Tracking major resources for automated progress monitoring of construction activities: masonry work case

Purpose The purpose of this study is to monitor the progress of construction activities in an automated way by using sensor-based technologies for tracking multiple resources that are used in building construction. Design/methodology/approach An automated on-site progress monitoring approach was proposed and a proof-of-concept prototype was developed, followed by a field experimentation study at a high-rise building construction site. The developed approach was used to integrate sensor data collected from multiple resources used in different steps of an activity. It incorporated the domain-specific heuristics that were related to the site layout conditions and method of activity. Findings The prototype estimated the overall progress with 95% accuracy. More accurate and up-to-date progress measurement was achieved compared to the manual approach, and the need for visual inspections and manual data collection from the field was eliminated. Overall, the field experiments demonstrated that low-cost implementation is possible, if readily available or embedded sensors on equipment are used. Originality/value Previous studies either monitored one particular piece of equipment or the developed approaches were only applicable to limited activity types. This study demonstrated that it is technically feasible to determine progress at the site by fusing sensor data that are collected from multiple resources during the construction of building superstructure. The rule-based reasoning algorithms, which were developed based on a typical work practice of cranes and hoists, can be adapted to other activities that involve transferring bulk materials and use cranes and/or hoists for material handling.

Automated summary

This study proposes an automated on-site progress monitoring approach for building construction activities using sensor-based technologies. By integrating sensor data from multiple resources, it achieves more accurate and up-to-date progress measurement compared to manual methods, eliminating the need for visual inspections and manual data collection. The field experiments demonstrate the feasibility and effectiveness of low-cost implementation using readily available or embedded sensors.