使用SSA和PSO优化机器学习,用于杆螺栓-杆键强度预测

Detan Liu^1,2, Chenglin Liu³, Hongwei Zhang⁴

¹Datang Hydropower Science & Technology Research Institute Co., Ltd., Chengdu 610083, China.

Materials (Basel, Switzerland)

|March 14, 2026

概括

机器学习准确地预测螺栓结合强度,这对于工程能力和耐用性至关重要. 优化模型,特别是PSO-LSBoost,显著优于传统方法和经验公式,提高结构可靠性.

科学领域:

土木工程土木工程是指土木工程.
材料科学材料科学材料科学
计算力学计算力学计算力学

背景情况:

螺栓结合强度 (τ) 对于座的工程能力和耐用性至关重要,特别是在铁杆腐蚀时.
传统的试验测量 τ 是复杂的,耗时的,劳动密集的.
开发高效准确的预测方法对t对于可靠的结构评估至关重要.

研究的目的:

开发一个通用的机器学习模型来预测螺栓结合强度 (τ).
使用元启发算法优化机器学习模型,以提高准确性和减少过度拟合.
将优化模型的性能与实证公式进行比较,并分析特征的重要性.

主要方法:

利用了来自429个钢筋混凝土样本的拉出试验数据.
使用Sparrow搜索算法 (SSA) 和粒子群优化 (PSO) 开发和优化了随机森林 (RF),最小平方提升 (LSBoost) 和通用添加模型 (GAM).
进行比较错误分析和SHAP分析,以评估模型性能和特征贡献.

主要成果:

未经优化的模型显示准确度低,并过度拟合.
优化的模型,特别是PSO-LSBoost,显示出明显改善的预测准确性 (R2 = 0.93),并减少了过拟合.
PSO-LSBoost模型对 τ 的预测大大超过了三种经验公式的预测.
SHAP分析发现腐蚀率 (Cw) 是最有影响的因素,杆类型 (ST) 是影响最小的因素.

结论:

机器学习,优化与SSA和PSO,提供了一个新的和高效的方法来预测固键强度.
与传统方法和经验公式相比,PSO-LSBoost模型提供了更高的准确性和概括能力.
这种方法通过改善承载能力和螺栓耐久性的评估来提高固结构的可靠性.

关键词:

在SHAP分析中,我们分析了SHAP.腐蚀的杆螺栓被腐蚀了接口的强度纽带强度接口的纽带强度机器学习是机器学习.粒子群优化算法粒子群优化算法子搜索算法子搜索算法

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