| Popis: |
Deterioration components (DCs) of reinforced columns (RC) are important for predication the seismic behavior and performance of RC structures. Theses DCs parameters include: Plastic chord rotation from yield to cap (θp), post capping plastic rotation capacity from the cap to point of zero strength (θpc) and normalized energy dissipation capacity relation between deterioration components of RC columns with different properties(λ). This paper investigates several machine learning (ML) algorithms for the prediction of DCs, referred to as ML-DCs, based on the results of 255 experimental reinforced concrete columns tests conducted from 1973 to 2002. The performance of the models are considered using regression metrics. In this regard, machine learning algorithms such as Least Squares Support Vector Machine (Lssvm), AdaBoost, Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR) and XGBoost are applied and finally the results obtained from the models are compared with experimental relationships. The XGBoost algorithm provides enhanced accuracy of 95% for θp, 84% for θpc, and 93% for λ comparing to the others. Also, the results of machine learning algorithms indicate that the results obtained from the machine learning models are more effective than the empirical relationships achieved by the test results. |
