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（CFST）柱的承载能力，弥合传统工程方法与机器学习技术之间的差距。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，该模型融合了皮尔逊相关系数、XGBoost和随机树算法等先进特征工程技术。与现有模型相比，DKNN模型在预测精度上实现显著提升，平均绝对百分比误差（MAPE）降低超过50%。通过全面的性能评估验证了其鲁棒性，即使在噪声环境下仍能保持较高精度。进一步开展了敏感性和SHAP分析，评估各有效参数对轴向承载力的贡献，并针对截面直径、材料强度范围及材料组合提出设计建议。该研究推进了CFST预测建模的发展，展示了结构工程中机器学习与领域知识融合的潜力。DKNN模型为该领域的准确性和可靠性确立了新的基准。