In this article, it has been aimed to predict the shear strength of short circular reinforced-concrete columns using the meta-heuristic algorithms. Based on the studies conducted so far, the parameters dominantly affecting the shear strength include axial force, longitudinal and transverse reinforcement, column dimension ratio, concrete compressive strength and ductility. In this respect, first, 200 numerical models of the short circular reinforced-concrete column incorporating various effective parameters so that a sufficient number of outputs could be provided, are analyzed by ABAQUS software to compute their shear strengths. Then, the gene expression programming and particle swarm optimization algorithms are employed to predict the shear strengths and by means of each algorithm, a relation was proposed accordingly. Then, using the experimental data, these relations are evaluated by comparing with those specified in ACI 318 and ASCE-ACI 426. The results indicate that the percentage of relative error between the experimental data and the values obtained from ACI 318 and ASCE-ACI 426 is respectively equal to 25% and 30%, which have been reduced to 13% and 9% through the gene expression programming and particle swarm optimization algorithms implying the satisfactory performance of these two algorithms. Finally, a comparison of the gene expression programming and particle swarm optimization is investigated in terms of convergence rate, degree of accuracy, and performance mechanism.

中文翻译：

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基于软计算方法的钢筋混凝土短圆形柱抗剪强度预测

在本文中，旨在使用元启发式算法预测短圆形钢筋混凝土柱的抗剪强度。根据迄今为止进行的研究，主要影响抗剪强度的参数包括轴力、纵向和横向配筋、柱尺寸比、混凝土抗压强度和延性。在这方面，首先，ABAQUS 软件分析了 200 个包含各种有效参数的短圆形钢筋混凝土柱的数值模型，以便提供足够数量的输出，以计算它们的抗剪强度。然后，采用基因表达编程和粒子群优化算法对剪切强度进行预测，并通过每种算法提出了相应的关系。然后，使用实验数据，通过与 ACI 318 和 ASCE-ACI 426 中规定的比较来评估这些关系。结果表明，实验数据与从 ACI 318 和 ASCE-ACI 426 获得的值之间的相对误差百分比分别为分别为 25% 和 30%，通过基因表达编程和粒子群优化算法分别降低到 13% 和 9%，表明这两种算法的性能令人满意。最后，在收敛速度、准确度和性能机制方面研究了基因表达编程和粒子群优化的比较。结果表明，实验数据与ACI 318和ASCE-ACI 426所得值的相对误差百分比分别为25%和30%，通过基因表达编程分别降低到13%和9%和粒子群优化算法意味着这两种算法的令人满意的性能。最后，在收敛速度、准确度和性能机制方面研究了基因表达编程和粒子群优化的比较。结果表明，实验数据与ACI 318和ASCE-ACI 426所得值的相对误差百分比分别为25%和30%，通过基因表达编程分别降低到13%和9%和粒子群优化算法意味着这两种算法的令人满意的性能。最后，在收敛速度、准确度和性能机制方面研究了基因表达编程和粒子群优化的比较。