REFINEMENT AND ACCURACY CONTROL OF THE SOLUTION METHOD FOR THE DURABILITY PROBLEM OF A CORRODING STRUCTURE USING NEURAL NETWORK

O. D. Brychkovskyi
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Abstract

Context. The prediction of the time until failure of corroding hinge-rod structures is a crucial component in risk management across various industrial sectors. An accurate solution to the durability problem of corroding structures allows for the prevention of undesired consequences that may arise in the event of an emergency situation. Alongside this, the question of the effectiveness of existing methods for solving this problem and ways to enhance them arises. Objective. The objective is to refine the method of solving the durability problem of a corroding structure using an artificial neural network and establish accuracy control. Method. To refine the original method, alternative sets of input data for the artificial neural network which increase information about the change in axial forces over time are considered. For each set of input data a set of models is trained. Based on target metric values distribution among the obtained sets, a set is selected where the minimum value of the mathematical expectation of the target metric is achieved. For the set of models corresponding to the identified best set, accuracy control of the method is determined by establishing the relationship between the mathematical expectation of the target metric and the parameters of the numerical solution. Results. The conditions under which a lower value of the mathematical expectation of the target metric is obtained compared to the original method are determined. The results of numerical experiments, depending on the considered case, show, in average, an improvement on 43.54% and 9.67% in the refined method compared to the original. Additionally, the proposed refinement reduces the computational costs required to find a solution by omitting certain steps of the original method. An accuracy control rule of the method is established, which allows to obtain on average a given error value without performing extra computations. Conclusions. The obtained results indicate the feasibility of applying the proposed refinement. A higher accuracy in predicting the time until failure of corroding hinge-rod structures allows to reduce the risks of an emergency situation. Additionally, accuracy control enables finding a balance between computational costs and the accuracy of solving the problem. KEYWORDS
利用神经网络对腐蚀结构耐久性问题求解方法的改进和精度控制
背景。预测锈蚀铰链杆结构的失效时间是各工业部门风险管理的重要组成部分。准确解决腐蚀结构的耐久性问题,可以防止在紧急情况下可能出现的不良后果。随之而来的问题是,解决这一问题的现有方法是否有效,以及如何改进这些方法。目标。目的是改进利用人工神经网络解决腐蚀结构耐久性问题的方法,并建立精度控制。方法。为了改进原始方法,我们考虑了人工神经网络的其他输入数据集,这些数据集增加了轴向力随时间变化的信息。针对每组输入数据训练一组模型。根据所获模型集的目标度量值分布情况,选择目标度量数学期望值最小的模型集。对于确定的最佳模型集,通过建立目标度量的数学期望值与数值解法参数之间的关系,确定该方法的精度控制。结果。确定了目标度量的数学期望值低于原始方法的条件。根据所考虑的情况,数值实验结果表明,与原始方法相比,改进方法平均提高了 43.54% 和 9.67%。此外,所提出的改进方法省略了原始方法的某些步骤,从而降低了求解所需的计算成本。该方法建立了一个精度控制规则,可以在不进行额外计算的情况下平均获得给定的误差值。结论。所获得的结果表明,应用所提出的改进方法是可行的。更准确地预测腐蚀铰链连接杆结构的失效时间可以降低紧急情况下的风险。此外,精度控制可以在计算成本和解决问题的精度之间找到平衡。关键词
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