Shrif Mazen, Barakat Samer, Al-Sadoon Zaid, Mostafa Omar, Awad Raghad
Department of Civil and Environmental Engineering, College of Engineering, University of Sharjah, United Arab Emirates.
Heliyon. 2024 Aug 3;10(15):e35778. doi: 10.1016/j.heliyon.2024.e35778. eCollection 2024 Aug 15.
Beam-like members use corrugated webs to increase their shear strength, stability, and efficiency. The corrugation positively affects the members' structural characteristics, especially those governed by the web parameters, such as the shear strength, while reducing the total weight. Existing code and analytical models for predicting the shear strength of trapezoidal corrugated steel webs (TCSWs) are summarized. This paper presents an optimized Artificial Neural Network (ANN)-based model to estimate the shear strength of steel girders with a TCSW subjected to a concentrated force. A database of 206 experimental results from the literature is used to feed the ANNs. Six geometrical and material parameters were identified as input variables, and the experimental shear strength at failure was considered the output variable. Four hyperparameter optimization techniques are applied to refine the ANN models: Bayesian Optimization (BO), Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), Firefly Algorithm (FA), and African Buffalo Optimization (ABO). The performance metrics indicate that the ABO-ANN model is the most effective among these. The predictions of the developed ML model were also compared with those of existing code and analytical models. The comparisons illustrated that the ANN-based model outperforms the other existing models. The sensitivity analysis using the proposed ANN-based model captured the relationships and interactions among the geometric and material parameters and their impact on shear strength. One main finding is that the corrugation angle in the 35-45° range maximized the TCSW shear strength.
梁状构件采用波纹腹板来提高其抗剪强度、稳定性和效率。波纹对构件的结构特性有积极影响，特别是那些受腹板参数控制的特性，如抗剪强度，同时还能减轻总重量。本文总结了用于预测梯形波纹钢腹板（TCSW）抗剪强度的现有规范和分析模型。本文提出了一种基于优化人工神经网络（ANN）的模型，用于估算承受集中荷载的带TCSW钢梁的抗剪强度。利用文献中的206个实验结果数据库来训练人工神经网络。确定了六个几何和材料参数作为输入变量，并将实验破坏时的抗剪强度作为输出变量。应用了四种超参数优化技术来优化人工神经网络模型：贝叶斯优化（BO）、有限内存布罗伊登-弗莱彻-戈德法布-香农算法（L-BFGS）、萤火虫算法（FA）和非洲水牛优化算法（ABO）。性能指标表明，ABO-ANN模型在这些模型中最为有效。还将所开发的机器学习模型的预测结果与现有规范和分析模型的预测结果进行了比较。比较结果表明，基于人工神经网络的模型优于其他现有模型。使用所提出的基于人工神经网络的模型进行的敏感性分析揭示了几何和材料参数之间的关系和相互作用及其对抗剪强度的影响。一个主要发现是，波纹角度在35°至45°范围内时，TCSW的抗剪强度最大。
