TY - GEN
T1 - Real-Time Noise Sensing at Construction Sites based on Spatial Interpolation for Effective Reduction Measures
AU - Lee, G.
AU - Moon, S.
AU - Hwang, J.
AU - Chi, S.
AU - Rim, D.
N1 - Publisher Copyright:
© 2022 29th EG-ICE International Workshop on Intelligent Computing in Engineering. All Rights Reserved.
PY - 2022
Y1 - 2022
N2 - Construction site noise needs to be properly managed because it affects the health and safety of workers and nearby residents. Therefore, the authors attempted to develop a real-time noise information mapping system for construction sites that can support the establishment of noise reduction measures by practitioners using spatial interpolation. Field constraints were identified to ensure that the noise-estimation model has high accuracy without disturbing workers. Spatial interpolation was utilized to develop the noise-estimation model, and the performance was evaluated by installing sensors in an experimental environment according to the field constraints. The model showed optimal performance, with maximum and minimum accuracies of 97.5% and 92.4%, respectively, when using eight sensing points as inputs. The research results were visualized through the Unity 3D Engine for the convenience of field workers. Through the results of this study, practitioners will be able to easily understand information on high-noise areas that need to be managed, thereby minimizing the cost and time required for on-site noise management.
AB - Construction site noise needs to be properly managed because it affects the health and safety of workers and nearby residents. Therefore, the authors attempted to develop a real-time noise information mapping system for construction sites that can support the establishment of noise reduction measures by practitioners using spatial interpolation. Field constraints were identified to ensure that the noise-estimation model has high accuracy without disturbing workers. Spatial interpolation was utilized to develop the noise-estimation model, and the performance was evaluated by installing sensors in an experimental environment according to the field constraints. The model showed optimal performance, with maximum and minimum accuracies of 97.5% and 92.4%, respectively, when using eight sensing points as inputs. The research results were visualized through the Unity 3D Engine for the convenience of field workers. Through the results of this study, practitioners will be able to easily understand information on high-noise areas that need to be managed, thereby minimizing the cost and time required for on-site noise management.
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U2 - 10.7146/aul.455.c199
DO - 10.7146/aul.455.c199
M3 - Conference contribution
AN - SCOPUS:85206813360
T3 - Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering
SP - 84
EP - 91
BT - Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering
A2 - Teizer, Jochen
A2 - Schultz, Carl Peter Leslie
PB - European Group for Intelligent Computing in Engineering (EG-ICE)
T2 - 29th International Workshop on Intelligent Computing in Engineering, EG-ICE 2022
Y2 - 6 July 2022 through 8 July 2022
ER -