Failure Mode Identification and Shear Strength Prediction of Rectangular Hollow RC Columns Using Novel Hybrid Machine Learning Models

Abstract

Failure mode identification and shear strength prediction are critical issues in designing reinforced concrete (RC) structures. Nevertheless, specific guidelines for identifying the failure modes and for accurate predictions of the shear strength of rectangular hollow RC columns are not provided in design codes. This study develops hybrid machine learning (ML) models to accurately identify the failure modes and precisely predict the shear strength of rectangular hollow RC columns. For this purpose, 121 experimental results of such columns are collected from the literature. Eight widely used ML models are employed to identify the failure modes and predict the shear strength of the column. The moth-flame optimization (MFO) algorithm and five-fold cross-validation are utilized to fine-tune the hyperparameters of the ML models. Additionally, seven empirical formulas are adopted to evaluate the performance of regression ML models in predicting the shear strength. The results reveal that the hybrid MFO-extreme gradient boosting (XGB) model outperforms others in both classifying the failure modes (accuracy of 93%) and predicting the shear strength (R2 = 0.996) of hollow RC columns. Additionally, the results indicate that the MFO-XGB model is more accurate than the empirical models for shear strength prediction. Moreover, the effect of input parameters on the failure modes and shear strength is investigated using the Shapley Additive exPlanations method. Finally, an efficient web application is developed for users who want to use the results of this study or update a new dataset.

Imperfection Sensitivity Detection in Pultruded Columns Using Machine Learning and Synthetic Data

“…ML is widely used in a variety of fields from everyday applications like image recognition [16] to engineering applications such as microstructural characterization and prediction of mechanical response of crystalline materials [17,18]. Furthermore, ML has been used for failure mode identification and strength prediction in columns [19][20][21][22].…”

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

“…Methods based on CatBoost, K-nearest neighbors and SVR using their own accumulated empirical base showed excellent results when the error was between 6.15-7.89%. In an article [31], a hybrid machine learning model was developed to identify failure modes more accurately and predict the shear strength of rectangular hollow reinforced concrete columns. This was achieved by utilizing moth-flame optimization (MFO) and implementing a five-fold cross-validation approach to fine-tune the hyperparameters.…”