Ahmad Muhammad Mahtab, Elahi Ayub, Barbhuiya Salim
Civil Engineering Department, University of Engineering and Technology Taxila, Taxila 47050, Pakistan.
Department of Engineering and Construction, University of East London, London E16 2RD, UK.
Materials (Basel). 2023 Dec 14;16(24):7642. doi: 10.3390/ma16247642.
This research aims to conduct a comparative analysis of the first crack load, flexural strength, and shear strength in reinforced concrete beams without stirrups. The comparison is made between the conventional model developed according to the current design code (ACI building code) and an unconventional approach using Artificial Neural Networks (ANNs). To accomplish this, a dataset comprising 110 samples of reinforced concrete beams without stirrup reinforcement was collected and utilised to train a Multilayer Backpropagation Neural Network in MATLAB. The primary objective of this work is to establish a knowledge-based structural analysis model capable of accurately predicting the responses of reinforced concrete structures. The coefficient of determination obtained from this comparison yields values of 0.9404 for the first cracking load, 0.9756 for flexural strength, and 0.9787 for shear strength. Through an assessment of the coefficient of determination and linear regression coefficients, it becomes evident that the ANN model produces results that closely align with those obtained from the conventional model. This demonstrates the ANN's potential for precise prediction of the structural behaviour of reinforced concrete beams.
本研究旨在对无腹筋钢筋混凝土梁的初裂荷载、抗弯强度和抗剪强度进行对比分析。对比是在根据现行设计规范（美国混凝土学会建筑规范）开发的传统模型与使用人工神经网络（ANN）的非传统方法之间进行的。为此，收集了包含110个无腹筋钢筋混凝土梁样本的数据集，并用于在MATLAB中训练多层反向传播神经网络。这项工作的主要目标是建立一个基于知识的结构分析模型，能够准确预测钢筋混凝土结构的响应。从该对比中获得的决定系数，初裂荷载为0.9404，抗弯强度为0.9756，抗剪强度为0.9787。通过对决定系数和线性回归系数的评估，很明显人工神经网络模型产生的结果与传统模型获得的结果非常吻合。这证明了人工神经网络在精确预测钢筋混凝土梁结构行为方面的潜力。
