Asghar Raheel, Javed Muhammad Faisal, Alrowais Raid, Khalil Alamgir, Mohamed Abdeliazim Mustafa, Mohamed Abdullah, Vatin Nikolai Ivanovich
Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
Department of Civil Engineering, Jouf University, Sakaka, Al-Jouf 72388, Saudi Arabia.
Materials (Basel). 2022 Apr 5;15(7):2673. doi: 10.3390/ma15072673.
This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (), root mean squared error (), mean absolute error (), and root relative squared error (). From the performance evaluation results of these models, , , , and were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported , , , and to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.
本研究提出了一种基于人工智能（AI）的基因表达式编程（GEP）的新方法，用于预测钢筋混凝土（RC）矩形柱在地震作用下的横向承载能力。为实现预期的研究目标，采用了由太平洋地震工程研究（PEER）中心汇编的包含250个RC矩形柱循环试验样本的实验数据库。利用这些柱样本的七个输入变量来建立针对既定能力输出的理想分析模型。这些输入变量的选择基于线性回归和余弦幅值法。基于GEP建模结果，提出了两个用于计算RC矩形柱抗弯和抗剪能力的分析模型。基于四个关键适应度指标，即决定系数（）、均方根误差（）、平均绝对误差（）和根相对平方误差（），对这两个模型的性能进行了评估。从这些模型的性能评估结果来看，抗弯能力模型的、、、分别为0.96、53.41、38.12和0.20，抗剪能力模型的分别为0.95、39.47、28.77和0.22。除了这些适应度标准外，还通过与美国混凝土结构设计规范ACI 318 - 19进行比较，对所提出模型的性能进行了评估。ACI模型报告的抗弯能力的、、、分别为0.88、101.86、51.74和0.39，抗剪能力输出的分别为0.87、238.74、183.66和1.35。比较结果表明，与ACI 318 - 19相比，所提出的模型具有更好的性能和更高的准确性。
