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contributor authorMonfared, Ehsan
contributor authorAlipouri, Yaghoub
date accessioned2026-08-20T21:28:02Z
date available2026-08-20T21:28:02Z
date copyright2026/03/26
date issued2026
identifier otherJCCEE5.CPENG-7234.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314501
description abstractAbstractLow productivity in the construction industry compared with other sectors is a longstanding concern. Traditionally, managers have relied on manual sampling of worker activities by monitoring task types and durations to identify and address ...Practical ApplicationsEnhancing labor productivity is a significant challenge in the construction industry, with direct implications for project costs and schedules. Traditional monitoring methods, such as manual observation, are often impractical due to ...
publisherAmerican Society of Civil Engineers
titleOn-Site Construction Worker Activity Monitoring Using Deep Learning
typeJournal Article
journal volume40
journal issue4
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/JCCEE5.CPENG-7234
journal fristpage04026040-1
journal lastpage04026040-20
page20
treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 004
contenttypeFulltext


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