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April 22, 2026Open Access

Prediction Model for the Local Bearing Capacity of Stirrup-Confined Concrete Based on the PSO-BP Neural Network

TMTianming MiaoJilin Jianzhu University JDJunwu DaiChina Earthquake Administration TJTao JiangChina Earthquake Administration

Key Points

This research aims to develop a prediction model for the local bearing capacity of stirrup-confined concrete using an advanced neural network.
Utilized particle swarm optimization and backpropagation neural network (PSO-BPNN) for analysis.
Combined test data from 40 specimens with 92 additional data points from literature.
Compared PSO-BPNN performance against other models like Random Forest Regression and Support Vector Regression.
PSO-BPNN reduced training iterations by 74.23% with comparable training effectiveness.
Mean squared error decreased by 33.9% while R2 increased by 5.5%.
PSO-BPNN outperformed other regression models in terms of stability and accuracy.

Abstract

The calculation for the local bearing capacity of stirrup-confined concrete is an important issue in structural design. Due to the coupling effects of multiple factors, there is no unified calculation method recognized by scholars. The improved backpropagation neural network model based on the particle swarm optimization algorithm (PSO-BPNN) is used in this research to conduct a systematic analysis. The results of 40 stirrup-confined concrete specimens from the tests conducted by ourselves and an additional 92 similar test data points from references were combined; the calculation efficiency and accuracy of the PSO-BPNN model were verified. Compared with the BPNN model, the training iterations of the PSO-BPNN model were reduced by 74.23% with the condition of same training effect. The mean squared error (MSE) is reduced by 33.9%, and the coefficient of determination (R2) is increased by 5.5% with the condition of the same number training iterations. In addition, compared with the calculation stability and accuracy of Random Forest Regression (RFR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) models, the PSO-BPNN model also shows better results. Within the applicable range of the codes, the average ratio of the predicted values to the calculated values for GB50010-2010, MC2020 and ACI318-25 are 1.988, 1.719, and 5.387, respectively. A higher evaluation for the contribution of stirrup is considered in the MC2020 code; the predicted values of some specimens are lower than the calculated values when Acor/Al is less than 1.35. The brittleness effect is not adequately considered: the predicted values of some specimens are also lower than the calculated values with the active powder concrete (RPC) is used. The sensitivity ranking of the model with coupling effect for parameters is Al, Ab, fc,k, s, d, dcor, and fy,k. It is slightly different from the sensitivity ranking obtained by analyzing individual parameters, but the calculation logic is consistent. The research results can provide a theoretical basis for practical engineering.

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