Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning

In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated...

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Main Authors:	Reventheran Ganasan, Chee Ghuan Tan, Zainah Ibrahim, Fadzli Mohamed Nazri, Muhammad M. Sherif, Ahmed El-Shafie
Format:	Article
Language:	English
Published:	MDPI AG 2021-08-01
Series:	Applied Sciences
Subjects:	crack widths RC beam-column joint drift ratio machine learning prediction models
Online Access:	https://www.mdpi.com/2076-3417/11/16/7700

id	doaj-df305cec1eed4c0397f2ead3408269c9
record_format	Article
spelling	doaj-df305cec1eed4c0397f2ead3408269c92021-08-26T13:31:03ZengMDPI AGApplied Sciences2076-34172021-08-01117700770010.3390/app11167700Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine LearningReventheran Ganasan0Chee Ghuan Tan1Zainah Ibrahim2Fadzli Mohamed Nazri3Muhammad M. Sherif4Ahmed El-Shafie5Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, MalaysiaSchool of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, MalaysiaDepartment of Civil, Construction and Environmental Engineering, School of Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USADepartment of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, MalaysiaIn recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of members in a structure subjected to hazard loads, due to earthquake and wind. This research develops models to predict the evolution of the cracks in the RC beam-column joint (BCJ) region. The RC beam-column joint is subjected to lateral cyclic loading. Four machine learning models are developed using Rapidminer to predict the crack width experienced by seven RC beam-column joints. The design parameters associated with RC beam-column joints and lateral cyclic loadings in terms of drift ratio are used as inputs. Several prediction models are developed, and the highest performing neural networks are selected, refined, and optimized using the various split data ratios, number of inputs, and performance indices. The error in predicting the experimental crack width is used as a performance index.https://www.mdpi.com/2076-3417/11/16/7700crack widthsRC beam-column jointdrift ratiomachine learningprediction models
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Reventheran Ganasan Chee Ghuan Tan Zainah Ibrahim Fadzli Mohamed Nazri Muhammad M. Sherif Ahmed El-Shafie
spellingShingle	Reventheran Ganasan Chee Ghuan Tan Zainah Ibrahim Fadzli Mohamed Nazri Muhammad M. Sherif Ahmed El-Shafie Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning Applied Sciences crack widths RC beam-column joint drift ratio machine learning prediction models
author_facet	Reventheran Ganasan Chee Ghuan Tan Zainah Ibrahim Fadzli Mohamed Nazri Muhammad M. Sherif Ahmed El-Shafie
author_sort	Reventheran Ganasan
title	Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning
title_short	Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning
title_full	Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning
title_fullStr	Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning
title_full_unstemmed	Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning
title_sort	development of crack width prediction models for rc beam-column joint subjected to lateral cyclic loading using machine learning
publisher	MDPI AG
series	Applied Sciences
issn	2076-3417
publishDate	2021-08-01
description	In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of members in a structure subjected to hazard loads, due to earthquake and wind. This research develops models to predict the evolution of the cracks in the RC beam-column joint (BCJ) region. The RC beam-column joint is subjected to lateral cyclic loading. Four machine learning models are developed using Rapidminer to predict the crack width experienced by seven RC beam-column joints. The design parameters associated with RC beam-column joints and lateral cyclic loadings in terms of drift ratio are used as inputs. Several prediction models are developed, and the highest performing neural networks are selected, refined, and optimized using the various split data ratios, number of inputs, and performance indices. The error in predicting the experimental crack width is used as a performance index.
topic	crack widths RC beam-column joint drift ratio machine learning prediction models
url	https://www.mdpi.com/2076-3417/11/16/7700
work_keys_str_mv	AT reventheranganasan developmentofcrackwidthpredictionmodelsforrcbeamcolumnjointsubjectedtolateralcyclicloadingusingmachinelearning AT cheeghuantan developmentofcrackwidthpredictionmodelsforrcbeamcolumnjointsubjectedtolateralcyclicloadingusingmachinelearning AT zainahibrahim developmentofcrackwidthpredictionmodelsforrcbeamcolumnjointsubjectedtolateralcyclicloadingusingmachinelearning AT fadzlimohamednazri developmentofcrackwidthpredictionmodelsforrcbeamcolumnjointsubjectedtolateralcyclicloadingusingmachinelearning AT muhammadmsherif developmentofcrackwidthpredictionmodelsforrcbeamcolumnjointsubjectedtolateralcyclicloadingusingmachinelearning AT ahmedelshafie developmentofcrackwidthpredictionmodelsforrcbeamcolumnjointsubjectedtolateralcyclicloadingusingmachinelearning
_version_	1721194969817939968

Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning

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