Digital twins in the built environment: Definition, applications, and challenges

Automation in Construction, Journal Year: 2024, Volume and Issue: 162, P. 105368 - 105368

Published: March 18, 2024

Language: Английский

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Deep learning-based automated productivity monitoring for on-site module installation in off-site construction

Developments in the Built Environment, Journal Year: 2024, Volume and Issue: 18, P. 100382 - 100382

Published: March 12, 2024

Effectively monitoring and analyzing on-site module installation for modular integrated construction (MiC) is essential to properly coordinating the MiC process. In this study, authors propose an automated productivity framework operations consisting of three modules: object detection, activity classification, analysis. The detection detects mobile cranes modules interacting with cranes, classification classifies activities into five different by considering spatiotemporal relationship between detected objects. Finally, analysis analyzes process utilizing accumulated results over image frames. proposed model achieves average accuracy 89% (hooking: 85.71%, lifting: 84.44%, positioning: 94.90%, returning: 83.09%, idling: 96.87%) in classifying activities. developed enables practitioners measure automatically. addition, data stored from diverse sites contribute identifying progress-impeding factors improving entire

Language: Английский

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Binocular vision-based pose monitoring technique for assembly alignment of precast concrete components

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103205 - 103205

Published: Feb. 18, 2025

Language: Английский

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Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Buildings, Journal Year: 2023, Volume and Issue: 13(8), P. 2093 - 2093

Published: Aug. 17, 2023

Noncompliance with safety rules is a major cause of unsatisfactory performance in construction worldwide. Although some research efforts have focused on using computer vision (CV) methods for rule inspection, these are still their early stages and cannot be effectively applied job sites. Therefore, it necessary to present feasible prototype conduct detailed analysis ensure compliance at the site. This study aims extend validation through four case scenarios. The proposed structured classification includes categorizing them based project phases work stages. phase-related divided into groups: (1) before work, (2) intervals, (3) during (4) after work. To validate framework, this developed prototypes each group’s scenarios deep learning algorithms, storage database record rules, an Android application edge computing, which required “before work” “after groups. findings could contribute development compact CV-based monitoring system enhance current management process industry.

Language: Английский

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Video surveillance-based multi-task learning with swin transformer for earthwork activity classification

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 131, P. 107814 - 107814

Published: Dec. 31, 2023

Language: Английский

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3D pose estimation and localization of construction equipment from single camera images by virtual model integration

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 57, P. 102092 - 102092

Published: July 8, 2023

Language: Английский

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Generalized vision-based framework for construction productivity analysis using a standard classification system

Automation in Construction, Journal Year: 2024, Volume and Issue: 165, P. 105504 - 105504

Published: June 11, 2024

Language: Английский

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Transformer-based berm detection for automated bulldozer safety in edge dumping

Automation in Construction, Journal Year: 2024, Volume and Issue: 166, P. 105642 - 105642

Published: July 29, 2024

Language: Английский

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Deep learning-based automated method for enhancing excavator activity recognition in far-field construction site surveillance videos

Automation in Construction, Journal Year: 2025, Volume and Issue: 173, P. 106099 - 106099

Published: March 1, 2025

Language: Английский

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Excavator 3D pose estimation from point cloud with self‐supervised deep learning

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 3, 2025

Abstract Pose estimation of excavators is a fundamental yet challenging task with significant implications for intelligent construction. Traditional methods based on cameras or sensors are often limited by their ability to perceive spatial structures. To address this, 3D light detection and ranging has emerged as promising paradigm excavator pose estimation. However, these face challenges: (1) accurate annotations labor‐intensive costly, (2) exhibit complex kinematics geometric structures, further complicating In this study, novel framework proposed full‐body directly from point clouds, without relying manual annotations. The parameterized using parameters primitives under kinematic constraints. A unified deep network designed predict clouds. initially pre‐trained synthetic data provide parameter initialization then fine‐tuned real‐world data. facilitate label‐free training, the self‐supervised loss functions exploiting consistency between clouds excavators. Experimental results construction sites demonstrate effectiveness robustness method, achieving an average accuracy 0.26 m. method also exhibits performance across various operational scenarios, highlighting its potential applications.

Language: Английский

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