The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R2) during the testing phase (R2 = 0.95), whereas the other comparable models generated relatively lower values of R2 ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

中文翻译：

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使用集成仿生算法和人工智能模型的钢筋混凝土深梁抗剪强度能力建模

尽管在该领域存在现代进步，但钢筋混凝土深梁的设计和可持续性仍然是结构工程领域的主要问题。正确理解剪应力特性有助于提供更安全的设计并防止深梁失效，从而挽救生命和财产。在这项研究中，基于支持向量回归与仿生优化方法的混合，称为遗传算法 (SVR-GA) 的新智能模型被用来预测钢筋混凝土 (RC) 深梁的抗剪强度。和材料参数属性。采用的 SVR-GA 建模方法已针对三种不同的完善的人工智能 (AI) 模型进行验证，包括经典 SVR、人工神经网络 (ANN) 和梯度提升决策树 (GBDT)。比较评估清楚地表明所提出的 SVR-GA 模型在预测简支深梁抗剪强度能力方面的优越能力。SVR-GA模型得到的模拟结果与实验结果非常接近。在定量结果中，测试阶段的决定系数 (R2) (R2 = 0.95)，而其他可比模型产生的 R2 值相对较低，范围从 0.884 到 0.941。总而言之，所提出的 SVR-GA 模型展示了一种适用且强大的计算机辅助技术，可用于模拟 RC 深梁剪切强度，有助于材料和结构工程方面的基础知识。比较评估清楚地表明所提出的 SVR-GA 模型在预测简支深梁抗剪强度能力方面的优越能力。SVR-GA模型得到的模拟结果与实验结果非常接近。在定量结果中，测试阶段的决定系数 (R2) (R2 = 0.95)，而其他可比模型产生的 R2 值相对较低，范围从 0.884 到 0.941。总而言之，所提出的 SVR-GA 模型展示了一种适用且强大的计算机辅助技术，可用于模拟 RC 深梁剪切强度，有助于材料和结构工程方面的基础知识。比较评估清楚地表明所提出的 SVR-GA 模型在预测简支深梁抗剪强度能力方面的优越能力。SVR-GA模型得到的模拟结果与实验结果非常接近。在定量结果中，测试阶段的决定系数 (R2) (R2 = 0.95)，而其他可比模型产生的 R2 值相对较低，范围从 0.884 到 0.941。总而言之，所提出的 SVR-GA 模型展示了一种适用且强大的计算机辅助技术，可用于模拟 RC 深梁剪切强度，有助于材料和结构工程方面的基础知识。