Enhancing Cold Joint Shear Strength Prediction in Concrete Structures: Novel Approach with Ensemble Spiking Neural Networks

Abstract

Cold joints often appear in precast structures, bridges, and retrofitted buildings, where concrete parts cast at different times meet. The potential of these cold joints to transfer shear stresses between concrete interfaces severely affects the overall structural integrity. Therefore, when developing or evaluating precast and retrofitted structures, it is crucial to comprehend the shear force transfer capability of cold joints. This research explores the application of ensemble spiking neural network models for predicting interface shear strength in concrete structures, a crucial parameter in civil engineering. The study utilizes a database of 217 cold joints, categorized by surface type (smooth or roughened), and employs a range of input parameters, including concrete strength, reinforcement characteristics, and interface dimensions, among others. Three ensemble learning techniques, namely, model averaging, separated stacking, integrated stacking, and local cascade ensemble, are employed, with spiking neural networks serving as base learners. The proposed models are compared with established machine learning algorithms, including eXtreme gradient boosting, gradient boosting, random forests, AdaBoost, and bagging. Results indicate that the stacked separate models with the bagging regressor algorithm outperforms other models, achieving the lowest RMSE, competitive mean absolute error, and a high R2 score on the testing set.