Noise propagation poses a particular challenge for construction companies, especially in the context of urban area projects involving heavy machinery and equipment. Residents living near construction sites may feel disturbed or experience adverse health effects, often leading to complaints against construction projects. Such complaints can result in delays or even the temporary suspension of construction project, causing cost overruns. Existing noise control methods face limitations in urban areas, since relocating residents is costly and often infeasible, while noise barriers are less effective due to the close proximity between noise source and receiver. Despite the importance of controlling construction noise to minimize conflicts and risks for companies, noise considerations are not integrated during the design and planning phases. Instead, noise assessments are conducted infrequently during the execution phase and are often postponed until conflicts arise. To effectively integrate noise considerations into the planning phase, a prediction model is needed that is both visual and comprehensive for all project participants. Although noise mapping is a commonly used method for predicting environmental noise, its potential for visualizing construction noise and aiding time planners in incorporating noise considerations already in the planning phase is overlooked. Even though detecting noise sources for noise prediction is possible with 4D BIM models, current BIM studies in the field of acoustics focus primarily on using 3D BIM models for indoor acoustics rather than outdoor noise propagation of construction processes. To address this gap, this thesis develops a 4D BIM-supported approach for predicting construction noise using the noise mapping method. The methodology utilizes 4D BIM models to extract noise source data, including equipment locations and operational durations, which are then used to generate noise maps. These noise maps calculate sound pressure levels at building facades and surrounding locations, providing visual and comprehensive predictions to all project stakeholders. Noise sensors installed at the construction site recorded sound pressure levels over a full workday, which were then compared with predicted values from the generated noise maps. The results demonstrated that 90% of predicted values fell within the ±6 dB(A) validation threshold, confirming the accuracy of the method. The noise maps successfully captured locations where sound pressure levels would exceed permissible limits. Therefore, the approach proposed in this study can provide construction planners with visual information to identify potential noise conflicts before the execution phase. In addition, the approach supports the decision-making process for equipment scheduling and work hour planning. By integrating noise considerations into the planning phase in a comprehensible way, this methodology supports construction companies in minimizing complaints and, therefore, to reduce risks of delay. More importantly, it can improve stakeholder communication through clear, visual noise impact presentations.
Nasim Babazadeh Khameneh (Thu,) studied this question.