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

Abstract

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.

Automation of Work Processes and Night Work

“…On the other hand, Kaasinena [52] notes that the night shift was less hectic than other shifts and that this was because the lighter work tended to be scheduled at night. The findings of this study are also consistent with findings from other industries such as construction [53], agriculture [54], and healthcare [55], all of which are difficult to automate to reduce night work. Folkard and Tucker [39] advocated the abandonment of night work and suggested that all the advantages and disadvantages of this form of work should be examined.…”

Overview of Emerging Technologies for Improving the Performance of Heavy-Duty Construction Machines

“…In addition, data collected through OBI are not useful for tracking productivity [1]. Therefore, to make it more effective, it must be combined with information regarding the material quality and site environment, as presented in [48]. It is very difficult to avoid accidents involving other cranes and building structures, as well as swaying loads, when it comes to workplace safety [21].…”

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

“…By using this simpler kind of information, the progress estimation can be based on an interpretation of localization data via in-area analysis. Guven and Ergen (2020) have used RFID to track major construction equipment and resources (e.g. Tower Crane), which is fused with the crane load sensor, and afterward analyzed in a rule-based reasoning system (Pradhan and Akinci, 2012).…”

“…Tower Crane), which is fused with the crane load sensor, and afterward analyzed in a rule-based reasoning system (Pradhan and Akinci, 2012). However, resource tracking has a significant drawback as the construction material must be tagged to follow it through delivery, movement and application (Guven and Ergen, 2020).…”