经验知识监督的RC墩柱力学性能神经网络分析方法OACSTPCD

The mechanical performance analysis of reinforced concrete(RC)columns using only experimental or numerical methods usually faces challenges in balancing computational accuracy and efficiency,while purely data-driven methods suffered from poor interpretability and over-de-pendence on available data samples.To address this issue,an empirical knowledge guided neural network(KGNN)-based RC column analysis by investigating the fusion mechanism of empirical knowledge,test data and machine learning methods.A test database is firstly built based on 761 quasi-static test specimens.In succession,the influence rules of primary characteristics of RC columns on their mechanical properties are ana-lyzed based on empirical knowledge to formulate mathematical representations.Finally,the test data and empirical knowledge were implemented into the artificial neural network to develop high performance,explainable,generalizable KGNN model with only minor training samples.The result comparisons of the proposed KGNN method and the pure data-driven neural network(BPNN)demonstrate that although the BPNN slightly over the KGNN in terms of the load-carrying capacity prediction accuracy,with mean square error and correlation coefficient of 0.070 and 0.978 comparing to 0.108 and 0.942 of the KGNN.However,the results of the BPNN are not consistent with the empirical knowledge and further causes overfitting problem since it fails to capture the relationship between the characteristics and mechanical properties of RC columns.Fortu-nately,the KGNN method can not only quickly and accurately provide the mechanical properties of RC columns,but also present a higher con-sistency with the empirical knowledge with greater reliability and practicality.Through this work,the neural network-based methods integrating experimental data and empirical knowledge are expected to provide a novel analysis approach for RC structures.