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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Autores principales:	Abdalrhman Abrahim Milad, Ibrahim Adwan, Sayf A. Majeed, Zubair Ahmed Memon, Munder Bilema, Hend Ali Omar, Maher G. M. Abdolrasol, Aliyu Usman, Nur Izzi Md Yusoff
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Geophysical monitoring hybridisation algorithms machine learning measurement pavement temperature profile Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/de693643f8ab4c7680d51218dff2d2d2
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id	oai:doaj.org-article:de693643f8ab4c7680d51218dff2d2d2
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spelling	oai:doaj.org-article:de693643f8ab4c7680d51218dff2d2d22021-12-03T00:01:16ZDevelopment of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction2169-353610.1109/ACCESS.2021.3129979https://doaj.org/article/de693643f8ab4c7680d51218dff2d2d22021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9623533/https://doaj.org/toc/2169-3536Machine 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<sup>2</sup>) 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.Abdalrhman Abrahim MiladIbrahim AdwanSayf A. MajeedZubair Ahmed MemonMunder BilemaHend Ali OmarMaher G. M. AbdolrasolAliyu UsmanNur Izzi Md YusoffIEEEarticleGeophysical monitoringhybridisation algorithmsmachine learningmeasurementpavement temperature profileElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 158041-158056 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Geophysical monitoring hybridisation algorithms machine learning measurement pavement temperature profile Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Geophysical monitoring hybridisation algorithms machine learning measurement pavement temperature profile Electrical engineering. Electronics. Nuclear engineering TK1-9971 Abdalrhman Abrahim Milad Ibrahim Adwan Sayf A. Majeed Zubair Ahmed Memon Munder Bilema Hend Ali Omar Maher G. M. Abdolrasol Aliyu Usman Nur Izzi Md Yusoff Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction
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 (R<sup>2</sup>) 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.
format	article
author	Abdalrhman Abrahim Milad Ibrahim Adwan Sayf A. Majeed Zubair Ahmed Memon Munder Bilema Hend Ali Omar Maher G. M. Abdolrasol Aliyu Usman Nur Izzi Md Yusoff
author_facet	Abdalrhman Abrahim Milad Ibrahim Adwan Sayf A. Majeed Zubair Ahmed Memon Munder Bilema Hend Ali Omar Maher G. M. Abdolrasol Aliyu Usman Nur Izzi Md Yusoff
author_sort	Abdalrhman Abrahim Milad
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	IEEE
publishDate	2021
url	https://doaj.org/article/de693643f8ab4c7680d51218dff2d2d2
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_version_	1718374019018784768

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

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