A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural Information
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 (she...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/3/1243 |
| _version_ | 1797319011704242176 |
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| author | Subin Kim Heejin Hwang Keunyeong Oh Jiuk Shin |
| author_facet | Subin Kim Heejin Hwang Keunyeong Oh Jiuk Shin |
| author_sort | Subin Kim |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-08T04:00:39Z |
| format | Article |
| id | doaj.art-8805c26fa9684fc3b8c913868b9b1c82 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T04:00:39Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8805c26fa9684fc3b8c913868b9b1c822024-02-09T15:08:22ZengMDPI AGApplied Sciences2076-34172024-02-01143124310.3390/app14031243A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural InformationSubin Kim0Heejin Hwang1Keunyeong Oh2Jiuk Shin3Department of Architectural Engineering, Gyeongsang National University (GNU), Jinju-daero, Jinju-si 52828, Gyeongsangnam-do, Republic of KoreaDepartment of Architectural Engineering, Gyeongsang National University (GNU), Jinju-daero, Jinju-si 52828, Gyeongsangnam-do, Republic of KoreaDepartment of Building Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero, Goyang-si 10223, Gyeonggi-do, Republic of KoreaDepartment of Architectural Engineering, Gyeongsang National University (GNU), Jinju-daero, Jinju-si 52828, Gyeongsangnam-do, Republic of KoreaThe 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).https://www.mdpi.com/2076-3417/14/3/1243reinforced concrete columnsmachine learningcolumn failure modesclassification modelsimple column details |
| spellingShingle | Subin Kim Heejin Hwang Keunyeong Oh Jiuk Shin A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural Information Applied Sciences reinforced concrete columns machine learning column failure modes classification model simple column details |
| title | A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural Information |
| title_full | A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural Information |
| title_fullStr | A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural Information |
| title_full_unstemmed | A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural Information |
| title_short | A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural Information |
| title_sort | machine learning based failure mode classification model for reinforced concrete columns using simple structural information |
| topic | reinforced concrete columns machine learning column failure modes classification model simple column details |
| url | https://www.mdpi.com/2076-3417/14/3/1243 |
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