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A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural Informationopen access

Authors
Kim, SubinHwang, HeejinOh, KeunyeongShin, Jiuk
Issue Date
Feb-2024
Publisher
MDPI
Keywords
reinforced concrete columns; machine learning; column failure modes; classification model; simple column details
Citation
APPLIED SCIENCES-BASEL, v.14, no.3
Indexed
SCIE
Journal Title
APPLIED SCIENCES-BASEL
Volume
14
Number
3
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/69734
DOI
10.3390/app14031243
ISSN
2076-3417
Abstract
The seismically deficient column details in existing reinforced concrete buildings affect the overall behavior of the building depending on the failure type of the column. The purpose of this study is to develop and validate a machine-learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using the concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating the accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model has the highest average value for the classification model performance measurements among the considered learning methods and can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with the simple column details. Additionally, it was demonstrated that the predicted failure modes from the selected model were exactly same as the failure mode determined from a code-defined equation (traditional method).
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공과대학 (건축공학부)
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