首页|基于机器学习的直角扣件滑移和扭转性能预测方法

基于机器学习的直角扣件滑移和扭转性能预测方法

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直角扣件初始刚度对模板支架结构承载力有较大影响，但扣件性能的量化设计存在工程量大、计算精确度低等问题。基于一系列扣件抗滑移和扭转试验，建立带直角扣件节点的三维数值模拟并利用试验结果进行验证，通过验证后的数值方法开展大量参数分析揭示多种设计参数对扣件性能的综合影响并建立扣件承载力设计数据库，分别基于随机森林(RF)、支持向量机(SVM)和K最邻近算法(K-NN)提出了扣件刚度预测模型，结合基因表达式编程(GEP)提出了抗滑移模型测点位移和扭转模型刚度预测表达式。研究表明，通过SVM和GEP能够较准确预测直角扣件抗滑移模型位移和扭转模型刚度，对指导工程模板支架结构中扣件的安全设计有着重要意义。
Prediction of Slip and Torsion Performance of Right-Angle Fasteners Based on Machine Learning Methods
Aiming at the issue of large CPU costs and low calculation accuracy in the design of right-angle fasteners in scaffolding structures,prediction models of fastener anti-slip performance and torsion performance based on machine learning are proposed.A three-dimensional solid model of right-angle fasteners is established using the finite element method,the effectiveness of the numerical simulation method is verified through test results,and the comprehensive influence of various design parameters on the performance of fasteners is revealed by the parametric analysis method.The database is established by combining the test and numerical simulation results,and the fastener stiffness prediction models are proposed based on random forest(RF),support vector machine(SVM)and K-most proximity algorithm(K-NN),respectively.The expressions for the measured point displacement of the anti-slip model and the stiffness prediction of the torsion model are proposed in combination with genetic expression programming.The results indicate that SVM and GEP can predict the displacement and torsional stiffness of right-angle fasteners more accurately,which is important for guiding the safety design of fasteners in engineering scaffolding structures.

rectangular fastenersanti-slip performancetorsional performanceparameter analysisma-chine learning

鲍朱杰、李祯、王斐亮、庞博、杨健

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上海交通大学海洋工程国家重点实验室,上海 200240

上海市公共建筑和基础设施数字化运维重点实验室,上海 200240

广州建筑集团有限公司,广州 510030

宁波大学冲击与安全工程教育部重点实验室,浙江宁波 315211

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直角扣件 抗滑移性能 扭转性能 参数分析 机器学习

上海市科技创新项目上海市科技创新项目四川省区域创新合作项目冲击与安全工程教育重点实验室(宁波大学)开放课题中国博士后科学基金

20dz120130121dz12047042022YFQ0048CJ2021062020M682669

2024

10.16183/j.cnki.jsjtu.2022.399

上海交通大学学报
上海交通大学

上海交通大学学报

CSTPCD北大核心
影响因子：0.555
ISSN：1008-7095
年,卷(期)：2024.58(2)
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