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Project control operations in construction are mostly executed via direct observations and the manual monitoring of progress and performance of construction tasks on the job site. Project engineers move physically within job-site areas to ensure activities are executed as planned. Such physical displacements are error-prone and ineffective in cost and time, particularly in larger construction zones. It is critical to explore new methods and technologies to effectively assist performance control operations by rapidly capturing data from materials and equipment on the job site. Motivated by the ubiquitous use of unmanned aerial vehicles (UAVs) in construction projects and the maturity of computer-vision-based machine-learning (ML) techniques, this research investigates the challenges of object detection—the process of predicting classes of objects (specified construction materials and equipment)—in real time. The study addresses the challenges of data collection and predictions for remote monitoring in project control activities. It uses these two proven and robust technologies by exploring factors that impact the use of UAV aerial images to design and implement object detectors through an analytical conceptualization and a showcase demonstration. The approach sheds light on the applications of deep-learning techniques to access and rapidly identify and classify resources in real-time. It paves the way to shift from costly and time-consuming job-site walkthroughs that are coupled with manual data processing and input to more automated, streamlined operations. The research found that the critical factor to develop object detectors with acceptable levels of accuracy is collecting aerial images with for adequate scales with high frequencies from different positions of the same construction areas.
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Automated material selection based on detected construction progress
One of the many ways in which automation may help the construction industry is on-site material management. This paper presents an automated process where materials are selected for staging by detecting construction progress from site images. The materials are then delivered to their respective workface locations by a robot. The effectiveness of the material selection process is assessed using a simulated and physical construction site. We demonstrate that our process is successful under a number of different conditions and environments. Our system contributes to the feasibility of autonomously managing materials on a construction site and reveals potential avenues for future research.
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- Award ID(s):
- 1928626
- PAR ID:
- 10358908
- Date Published:
- Journal Name:
- international symposium on automation and construction
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.22260/ISARC2022/0056
