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A Neural Network Modelling of Steel Joint Block Shear Capacity

机译：钢结块剪切容量的神经网络建模

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Despite the multiple failure modes associated to bolted steel joints the block shear failure is frequently the controlling limit state of gusset plates, tension members and coped beam joints. The block shear rupture is characterised by a condition where a steel block area, nearby the holes of bolted joint region, reaches its maximum capacity dictated by a tension to shear interaction. If the joint is loaded beyond this limit a steel block is eventually separated from the main structure. Many models have been proposed to avoid shear block failure in steel design. These models incorporate a combination of the various hypotheses that control the structural rupture. In the majority of cases, it involves a combination of rupture and yielding in the tension and shear planes of the designed joint. Various design formula for this structural engineering problem were proposed all over the years but a closed solution has not yet been reached, due to the influence of several independent parameters. Many studies have already been carried out with the available experimental data, however, non negligible errors are still present in the current design formulae. The main objective of the present paper is to present a back propagation neural network evaluation of the block shear phenomenon. The main neural network input parameters were the geometrical and material variables that control the block shear design. The neural network output parameter was the block shear capacity. The neural network training was based on experimental results present in literature and adopted cross validation techniques to avoid the neural network overfitting. The neural network results were promising, with reduced associated errors and confirmed the possibility of using this methodology to generate trustworthy data. These new data, coupled with the existent experiments, can help the production of more accurate design formulae.
机译：尽管与螺栓钢接头相关联的多种故障模式，但块剪切失效频繁是角撑板，张力构件和应对梁接头的控制限制状态。块剪切破裂的特征在于一种条件，其中钢块面积在螺栓接合区域的孔附近达到其最大容量，其由剪切相互作用的张力决定。如果接头装载超过该限制，则最终将钢块与主要结构分离。已经提出了许多模型以避免钢设计中的剪切阻力。这些模型包括控制结构破裂的各种假设的组合。在大多数情况下，它涉及破裂和屈服于所设计的关节的张力和剪切平面的组合。多年来提出了这种结构工程问题的各种设计公式，但由于几个独立参数的影响，尚未达到封闭的解决方案。许多研究已经通过可用的实验数据进行，然而，当前的设计公式中仍然存在不可忽略的误差。本文的主要目的是呈现反向传播神经网络评估块剪切现象。主要的神经网络输入参数是控制块剪切设计的几何和材料变量。神经网络输出参数是块剪切容量。神经网络培训基于文献中存在的实验结果，采用交叉验证技术，以避免神经网络过度装备。神经网络的结果是有前途的，并且相关错误减少并确认使用该方法生成可靠性数据的可能性。这些新数据与现有的实验相结合，可以帮助生产更准确的设计公式。

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