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An appraisal of statistical and probabilistic models in highway pavements

Yıl 2024, Cilt: 8 Sayı: 2, 300 - 329, 30.04.2024
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.

Kaynakça

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Yıl 2024, Cilt: 8 Sayı: 2, 300 - 329, 30.04.2024
https://doi.org/10.31127/tuje.1389994

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Kaynakça

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  • 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
Toplam 166 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İnşaat Yapım Mühendisliği
Bölüm Articles
Yazarlar

Jonah Agunwamba 0000-0002-0228-8250

Michael Toryila Tiza 0000-0003-3515-8951

Fidelis Okafor 0000-0002-9408-5302

Erken Görünüm Tarihi 13 Nisan 2024
Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 13 Kasım 2023
Kabul Tarihi 17 Şubat 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 2

Kaynak Göster

APA Agunwamba, J., Tiza, M. T., & Okafor, F. (2024). An appraisal of statistical and probabilistic models in highway pavements. Turkish Journal of Engineering, 8(2), 300-329. https://doi.org/10.31127/tuje.1389994
AMA Agunwamba J, Tiza MT, Okafor F. An appraisal of statistical and probabilistic models in highway pavements. TUJE. Nisan 2024;8(2):300-329. doi:10.31127/tuje.1389994
Chicago Agunwamba, Jonah, Michael Toryila Tiza, ve Fidelis Okafor. “An Appraisal of Statistical and Probabilistic Models in Highway Pavements”. Turkish Journal of Engineering 8, sy. 2 (Nisan 2024): 300-329. https://doi.org/10.31127/tuje.1389994.
EndNote Agunwamba J, Tiza MT, Okafor F (01 Nisan 2024) An appraisal of statistical and probabilistic models in highway pavements. Turkish Journal of Engineering 8 2 300–329.
IEEE J. Agunwamba, M. T. Tiza, ve F. Okafor, “An appraisal of statistical and probabilistic models in highway pavements”, TUJE, c. 8, sy. 2, ss. 300–329, 2024, doi: 10.31127/tuje.1389994.
ISNAD Agunwamba, Jonah vd. “An Appraisal of Statistical and Probabilistic Models in Highway Pavements”. Turkish Journal of Engineering 8/2 (Nisan 2024), 300-329. https://doi.org/10.31127/tuje.1389994.
JAMA Agunwamba J, Tiza MT, Okafor F. An appraisal of statistical and probabilistic models in highway pavements. TUJE. 2024;8:300–329.
MLA Agunwamba, Jonah vd. “An Appraisal of Statistical and Probabilistic Models in Highway Pavements”. Turkish Journal of Engineering, c. 8, sy. 2, 2024, ss. 300-29, doi:10.31127/tuje.1389994.
Vancouver Agunwamba J, Tiza MT, Okafor F. An appraisal of statistical and probabilistic models in highway pavements. TUJE. 2024;8(2):300-29.
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