Classification and prediction of deformed steel and concrete bond-slip failure modes based on SSA-ELM model

Abstract

Bond-slip failure modes between reinforcement and concrete can seriously affect the load-bearing performance and safety of reinforced concrete (RC) structures. The pull-out test is widely used to analyze RC bond-slip and failure modes because of its simplicity. Therefore, a prediction model based on Sparrow Search Algorithm (SSA) Optimized Extreme Learning Machine (SSA-ELM) is proposed in this study to quickly and effectively evaluate the failure forms of RC structures. The bond-slip test samples containing 399 sets of deformed bar and concrete deformation pull-out tests were first collected as a database, and the various characteristic parameters were preprocessed. Then, the features significantly influencing the failure pattern were screened out using random forest, and the plausibility tests were performed using the Pearson correlation coefficient and mutual information. Subsequently, a classification prediction model of the pull-out failure pattern was developed based on standard machine learning (ML) algorithms. In addition, nine well-known ML algorithms (LR, K-NN, DT, SVC, BPNN, NB, RF, AdaBoost, and XGBoost) are considered. This model's accuracy and generalization ability are compared based on the Precision and Recall confusion matrix. Finally, the optimized ELM was trained by SSA using the training set data with optimized ELM weights and thresholds. The results showed that the concrete protective layer thickness ratio to reinforcement diameter had the highest sensitivity to the failure pattern of RC-drawn specimens. The accuracy of the prediction results of the SSA-ELM model was better than other ML algorithms (Accuracy = 95.8%), such as BP Neural Network. In addition, SSA has better stability and higher accuracy than Gray Wolf Optimization (GWO) Algorithm, Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and other ML classification algorithms.