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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Institute of Electrical and Electronics Engineers Inc.
2021
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my.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/ |
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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. |
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| 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_facet |
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. |
| 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/ |
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13.211869 |