Identification of historical maximum load on concrete beams based on crack and deformation response using knowledge-embedded machine learning algorithms

Abstract

This study introduces an innovative method for identifying the historical maximum load on concrete beams, utilizing a multi-layer back propagation (BP) neural network model with physically interpretable features as inputs. The method establishes a mapping relationship among four-dimensional features (boundary constraints, system parameters, cracks, and deformations) and the maximum load in the load history, using incremental load action and structural response. Moreover, a hybrid optimization algorithm, combining Hunter Prey Optimization (HPO) and Particle Swarm Optimization (PSO), is introduced to optimize the model's parameters. The method is compared with commonly used machine learning models, demonstrating its accuracy and reliability. Additionally, the study performs a sensitivity analysis of the input features using the proposed intelligent identification model. The results reveal that the proposed intelligent identification model exhibits small prediction errors on both the training and testing sets, with average absolute percentage errors of 16.46 % and 19.56 %, and determination coefficients of 0.922 and 0.8185, respectively, showcasing its strong robustness and generalization capability. Compared to the non-optimized multi-layer BP neural network model, the proposed model achieves a reduction in average absolute percentage errors of 39.59 % and 48.33 % on the training and testing sets, respectively.