Srecon-Nerf: A Neural Rendering-Based Semantic Point Cloud Retrieval Method for Indoor Construction Progress Monitoring DOI
Zhiming Dong,

Wilson Lu,

Junjie Chen

et al.

Published: Jan. 1, 2023

Language: Английский

Automated vision-based construction progress monitoring in built environment through digital twin DOI Creative Commons
Aritra Pal, Jacob J. Lin, Shang‐Hsien Hsieh

et al.

Developments in the Built Environment, Journal Year: 2023, Volume and Issue: 16, P. 100247 - 100247

Published: Oct. 11, 2023

Effective progress monitoring is ineviTable for completing the construction of building and infrastructure projects successfully. In this digital transformation era, with data-centric management control approach, effectiveness methods expected to improve dramatically. "Digital Twin," which creates a bidirectional communication flow between physical entity its counterpart, found be crucial enabling technology information-aware decision-making systems in manufacturing other automotive industries. Recognizing benefits production construction, researchers have proposed Digital Twin Construction (DTC). DTC leverages information modeling processes, lean practices, on-site data collection mechanisms, Artificial Intelligence (AI) based analytics improving planning processes. Progress monitoring, key component control, can significantly benefit from DTC. However, some knowledge gaps still need filled practical implementation built environment domain. This research reviews existing vision-based methods, studies evolution automated research, highlights methodological technological that must addressed DTC-based predictive monitoring. Subsequently, it proposes framework closed-loop through Finally, way forward fully automated, real-time upon concept proposed.

Language: Английский

Citations

38
4D BIM and Reality Model–Driven Camera Placement Optimization for Construction Monitoring DOI

Seau Chen Houng,

Aritra Pal, Jacob J. Lin

et al.

Journal of Construction Engineering and Management, Journal Year: 2024, Volume and Issue: 150(6)

Published: March 29, 2024

Cameras are one of the most valuable sensors for collecting high-quality visual data on construction sites uses ranging from surveillance to automated information exaction. The dynamic nature means cameras can suffer occlusions and lack coverage due progressing works, hindering performance analysis methods. Therefore, manual planning adjustments by experienced practitioners required appropriate camera placement at site, which is expensive time-consuming. Past research has simulated used algorithms with an objective function optimize installation parameters planned site models two-dimensional (2D) four-dimensional (4D). However, these ongoing conditions, hampering actual performance. This study proposes a framework incorporating 4D-building model (BIM) reality models. first identifies determinants through expert interviews. Next, BIM construct simulation environment, optimized. proposed implemented evaluated site. 25% average improvement benchmark solution achieved. further contributes potential application monitoring systems sites.

Language: Английский

Citations

7
Data integration using deep learning and real-time locating system (RTLS) for automated construction progress monitoring and reporting DOI Creative Commons
Dena Shamsollahi,

Osama Moselhi,

Khashayar Khorasani

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 168, P. 105778 - 105778

Published: Sept. 23, 2024

Language: Английский

Citations

4
Production-based progress monitoring of rebar tying using few-shot learning and kernel density DOI Creative Commons
Biaoli Gao, Bin Yang,

Hongru Xiao

et al.

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 117, P. 81 - 98

Published: Jan. 11, 2025

Language: Английский

Citations

0
Vision-based real-time progress tracking and productivity analysis of the concrete pouring process DOI Creative Commons

Ruoxue Zhang,

Ruyu Deng,

Zhao Zhang

et al.

Developments in the Built Environment, Journal Year: 2025, Volume and Issue: unknown, P. 100609 - 100609

Published: Jan. 1, 2025

Language: Английский

Citations

0
Change detection network for construction housekeeping using feature fusion and large vision models DOI Creative Commons
Kailai Sun,

Zherui Shao,

Yang Miang Goh

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 172, P. 106038 - 106038

Published: Feb. 6, 2025

Language: Английский

Citations

0
Neural radiance fields for construction site scene representation and progress evaluation with BIM DOI
Yuntae Jeon, Dai Quoc Tran,

Khoa Vo

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 172, P. 106013 - 106013

Published: Feb. 8, 2025

Language: Английский

Citations

0
Ground Surface Semantic Segmentation with Uav-Based Point Clouds for Automated Earthwork Monitoring DOI
Gitaek T. Lee,

Haksun Kim,

Seokho Chi

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Hybrid deep learning model for predicting failure properties of asphalt binder from fracture surface images DOI Creative Commons

Babak Asadi,

Viraj Shah, Abhilash Vyas

et al.

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

Published: Feb. 20, 2025

Abstract Cracking impacts asphalt concrete durability primarily due to cohesive binder failures. The poker chip test has recently been introduced better characterize the cracking potential of binders by fracturing a specimen in realistic stress state thin film. However, broader adoption faces challenges high instrumentation costs for measuring load and displacement. This paper presents validates deep learning model that predicts ductility tensile strength from posttest images fractured surfaces, with extensions simplified instrumentation. hybrid model, named PCNet, integrates custom lightweight convolutional neural network (CNN) developed capture local features (e.g., edges, boundaries, contours) within fracture cavities Swin Transformer models global contextual dependencies. A bidirectional cross‐attention fusion module is designed facilitate mutual information exchange between CNN transformer branches. fused are then processed fully connected (FCN) predict indices derived test. proposed demonstrates predictive accuracy across range configurations, achieving an 0.966 mean absolute percentage error (MAPE) 12.95% predicting ductility, while also attaining 0.947 MAPE 9.15% strength, outperforming standalone models. Monte Carlo Dropout incorporated FCN quantify prediction confidence. cost‐effective methodology provides insights into propagation soft viscoelastic media contributes field experimental mechanics. With further data collection, holds implementation, directly linking surface mixture or field‐scale behavior.

Language: Английский

Citations

0
Monitoring concrete pouring progress using knowledge graph-enhanced computer vision DOI Creative Commons
Fabian Pfitzner, Shiu‐Lok Hu, Alexander Braun

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 174, P. 106117 - 106117

Published: March 18, 2025

Language: Английский

Citations

0