A new type of bridge structure reinforcement effect evaluation algorithm based on time series

Zhiwei Yin
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引用次数: 0

Abstract

A supplement method based on reusing the original bridge structure is suggested in order to expedite construction and reduce costs. And the theoretical analysis of the single pile bearing capacity is conducted, primarily involving the calculation of bridge pile foundation settlement, horizontal resistance, and axial bearing capacity. At the same time, considering that the time series data generated in the actual process often has characteristics of imbalance between classes, a cost sensitive hybrid network (CSHN) model is also constructed. Its significance is to analyze the bridge reinforcement performance data. The experimental results showed that compared with the cross entropy loss function, the introduction of a cost-sensitive loss function can significantly improve the monitoring accuracy of abnormal data sets. The score of ACC-, G-means, and F-measures is higher than that of specific cross entropy loss function. The permissible bearing capacity of muddy clay determined by a network model complies with engineering standards in the evaluation of bridge reinforcing performance. As a result, the reinforcing reliability of this form of reinforcement is improved.
一种新型的基于时间序列的桥梁结构加固效果评价算法
为了加快施工速度,降低成本,提出了一种基于原有桥梁结构再利用的补充方法。并对单桩承载力进行了理论分析,主要包括桥梁桩基沉降、水平阻力、轴向承载力的计算。同时,考虑到实际过程中产生的时间序列数据往往具有类间不平衡的特点,构建了代价敏感混合网络(CSHN)模型。其意义在于分析桥梁的配筋性能数据。实验结果表明,与交叉熵损失函数相比,引入代价敏感损失函数可以显著提高异常数据集的监测精度。ACC-、G-means和F-measures的得分高于特定交叉熵损失函数的得分。用网络模型确定的泥质粘土的允许承载力符合桥梁加固性能评价的工程标准。从而提高了这种钢筋形式的加固可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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