To investigate the local compressive bearing capacity of concrete reinforced with spiral stirrups, a study was conducted on 40 specimens, focusing on the analysis of failure modes and strain variations. The study investigated the effects of concrete strength grade fcg, stirrup diameter d, volumetric ratio of the spiral stirrups ρv, and ratio of core area to load area Acor/Al on the local bearing capacity Fl. The experimental results indicated that the local bearing capacity Fl increased as the ratio of core area to load area Acor/Al decreased, with an observed increase of 23.0%. Furthermore, the local bearing capacity Fl increased with a reduction in stirrup spacing s, showing a 65.4% increase compared to the initial value. An increase in stirrup diameter d also would contribute to a higher Fl, with an improvement of 24.7%. Additionally, the local bearing capacity Fl exhibited a directly proportional relationship with the concrete strength grade fcg. Based on the theoretical analyses, the local bearing capacity of calculation equation Nu′ was established with the reinforcement term ρvfyAl and the concrete term fcAl. The experimental data on concrete reinforced with spiral stirrups were collected to verify the accuracy of calculation equation. The results show that there was a high calculation accuracy for the mechanical model and could provide a reference for local bearing capacity calculation.

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 A_cor/A_l 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 A_l, A_b, f_c,k, s, d, d_cor, and f_y,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.