contributor author | Qingling Meng | |
contributor author | Yun Zhang | |
contributor author | Hailiang Wang | |
contributor author | Xin Huang | |
contributor author | Zhenyu Wang | |
date accessioned | 2023-04-07T00:40:46Z | |
date available | 2023-04-07T00:40:46Z | |
date issued | 2022/12/01 | |
identifier other | %28ASCE%29CF.1943-5509.0001773.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4289532 | |
description abstract | In recent years, more and more disasters caused by the fracture of bridge cables have been reported. Brittle fracture of cables is mainly caused by corrosion fatigue, and thus it is crucial to find cable defects early. The present study developed a cable inspection method embedded with a lightweight deep-learning model and equipped with micromagnetic sensors, based on intelligent image recognition and magnetic memory technology. After four cable-stayed bridges were found with defects, five types of defects and features on the surface of cables were identified by the SqueezeNet network model with the image denoising algorithm and transfer-learning method, with accuracy of 97.18%. The corrosion along the cable was positioned with micromagnetic sensors. Four alerting levels were proposed and corresponding remedial measures were suggested to be implemented. The novelty of this work lies in the intelligent detection of bridge defects, as well as accurate evaluation of long-term performance of bridge cables. | |
publisher | ASCE | |
title | A Detection Method for Bridge Cables Based on Intelligent Image Recognition and Magnetic-Memory Technology | |
type | Journal Article | |
journal volume | 36 | |
journal issue | 6 | |
journal title | Journal of Performance of Constructed Facilities | |
identifier doi | 10.1061/(ASCE)CF.1943-5509.0001773 | |
journal fristpage | 04022059 | |
journal lastpage | 04022059_11 | |
page | 11 | |
tree | Journal of Performance of Constructed Facilities:;2022:;Volume ( 036 ):;issue: 006 | |
contenttype | Fulltext | |