Crack width and spacing measurement using deep learning and damage assessment for reinforced concrete non-structural wall
Year: 2024
Type: article
Abstract: Recently, the seismic resistance of reinforced concrete (RC) buildings has significantly increased. However, as the performance of structural members increases, damage to both structural and non-structural members becomes a severe problem. In this study, deep learning (DL) was used for damage assessment in non-structural walls by investigating the crack width and spacing. The images captured during previous tests, which were loading tests for non-structural specimens, were used as the DL data. First, twelve DL models were created and verified to determine the optimal model for crack detection. The model that used Xception and SGDM, and did not use image extension performed better than the other models. Subsequently, a crack width measurement method was proposed, and these values were found to be similar to the crack widths based on the crack gauge. However, the measured values were 1.35 times larger than those of the crack gauge. Additionally, a crack spacing measurement technique was proposed. The measured spacing was similar to that obtained through visual observation. Nevertheless, when the distance between the two cracks was small, the accuracy of the proposed technique decreased. However, the proposed technique was useful when numerous images were used. Finally, the relationship between the maximum shear force and the concrete damage was investigated. The crack width was more affected by the rebar ratio than by the rebar spacing. In contrast, the crack spacing was affected more by the rebar spacing than by the rebar ratio. Additionally, the crack spacing significantly affected the maximum shear force. Therefore, it is clear that the rebar spacing should be considered in the shear strength estimation. These results will help in damage assessment and developing a new shear strength equation for RC structural and non-structural walls.
Authors: Kota Ohsasa, Seiya Kamada, Yuya Takase
Institution: Muroran Institute of Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,110
OpenAlex ID: W4402654291