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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: Ganasan, Reventheran, Tan, Chee Ghuan, Ibrahim, Zainah, Nazri, Fadzli Mohamed, Sherif, Muhammad M., El-Shafie, Ahmed
Format: Article
Published: MDPI 2021
Subjects:
QC Physics
QD Chemistry
Online Access:http://eprints.um.edu.my/28294/
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spelling my.um.eprints.282942022-03-29T07:56:51Z http://eprints.um.edu.my/28294/ Development of crack width prediction models for RC beam-column joint subjected to lateral cyclic loading using machine learning Ganasan, Reventheran Tan, Chee Ghuan Ibrahim, Zainah Nazri, Fadzli Mohamed Sherif, Muhammad M. El-Shafie, Ahmed QC Physics QD Chemistry 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. MDPI 2021 Article PeerReviewed Ganasan, Reventheran and Tan, Chee Ghuan and Ibrahim, Zainah and Nazri, Fadzli Mohamed and Sherif, Muhammad M. and El-Shafie, Ahmed (2021) Development of crack width prediction models for RC beam-column joint subjected to lateral cyclic loading using machine learning. Applied Sciences-Basel, 11 (16). ISSN 2076-3417, DOI https://doi.org/10.3390/app11167700 <https://doi.org/10.3390/app11167700>. 10.3390/app11167700
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QC Physics
QD Chemistry
spellingShingle QC Physics
QD Chemistry
Ganasan, Reventheran
Tan, Chee Ghuan
Ibrahim, Zainah
Nazri, Fadzli Mohamed
Sherif, Muhammad M.
El-Shafie, Ahmed
Development of crack width prediction models for RC beam-column joint subjected to lateral cyclic loading using machine learning
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.
format Article
author Ganasan, Reventheran
Tan, Chee Ghuan
Ibrahim, Zainah
Nazri, Fadzli Mohamed
Sherif, Muhammad M.
El-Shafie, Ahmed
author_facet Ganasan, Reventheran
Tan, Chee Ghuan
Ibrahim, Zainah
Nazri, Fadzli Mohamed
Sherif, Muhammad M.
El-Shafie, Ahmed
author_sort Ganasan, Reventheran
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
publishDate 2021
url http://eprints.um.edu.my/28294/
_version_ 1735409551481503744
score 13.211869

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