Elamary Ahmed S, Taha Ibrahim B M
Civil Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Electrical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Materials (Basel). 2021 May 1;14(9):2364. doi: 10.3390/ma14092364.
The use of corrugated webs increases web shear stability and eliminates the need for transverse stiffeners in steel beams. Optimised regression learner techniques (ORLTs) are rarely used for calculating shear capacity in steel beam research. This study proposes a new approach for calculating the maximum shear capacity of steel beams with trapezoidal corrugated webs (SBCWs) by using ORLTs. A new shear model is proposed using ORLTs in accordance with plate buckling theory and previously developed formulas for predicting the shear strength of SBCWs. The proposed ORLT models are implemented using the regression learner toolbox of MATLAB software (2020b). The available data of more than 125 test results from different specimens prepared by previous researchers are used to create the model. In this study, web geometry and relevant web steel grades determine the shear capacity of SBCWs. Four regression methods are adopted. Results are compared with those of an artificial neural network model. The model output factor represents the ratio of the web vertical shear stress to the normalised shear stress. Shear capacity can be estimated on the basis of the resulting factor from the model. The proposed model is verified using two methods. In the first method, a series of tests are performed by the authors. In the second method, the results of the model are compared with the shear values obtained experimentally by other researchers. On the basis of the test results of previous studies and the current work, the proposed model provides an acceptable degree of accuracy for predicting the shear capacity of SBCWs. The results obtained using Gaussian process regression are the most appropriate because its recoded mean square error is 0.07%. The proposed model can predict the shear capacity of SBCWs with an acceptable percentage of error. The recoded percentage of error is less than 5% for 93% of the total specimens. By contrast, the maximum differential obtained is ±10%, which is recorded for 3 out of 125 specimens.
波纹腹板的使用提高了腹板的抗剪稳定性，并且无需在钢梁中设置横向加劲肋。在钢梁研究中，优化回归学习器技术（ORLTs）很少用于计算抗剪承载力。本研究提出了一种利用ORLTs计算梯形波纹腹板钢梁（SBCWs）最大抗剪承载力的新方法。根据板件屈曲理论和先前推导的用于预测SBCWs抗剪强度的公式，利用ORLTs提出了一种新的抗剪模型。所提出的ORLT模型使用MATLAB软件（2020b）的回归学习器工具箱来实现。利用先前研究人员制备的不同试件的125多个试验结果的可用数据来创建该模型。在本研究中，腹板几何形状和相关腹板钢材等级决定了SBCWs的抗剪承载力。采用了四种回归方法。将结果与人工神经网络模型的结果进行了比较。模型输出因子表示腹板垂直剪应力与归一化剪应力的比值。抗剪承载力可根据模型得到的因子进行估算。所提出的模型通过两种方法进行了验证。第一种方法是作者进行一系列试验。第二种方法是将模型结果与其他研究人员通过试验获得的剪应力值进行比较。基于先前研究和当前工作的试验结果，所提出的模型在预测SBCWs抗剪承载力方面具有可接受的精度。使用高斯过程回归得到的结果最为合适，因为其记录的均方误差为0.07%。所提出的模型能够以可接受的误差百分比预测SBCWs的抗剪承载力。对于93%的试件，记录的误差百分比小于5%。相比之下，所获得的最大差值为±10%，这在125个试件中有3个出现。
