Developing deterministic and probabilistic prediction models to evaluate high-temperature performance of modified bitumens

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@Article{EHSANI:2023:conbuildmat,

• author = "Mehrdad Ehsani and Pouria Hajikarimi and Masoud Esfandiar and Mohammad Rahi and Behzad Rasouli and Yousef Yousefi and Fereidoon {Moghadas Nejad}",
• title = "Developing deterministic and probabilistic prediction models to evaluate high-temperature performance of modified bitumens",
• journal = "Construction and Building Materials",
• volume = "401",
• pages = "132808",
• year = "2023",
• ISSN = "0950-0618",
• DOI = "doi:10.1016/j.conbuildmat.2023.132808",
• URL = "https://www.sciencedirect.com/science/article/pii/S0950061823025242",
• keywords = "genetic algorithms, genetic programming, MSCR, Multi-gene genetic programming, Logistic regression, Modified bitumen, Machine learning, Prediction model",
• abstract = "This study aims to develop deterministic and probabilistic prediction models for the multiple stress creep and recovery (MSCR) test. For this purpose, crumb rubber, polyphosphoric acid, and styrene-butadiene-styrene bitumen modifiers have been used with different dosages to modify high-temperature performance of PG 58-28 and PG 64-22 base bitumens. The MSCR test has been performed at different temperatures. Deterministic models are developed by the multi-gene genetic programming technique for each modifier individually, and the non-recoverable creep compliance (Jnr) and percent recovery (R) parameters are predicted. The accuracy of deterministic models is suitable and the performance of R models has been better than Jnr models. Furthermore, a comprehensive probabilistic model has been developed by using the logistic regression technique to predict different traffic levels. The accuracy of the probabilistic model is 0.85. The sensitivity analysis has been performed on this model and the effect of changes in the modifier dosage and temperature on the traffic levels have been investigated. Results show that using the probabilistic model, it is possible to find a range of modifier's dosage in which the traffic level is desired",

}

Genetic Programming entries for Mehrdad Ehsani Pouria Hajikarimi Masoud Esfandiar Mohammad Rahi Behzad Rasouli Yousef Yousefi Fereidoon Moghaddas Nejad

Citations