| contributor author | Monfared, Ehsan | |
| contributor author | Alipouri, Yaghoub | |
| date accessioned | 2026-08-20T21:28:02Z | |
| date available | 2026-08-20T21:28:02Z | |
| date copyright | 2026/03/26 | |
| date issued | 2026 | |
| identifier other | JCCEE5.CPENG-7234.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314501 | |
| description abstract | AbstractLow 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 ... | |
| publisher | American Society of Civil Engineers | |
| title | On-Site Construction Worker Activity Monitoring Using Deep Learning | |
| type | Journal Article | |
| journal volume | 40 | |
| journal issue | 4 | |
| journal title | Journal of Computing in Civil Engineering | |
| identifier doi | 10.1061/JCCEE5.CPENG-7234 | |
| journal fristpage | 04026040-1 | |
| journal lastpage | 04026040-20 | |
| page | 20 | |
| tree | Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 004 | |
| contenttype | Fulltext | |