Axial Compression Prediction and GUI Design for CCFST Column Using Machine Learning and Shapley Additive Explanation

Abstract

Axial bearing capacity is the key index of circular concrete-filled steel tubes (CCFST). A hybrid PSO-ANN model consisting of an artificial neural network (ANN) optimized with particle swarm algorithm (PSO) was proposed to reliably and accurately predict the axial bearing capacity in this paper. The predictive performance of the model was evaluated and compared with the EC4 code and original ANN based on a dataset of 227 experiments, and a graphical user interface (GUI) was developed to achieve the automatic output of the results. The influence of each design parameter on the bearing capacity was analyzed and quantified using the Shapley additive explanation (SHAP) method and sensitivity analysis. The results show that the prediction performance of the PSO-ANN model is superior, and can be recommended as a candidate for the prediction of axial compression bearing capacity of the CCFST column in terms of performance indices. Shapley additive explanation-based parameter analysis indicated that the diameter and thickness of the steel tube are the most two important parameters to the bearing capacity; in particular, the fluctuation of the diameter under the stochastic environment leads to the variation of the axial compression bearing capacity beyond the diameter itself.

Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns

“…ML approaches are methods that construct complicated mathematical models with great precision to reflect the connection between the input and output parameters of a given data set. Based on this perspective, numerous scientists currently utilize ML to identify the structures' behavior [32][33][34][35][36][37][38]. The application of ML to forecast the ALC of circular CFST columns has also been the subject of substantial research [31][32][33][34][35].…”

“…Based on this perspective, numerous scientists currently utilize ML to identify the structures' behavior [32][33][34][35][36][37][38]. The application of ML to forecast the ALC of circular CFST columns has also been the subject of substantial research [31][32][33][34][35]. Specifically, Ahmadi et…”

Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns

“…ML approaches are methods that construct complicated mathematical models with great precision to reflect the connection between the input and output parameters of a given data set. Based on this perspective, numerous scientists currently utilize ML to identify the structures' behavior [32][33][34][35][36][37][38]. The application of ML to forecast the ALC of circular CFST columns has also been the subject of substantial research [31][32][33][34][35].…”

“…Based on this perspective, numerous scientists currently utilize ML to identify the structures' behavior [32][33][34][35][36][37][38]. The application of ML to forecast the ALC of circular CFST columns has also been the subject of substantial research [31][32][33][34][35]. Specifically, Ahmadi et…”

Analysis of Non-Destructive Indicating Properties for Predicting Compressive Strengths of Dendrocalamus sericeus Munro Bamboo Culms

“…Statistical analysis was used to determine the relationship between the non-destructive test properties and axial load strength, which are statistically called independent and dependent variables, respectively. First, their relationships were obtained based on simple linear regression analysis (SLRA), which is easily applicable and has been widely used [ 35 ]. However, the analysis is limited to a single independent variable relationship, which generally provides a lower correlation (R 2 value).…”