Digital Twin for Control of Noise Emissions from Heavy Equipment on Construction Sites

Noise originating from construction sites has been found to cause psychological and social distress for the public, as well as hearing loss for construction workers. Although it is important to focus on construction noise during the pre-construction phase of planning for proactive avoidance, noise propagation models are rarely used to assess its impact on the health of workers and those living nearby. The primary reason for this is that information about noise sources is difficult to obtain from conventional construction schedules for preparing noise prediction models, so-called "noise maps". Building Information Modeling (BIM) is already widely used to achieve project management goals such as time and cost control. Likewise, the Internet of Things (IoT) can add reliable runtime noise data capture functionality as part of a Digital Twin (DT) and utilize remote sensing technology to record precise on- and off-construction site noise data sets. Although noise maps are widely used for large-scale noise forecasting, such as traffic noise in cities, their potential for supporting health and safety in the construction industry has not yet been recognized. This research aims to define an approach for creating stepwise noise maps using noise source data exported from a BIM model and real-time captured data from the construction site. Construction noise maps will not only assist in the task of assigning work packages for minimizing noise coming from construction equipment and, accordingly, a proper site layout plan and schedule for the project, but they will also provide the project manager with precise knowledge that gives control over the exposure of construction workers and the surrounding community to higher sound pressure levels.