Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning
| Autor: | Ahmed El-Shafie, Muhammad Sherif, Fadzli Mohamed Nazri, REVENTHERAN GANASAN, ZAINAH IBRAHIM, Salmia Beddu, Chee Ghuan Tan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Hazard (logic)
Technology QH301-705.5 QC1-999 drift ratio crack widths Machine learning computer.software_genre General Materials Science Biology (General) QD1-999 Instrumentation Joint (geology) Mathematics Fluid Flow and Transfer Processes Artificial neural network business.industry Physics Process Chemistry and Technology General Engineering prediction models Fracture mechanics Engineering (General). Civil engineering (General) Finite element method Computer Science Applications Chemistry machine learning Development (differential geometry) Artificial intelligence TA1-2040 Focus (optics) business computer RC beam-column joint Predictive modelling |
| Zdroj: | Applied Sciences Volume 11 Issue 16 Applied Sciences, Vol 11, Iss 7700, p 7700 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11167700 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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