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Integrating IoT into operational workflows for real-time and automated decision-making in repetitive construction operations.

Authors :
Louis, Joseph
Dunston, Phillip S.
Source :
Automation in Construction. Oct2018, Vol. 94, p317-327. 11p.
Publication Year :
2018

Abstract

Construction operations are typically spread across large areas and require remote collaboration between multiple disparate resources—characteristics that create logistical challenges for making and automating decisions on the worksite. This paper provides a framework for leveraging the growing ubiquity of devices that can be considered part of the internet of things (IoT) to inform real-time decision-making on the construction site. Specifically, systems and control theory concepts are implemented by first synthesizing sensor information at their point of origin into resource state, and then using this state as feedback into a process model of the operation. Decisions that are programed into the process model are then made automatically based on real-time status of the operation and relayed back to the entities on the construction site through the IoT infrastructure. The real-time decision-making capabilities enabled by the presented methodology and its associated benefits to construction performance are demonstrated through the use of a virtual experimental platform that simulates a potential implementation of IoT-enabled control on the construction worksite for an earthmoving operation. This research provides a practical and sensor-agnostic implementation of operation-level decision-making by utilizing IoT networks along with advancements in modeling and simulation tools. This paper illustrates the types of insights that can be synthesized from an operations-level IoT network that gathers and transmits information in real time from various locations of the worksite. Currently and without the use of the prescribed methodology, such actionable insight would be impractical, cost-prohibitive, and even impossible to obtain. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09265805
Volume :
94
Database :
Academic Search Index
Journal :
Automation in Construction
Publication Type :
Academic Journal
Accession number :
131235458
Full Text :
https://doi.org/10.1016/j.autcon.2018.07.005