A Machine Learning Framework for Predicting Bridge Defect Detection Cost

Evaluating the cost of detecting bridge defects is a difficult task, but one that is vital to the lifecycle cost analysis of bridges. In this study, a detection cost sample database was established based on practical engineering data, and a bridge defect detection cost prediction model and software...

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Autores principales:	Chongjiao Wang, Changrong Yao, Bin Qiang, Siguang Zhao, Yadong Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	bridge engineering life cycle detection cost machine learning intelligent prediction model Technology T
Acceso en línea:	https://doaj.org/article/8eba36e2eb704eeda6f96e90cfe28b7b
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id	oai:doaj.org-article:8eba36e2eb704eeda6f96e90cfe28b7b
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spelling	oai:doaj.org-article:8eba36e2eb704eeda6f96e90cfe28b7b2021-11-25T17:58:49ZA Machine Learning Framework for Predicting Bridge Defect Detection Cost10.3390/infrastructures61101522412-3811https://doaj.org/article/8eba36e2eb704eeda6f96e90cfe28b7b2021-10-01T00:00:00Zhttps://www.mdpi.com/2412-3811/6/11/152https://doaj.org/toc/2412-3811Evaluating the cost of detecting bridge defects is a difficult task, but one that is vital to the lifecycle cost analysis of bridges. In this study, a detection cost sample database was established based on practical engineering data, and a bridge defect detection cost prediction model and software were developed using machine learning. First, the random forest method was adopted to evaluate the importance of the seven main factors affecting the detection cost. The most important indicators were selected, and the recent GDP growth rate was employed to account for the impact of social and economic developments on the detection cost. Combining a genetic algorithm with a multilayer neural network, a detection cost prediction model was established. The predictions given by this model were found to have an average relative error of 3.41%. Finally, an intelligent prediction software for bridge defect detection costs was established, providing a reliable reference for bridge lifecycle cost analysis and the evaluation of defect detection costs during the operation period.Chongjiao WangChangrong YaoBin QiangSiguang ZhaoYadong LiMDPI AGarticlebridge engineeringlife cycle detection costmachine learningintelligent prediction modelTechnologyTENInfrastructures, Vol 6, Iss 152, p 152 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bridge engineering life cycle detection cost machine learning intelligent prediction model Technology T
spellingShingle	bridge engineering life cycle detection cost machine learning intelligent prediction model Technology T Chongjiao Wang Changrong Yao Bin Qiang Siguang Zhao Yadong Li A Machine Learning Framework for Predicting Bridge Defect Detection Cost
description	Evaluating the cost of detecting bridge defects is a difficult task, but one that is vital to the lifecycle cost analysis of bridges. In this study, a detection cost sample database was established based on practical engineering data, and a bridge defect detection cost prediction model and software were developed using machine learning. First, the random forest method was adopted to evaluate the importance of the seven main factors affecting the detection cost. The most important indicators were selected, and the recent GDP growth rate was employed to account for the impact of social and economic developments on the detection cost. Combining a genetic algorithm with a multilayer neural network, a detection cost prediction model was established. The predictions given by this model were found to have an average relative error of 3.41%. Finally, an intelligent prediction software for bridge defect detection costs was established, providing a reliable reference for bridge lifecycle cost analysis and the evaluation of defect detection costs during the operation period.
format	article
author	Chongjiao Wang Changrong Yao Bin Qiang Siguang Zhao Yadong Li
author_facet	Chongjiao Wang Changrong Yao Bin Qiang Siguang Zhao Yadong Li
author_sort	Chongjiao Wang
title	A Machine Learning Framework for Predicting Bridge Defect Detection Cost
title_short	A Machine Learning Framework for Predicting Bridge Defect Detection Cost
title_full	A Machine Learning Framework for Predicting Bridge Defect Detection Cost
title_fullStr	A Machine Learning Framework for Predicting Bridge Defect Detection Cost
title_full_unstemmed	A Machine Learning Framework for Predicting Bridge Defect Detection Cost
title_sort	machine learning framework for predicting bridge defect detection cost
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8eba36e2eb704eeda6f96e90cfe28b7b
work_keys_str_mv	AT chongjiaowang amachinelearningframeworkforpredictingbridgedefectdetectioncost AT changrongyao amachinelearningframeworkforpredictingbridgedefectdetectioncost AT binqiang amachinelearningframeworkforpredictingbridgedefectdetectioncost AT siguangzhao amachinelearningframeworkforpredictingbridgedefectdetectioncost AT yadongli amachinelearningframeworkforpredictingbridgedefectdetectioncost AT chongjiaowang machinelearningframeworkforpredictingbridgedefectdetectioncost AT changrongyao machinelearningframeworkforpredictingbridgedefectdetectioncost AT binqiang machinelearningframeworkforpredictingbridgedefectdetectioncost AT siguangzhao machinelearningframeworkforpredictingbridgedefectdetectioncost AT yadongli machinelearningframeworkforpredictingbridgedefectdetectioncost
_version_	1718411757722009600

A Machine Learning Framework for Predicting Bridge Defect Detection Cost

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