基于机器学习的单钢板混凝土组合板冲击响应预测及优化

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (8) : 28-37.

论文

赵唯以，陈沛涵

作者信息 +

Impact response prediction and optimization of half steel-concrete composite slabs based on machine learning

ZHAO Weiyi，CHEN Peihan

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摘要

单钢板混凝土组合板（HSC板）的抗冲击性能十分出色，可有效抵挡落石冲击，在符合相关规范要求的前提下，可以通过设计降低HSC板在落石冲击作用下的变形，以最大限度地保证山区基础设施和人民的安全。为快速、准确地处理HSC板的变形和设计参数之间的复杂非线性关系，基于3种机器学习算法分别建立冲击作用下HSC板最大变形的预测模型，并通过有限元结果对模型进行验证。在此基础上，以落石冲击下HSC板的最大变形、自重和造价为优化目标，采用遗传算法对某山区建筑中某HSC屋面板进行优化设计，求解HSC屋面板的厚度、连接件尺寸等设计参数的最优解集。研究结果表明，高斯过程回归（GPR）模型对HSC屋面板变形的预测精度最高，可以代替复杂耗时的有限元计算，有效提高目标方程的计算效率，且最终的优化结果给出了多种优化方案，可以有效降低HSC屋面板变形，为工程设计提供了参考。

Abstract

The impact resistance of half steel-concrete composite slabs (HSC slabs) is very excellent, which can effectively resist rockfall impact. Under the premise of meeting the requirements of relevant codes, HSC slabs can be designed to reduce the deformation of HSC slabs under rockfall impact, so as to maximize the safety of infrastructure and people in mountain areas. To quickly and accurately deal with the complex nonlinear relationship between HSC slabs deformation and design parameters, the prediction models of HSC slabs maximum deformation under impact are established based on three machine learning algorithms, and the model is verified by finite element results. On this basis, taking the maximum deformation, self-weight and cost of HSC slabs under rockfall impact as the optimization goal, the genetic algorithm is used to optimize the design of a HSC roof slab in a mountain building, and the optimal solution set of design parameters such as thickness and connector size of HSC roof slabs is solved. The research results show that the gaussian process regression (GPR) model has the highest prediction accuracy for the deformation of HSC roof panels, which can replace the complex and time-consuming finite element calculation, and effectively improve the calculation efficiency of the objective equation, and the final optimization results give a variety of optimization schemes, which can effectively reduce the deformation of HSC roof slabs, and provide a reference for engineering design.

导出引用

赵唯以，陈沛涵. 基于机器学习的单钢板混凝土组合板冲击响应预测及优化[J]. 振动与冲击, 2023, 42(8): 28-37

ZHAO Weiyi，CHEN Peihan. Impact response prediction and optimization of half steel-concrete composite slabs based on machine learning[J]. Journal of Vibration and Shock, 2023, 42(8): 28-37

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