This study introduces a novel machine learning framework, integrating domain knowledge, to accurately predict the bearing capacity of CFSTs, bridging the gap between traditional engineering and machine learning techniques. Utilizing a comprehensive database of 2621 experimental data points on CFSTs, we developed a Domain Knowledge Enhanced Neural Network (DKNN) model. This model incorporates advanced feature engineering techniques, including Pearson correlation, XGBoost, and Random tree algorithms. The DKNN model demonstrated a marked improvement in prediction accuracy, with a Mean Absolute Percentage Error (MAPE) reduction of over 50% compared to existing models. Its robustness was confirmed through extensive performance assessments, maintaining high accuracy even in noisy environments. Furthermore, sensitivity and SHAP analysis were conducted to assess the contribution of each effective parameter to axial load capacity and propose design recommendations for the diameter of cross-section, material strength range and material combination. This research advances CFST predictive modelling, showcasing the potential of integrating machine learning with domain expertise in structural engineering. The DKNN model sets a new benchmark for accuracy and reliability in the field.

翻译：本研究提出了一种融合领域知识的新型机器学习框架，以精确预测钢管混凝土柱（CFST）的承载力，填补了传统工程与机器学习技术之间的鸿沟。基于包含2621个CFST实验数据点的综合数据库，我们开发了领域知识增强神经网络（DKNN）模型。该模型整合了先进的特征工程技术，包括皮尔逊相关性、XGBoost和随机树算法。DKNN模型在预测精度上实现了显著提升，其平均绝对百分比误差（MAPE）相比现有模型降低了50%以上。通过广泛的性能评估验证了其鲁棒性，即使在噪声环境下仍能保持高精度。此外，通过敏感性和SHAP分析，评估了各有效参数对轴向荷载承载力的贡献，并提出了关于截面直径、材料强度范围和材料组合的设计建议。本研究推动了CFST预测建模的发展，展示了将机器学习与结构工程领域专业知识相结合的潜力。DKNN模型为该领域的准确性和可靠性设立了新基准。