Axial compressive capacity prediction and optimal design of circular UHPC-filled steel tube based on Hybrid Symbolic Regression - Neural Network model

IF 3.9 2区工程技术 Q1 ENGINEERING, CIVIL

Structures Pub Date : 2024-08-31 DOI:10.1016/j.istruc.2024.107084

Zhigang Ren, Dian Wang, Gen Kondo

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Abstract

This study presents two methodologies for estimating the ultimate bearing capacity of ultra-high-performance concrete-filled circular steel tube (UHPCFST) columns and offers design optimization guidance: one employing a Symbolic Regression algorithm and the other a Hybrid Symbolic Regression - Neural Network (SR-NN) model. The models were trained, tested, and validated using experimental data from 498 samples sourced from existing literature. The optimal model, SR-NN_14_6, was identified from a pool of 480 models using a trial-and-error approach. The formula and model exhibited enhanced stability and precision, as indicated by their mean absolute error of 8.601 % and 8.051 %, respectively, marking an improvement exceeding 30 % relative to the best existing AIJ code. Using the validated model, a parametric analysis was conducted to assess the influence of the confinement effect coefficient () and steel ratio () on the strength index () of UHPCFST members, leading to the identification of two optimal parameter ranges. The optimal ranges of and were determined to be 0.84 to 1.29 % and 20 % to 30 %, respectively, offering valuable guidelines for the design of UHPCFST members.

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基于混合符号回归-神经网络模型的圆形超高性能混凝土填充钢管轴向抗压能力预测与优化设计

本研究介绍了两种估算超高性能混凝土填充圆钢管（UHPCFST）柱极限承载力的方法，并提供了设计优化指导：一种是符号回归算法，另一种是符号回归-神经网络混合模型（SR-NN）。利用现有文献中 498 个样本的实验数据对这些模型进行了训练、测试和验证。通过试错法，从 480 个模型中确定了最佳模型 SR-NN_14_6。该公式和模型显示出更高的稳定性和精确度，其平均绝对误差分别为 8.601 % 和 8.051 %，与现有最佳 AIJ 代码相比，改进幅度超过 30%。利用经过验证的模型，进行了参数分析，以评估约束效应系数（）和钢比（）对 UHPCFST 构件强度指数（）的影响，从而确定了两个最佳参数范围。确定了和的最佳范围，分别为 0.84 % 至 1.29 % 和 20 % 至 30 %，从而为超高塑性混凝土结构构件的设计提供了有价值的指导。

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来源期刊

Structures Engineering-Architecture

CiteScore

5.70

自引率

17.10%

发文量

1187

期刊介绍： Structures aims to publish internationally-leading research across the full breadth of structural engineering. Papers for Structures are particularly welcome in which high-quality research will benefit from wide readership of academics and practitioners such that not only high citation rates but also tangible industrial-related pathways to impact are achieved.