Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

中文翻译：

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基于计算混合机器学习的钢纤维混凝土梁抗剪承载力预测

了解剪切行为对于钢筋混凝土梁的设计以及建筑和土木工程的可持续性至关重要。尽管已经提出了许多研究，但对这种行为的预测仍需要进一步改进。本研究提出了一种软计算工具来预测钢纤维增强混凝土梁的极限抗剪承载力 (USC)，这是结构设计中最重要的因素之一。创建了两种混合机器学习 (ML) 算法，将神经网络 (NN) 与两种不同的优化技术（即实编码遗传算法 (RCGA) 和萤火虫算法 (FFA)）相结合：NN-RCGA 和 NN -FFA。从可靠的文献中收集了 463 个实验数据的数据库，用于模型的开发。施工、验证后，并根据通用统计标准选择最佳模型，并与文献中可用的经验方程进行比较。此外，还进行了敏感性分析以评估 16 个输入的重要性并揭示结构参数对 USC 的依赖性。结果表明，NN-RCGA（R = 0.9771）优于NN-FFA和其他分析模型（R = 0.5274-0.9075）。敏感性分析结果表明，腹板宽度、有效深度和清晰深度比是钢纤维混凝土梁抗剪承载力建模中最重要的参数。进行了敏感性分析以评估 16 个输入的重要性并揭示结构参数对 USC 的依赖性。结果表明，NN-RCGA（R = 0.9771）优于NN-FFA和其他分析模型（R = 0.5274-0.9075）。敏感性分析结果表明，腹板宽度、有效深度和清晰深度比是钢纤维混凝土梁抗剪承载力建模中最重要的参数。进行了敏感性分析以评估 16 个输入的重要性并揭示结构参数对 USC 的依赖性。结果表明，NN-RCGA（R = 0.9771）优于NN-FFA和其他分析模型（R = 0.5274-0.9075）。敏感性分析结果表明，腹板宽度、有效深度和清晰深度比是钢纤维混凝土梁抗剪承载力建模中最重要的参数。