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Development of a real-time noise estimation model for construction sitesopen access

Authors
Lee, GitaekMoon, SeonghyeonHwang, JaehyunChi, Seokho
Issue Date
Oct-2023
Publisher
Elsevier Ltd
Keywords
Construction noise; Noise management; Noise propagation; Real-time noise estimation; Spatial interpolation
Citation
Advanced Engineering Informatics, v.58
Indexed
SCIE
SCOPUS
Journal Title
Advanced Engineering Informatics
Volume
58
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/68755
DOI
10.1016/j.aei.2023.102133
ISSN
1474-0346
1873-5320
Abstract
As construction noise negatively affects the health and quality of life of stakeholders, field managers need to properly monitor and manage noise. Thus, the authors developed a model that estimates real-time noise levels at a construction site and the surroundings to enable preemptive responses to noise-related issues. To accurately estimate noise, necessary field data were collected using an unmanned aerial vehicle (UAV) and noise sensors. The noise estimation model was composed of two sub-models: the noise-customized spatial interpolation model and the noise propagation model. The noise-customized spatial interpolation model was developed to estimate the internal noise of the construction site using a few sensor noise levels. Meanwhile, the noise propagation model was developed to estimate the noise level outside the construction site using internal noise estimation results, obstacles, weather information, and noise sources information. The model was evaluated through field tests at a construction technology demonstration center, environments identical to real construction sites in South Korea. The model showed satisfactory performance, with an accuracy of 96.71% and a root mean square error (RMSE) of 2.62 for the internal construction site noise and an accuracy of 96.03% and an RMSE of 2.70 for outside the construction site. To facilitate the usage of the noise estimation results for field managers, the research team visualized the results using the Unity 3D Engine. The results will enable field managers to assess workers’ long-term noise exposure and respond to potential civil complaints, gearing up to realize environmental, social, and governance (ESG) goals in the construction industry. © 2023 The Author(s)
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