CONSIDERATION OF CRACK WIDTH MEASUREMENT OF REINFORCED CONCRETE STRUCTURES BY USING PLURAL DEEP LEARNING MODELS

被引：0

作者：

Murakami S. ^{[1
]}

Kamada S. ^{[1
]}

Takase Y. ^{[2
]}

Mizoguchi M. ^{[2
]}

机构：

[1] Division of Sustainable and Environmental Engineering, Muroran Institute of Technology

[2] College of Design and Manufacturing Technology, Muroran Institute of Technology

来源：

AIJ Journal of Technology and Design | 2022年 / 28卷 / 69期

关键词：

Crack width; Deep learning; Image analysis; Reinforced concrete;

D O I：

10.3130/aijt.28.673

中图分类号：

学科分类号：

摘要：

Recently, after a huge earthquake, reinforced concrete buildings were not available or demolished due to sever damages. Therefore, a damage assessment becomes important; hence, measuring damages from images is one of the most useful techniques. In this study, crack widths of the non-structural wall specimens were measured by using plural deep learning model. By the models which provide the extremely small values of Accuracy and Precision, cracks could not be predicted. While, the deep learning model, in which the values for Recall and F1Score were high, could properly identify the cracks; then, the crack width was reasonably measured. © 2022 Architectural Institute of Japan. All rights reserved.

引用

页码：673 / 678

页数：5

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