Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction

被引:17
|
作者
Milad, Abdalrhman Abrahim [1 ]
Adwan, Ibrahim [2 ]
Majeed, Sayf A. [3 ]
Memon, Zubair Ahmed [4 ]
Bilema, Munder [5 ]
Omar, Hend Ali [6 ]
Abdolrasol, Maher G. M. [7 ]
Usman, Aliyu [8 ]
Yusoff, Nur Izzi Md [2 ]
机构
[1] Univ Nizwa, Coll Engn & Architecture, Dept Civil & Environm Engn, Birkat Al Mouz 616, Nizwa, Oman
[2] Univ Kebangsaan Malaysia, Dept Civil Engn, Bangi 43600, Selangor, Malaysia
[3] Al Hadbaa Univ Coll, Tech Comp Engn Dept, Mosul 41002, Iraq
[4] Prince Sultan Univ PSU, Dept Engn Management, Riyadh 12435, Saudi Arabia
[5] Univ Tun Hussein Onn Malaysia, Fac Civil Engn & Built Environm, Batu Pahat 86400, Johor, Malaysia
[6] Univ Tripoli, Dept Civil Engn, Tripoli, Libya
[7] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Dept Elect Elect & Syst Engn, Bangi 43600, Malaysia
[8] Univ Teknol PETRONAS, Dept Civil & Environm Engn, Bandar Seri Iskandar 32610, Perak, Malaysia
来源
IEEE ACCESS | 2021年 / 9卷
关键词
Temperature measurement; Asphalt; Predictive models; Temperature distribution; Atmospheric modeling; Mathematical models; Prediction algorithms; Geophysical monitoring; hybridisation algorithms; machine learning; measurement; pavement temperature profile; PERFORMANCE; DAMAGE; MCMC;
D O I
10.1109/ACCESS.2021.3129979
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Machine learning (ML) models are excellent alternative solutions to model complex engineering issues with high reliability and accuracy. This paper presents two extensively explored ensemble models for predicting asphalt pavement temperature, the Markov chain Monte Carlo (MCMC) and random forest (RF). The RF and multiple MCMC (RF-MCMC) were used to hybridise the proposed algorithms for the optimal prediction of asphalt pavement temperature. This study used thermal instruments to measure the asphalt pavement temperature in Gaza Strip, Palestine. The temperature measurements were made at a two-hour interval from March 2012 to February 2013. The temperature data was used to model the pavement temperature. More than 7200 measured pavement temperatures were used to train and validate the proposed models. The validation showed that the ML models are satisfactory. The modelling results ensured the value of the proposed hybridisation models in predicting the asphalt pavement temperature levels. The developed hybrid algorithms regression model achieved acceptable and better prediction results with a coefficient of determination (R-2) of 0.96. Generally, the results confirmed the significance of the proposed hybrid model as a reliable alternative computer-aided model for predicting asphalt pavement temperature.
引用
收藏
页码:158041 / 158056
页数:16
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