Show simple item record

contributor authorMa, Yu
contributor authorCui, Benben
contributor authorJiang, Wen
contributor authorYan, Laibao
contributor authorCui, Xiaotian
date accessioned2026-08-20T21:27:20Z
date available2026-08-20T21:27:20Z
date copyright2026/02/25
date issued2026
identifier otherJCCEE5.CPENG-7088.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314481
description abstractAbstractCracks are frequent defects that pose significant potential threats to structural safety and property. Due to the inefficiency of traditional crack detection methods, deep learning techniques—particularly convolutional neural networks—have become ...Practical ApplicationsThis study proposes a pluggable crack detection module designed for concrete structures. The module can function as a standalone detection model or be seamlessly integrated into existing detection frameworks. It enhances the ...
publisherAmerican Society of Civil Engineers
titleCMCAB: An Insertable Block for Concrete Crack Detection Based on Masked Convolution Attention
typeJournal Article
journal volume40
journal issue3
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/JCCEE5.CPENG-7088
journal fristpage04026027-1
journal lastpage04026027-9
page9
treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 003
contenttypeFulltext


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record