Abstract

Traditionally, on-site construction production monitoring depends primarily on manual processes that are time-consuming and error-prone. State-of-the-art technologies have been utilized lately to improve these processes to support timely decisions pertinent to the productivity and safety of onsite operations. This research introduces a novel construction site monitoring system to track workers' location, body orientation, and productivity state. The developed system uses Bluetooth Low Energy (BLE) based reference transmitting beacons fixed on job sites and a set of receiving beacons mounted on workers’ hardhats, chests, and wrists. The system works via three modules, i.e. (i) RTLS (Real-Time Location System) module; (ii) body orientation detection module; and (iii) productivity state detection module.

The RTLS module is developed to continuously track the location of the workers and subsequently extract the actual labor workspaces. The RTLS is explicitly designed for construction by satisfying requirements for widespread on-site adoption, including cost efficiency, deployability, scalability, adjustability to the construction site dynamism, and the expected accuracy. The main features of the developed RTLS are (i) substituting commonly used BLE receivers with BLE receiving beacons; (ii) proposing a modular infrastructure placement strategy; (iii) deploying Trilateration and Min-Max as localization techniques; (iv) post-processing the worker’s estimated locations.

As per the body orientation detection module, it identifies workers' body orientation on the job sites, using the impacts of signal blockage by a human body to identify an approximate worker's body orientation. It works based on geometrical relationships and Received Signal Strength Indicator (RSSI) values between the chest-mounted receiving beacon and the reference transmitting beacons. Last but not least, the productivity state detection module determines workers' productivity state (i.e., direct work, support work, delay) and travel state, using the accelerometer sensor embedded in the body-mounted receiving beacons. Consequently, the collected data of the system modules are fused to augment real-time knowledge of workers' status on job sites.