt: 8 Sayı: 2, 300 – 329, 30.04.2024

Jonah Agunwamba , Michael Toryila Tiza , Fidelis Okafor

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

Öz

Kaynakça

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• 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

Toplam 166 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

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