Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement

Potential damage, eventually demonstrated as moisture damage on inner and in-situ road structures, is the most complex problem to predict, which costs lots of money, time, and natural resources for maintenance and even leads to safety problems. Traditional linear regression analysis cannot fit well...

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Autores principales:	Xiaogang Guo, Peiwen Hao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	moisture damage random forest machine learning factor importance prediction Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/bfdfe0fc29f1423283ee1341368207f5
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spelling	oai:doaj.org-article:bfdfe0fc29f1423283ee1341368207f52021-11-11T15:23:58ZUsing a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement10.3390/app1121103962076-3417https://doaj.org/article/bfdfe0fc29f1423283ee1341368207f52021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10396https://doaj.org/toc/2076-3417Potential damage, eventually demonstrated as moisture damage on inner and in-situ road structures, is the most complex problem to predict, which costs lots of money, time, and natural resources for maintenance and even leads to safety problems. Traditional linear regression analysis cannot fit well with this multi-factor task in such in-field circumstances. Random Forest (RF) is a progressive nonlinear algorithm, which can combine all relative factors to gain accurate prediction and good explanation. In this study, an RF model is constructed for the prediction of potential damage. In addition, relative variable importance is analyzed to obtain the correlations between factors and potential damage separately. The results show that, through the optimization, the model achieved a good average accuracy of 83.33%. Finally, the controlling method for moisture damage is provided by combining the traditional analysis method and the RF model. In a word, RF is a prospective method in predictions and data mining for highway engineering. Trained with effective data, it can be multifunctional and powerful to solve hard problems.Xiaogang GuoPeiwen HaoMDPI AGarticlemoisture damagerandom forestmachine learningfactor importancepredictionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10396, p 10396 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	moisture damage random forest machine learning factor importance prediction Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	moisture damage random forest machine learning factor importance prediction Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Xiaogang Guo Peiwen Hao Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
description	Potential damage, eventually demonstrated as moisture damage on inner and in-situ road structures, is the most complex problem to predict, which costs lots of money, time, and natural resources for maintenance and even leads to safety problems. Traditional linear regression analysis cannot fit well with this multi-factor task in such in-field circumstances. Random Forest (RF) is a progressive nonlinear algorithm, which can combine all relative factors to gain accurate prediction and good explanation. In this study, an RF model is constructed for the prediction of potential damage. In addition, relative variable importance is analyzed to obtain the correlations between factors and potential damage separately. The results show that, through the optimization, the model achieved a good average accuracy of 83.33%. Finally, the controlling method for moisture damage is provided by combining the traditional analysis method and the RF model. In a word, RF is a prospective method in predictions and data mining for highway engineering. Trained with effective data, it can be multifunctional and powerful to solve hard problems.
format	article
author	Xiaogang Guo Peiwen Hao
author_facet	Xiaogang Guo Peiwen Hao
author_sort	Xiaogang Guo
title	Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
title_short	Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
title_full	Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
title_fullStr	Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
title_full_unstemmed	Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
title_sort	using a random forest model to predict the location of potential damage on asphalt pavement
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bfdfe0fc29f1423283ee1341368207f5
work_keys_str_mv	AT xiaogangguo usingarandomforestmodeltopredictthelocationofpotentialdamageonasphaltpavement AT peiwenhao usingarandomforestmodeltopredictthelocationofpotentialdamageonasphaltpavement
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Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement

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