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

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...

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Main Authors: Milad, A.A., Adwan, I., Majeed, S.A., Memon, Z.A., Bilema, M., Omar, H.A., Abdolrasol, M.G.M., Usman, A., Yusoff, N.I.M.
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Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29371 /
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120050406&doi=10.1109%2fACCESS.2021.3129979&partnerID=40&md5=234343c6590d335875edd8bcad344ef6
http://eprints.utp.edu.my/29371/
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spelling utp-eprints.293712022-03-25T01:36:13Z Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction Milad, A.A. Adwan, I. Majeed, S.A. Memon, Z.A. Bilema, M. Omar, H.A. Abdolrasol, M.G.M. Usman, A. Yusoff, N.I.M. 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 (R2) 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. © 2013 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120050406&doi=10.1109%2fACCESS.2021.3129979&partnerID=40&md5=234343c6590d335875edd8bcad344ef6 Milad, A.A. and Adwan, I. and Majeed, S.A. and Memon, Z.A. and Bilema, M. and Omar, H.A. and Abdolrasol, M.G.M. and Usman, A. and Yusoff, N.I.M. (2021) Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction. IEEE Access, 9 . pp. 158041-158056. http://eprints.utp.edu.my/29371/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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 (R2) 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. © 2013 IEEE.
format Article
author Milad, A.A.
Adwan, I.
Majeed, S.A.
Memon, Z.A.
Bilema, M.
Omar, H.A.
Abdolrasol, M.G.M.
Usman, A.
Yusoff, N.I.M.
spellingShingle Milad, A.A.
Adwan, I.
Majeed, S.A.
Memon, Z.A.
Bilema, M.
Omar, H.A.
Abdolrasol, M.G.M.
Usman, A.
Yusoff, N.I.M.
Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction
author_sort Milad, A.A.
title Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction
title_short Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction
title_full Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction
title_fullStr Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction
title_full_unstemmed Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction
title_sort development of a hybrid machine learning model for asphalt pavement temperature prediction
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120050406&doi=10.1109%2fACCESS.2021.3129979&partnerID=40&md5=234343c6590d335875edd8bcad344ef6
http://eprints.utp.edu.my/29371/
_version_ 1741197231464120320
score 11.62408

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