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Automated activity and progress analysis based on non-monotonic reasoning of construction operations

Karsten Winther Johansen (Civil and Architectural Engineering, Aarhus University, Aarhus, Denmark)
Rasmus Nielsen (Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark)
Carl Schultz (Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark)
Jochen Teizer (Civil and Architectural Engineering, Aarhus University, Aarhus, Denmark)

Smart and Sustainable Built Environment

ISSN: 2046-6099

Article publication date: 18 June 2021

Issue publication date: 10 November 2021

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Abstract

Purpose

Real-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.

Design/methodology/approach

The presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.

Findings

This work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.

Research limitations/implications

The KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.

Practical implications

The presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.

Originality/value

While automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.

Keywords

Acknowledgements

The research presented in this paper has received funding from the European Union's Horizon 2020 research and innovation program under grant agreements no. 958398 and no. 958310. This is a substantially extended and enhanced version of the paper presented at the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). The authors would like to acknowledge the editorial contributions of Professor Nashwan Dawood and Dr. Farzad Rahimian of Teesside University in the publication of this paper.

Citation

Johansen, K.W., Nielsen, R., Schultz, C. and Teizer, J. (2021), "Automated activity and progress analysis based on non-monotonic reasoning of construction operations", Smart and Sustainable Built Environment, Vol. 10 No. 3, pp. 457-486. https://doi.org/10.1108/SASBE-03-2021-0044

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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