Abstract
Concerns over environmental issues have recently increased. Particularly, construction noise in highly populated areas is recognized as a serious stressor that not only negatively affects humans and their environment, but also construction firms through project delays and cost overruns. To deal with noise-related problems, noise levels need to be predicted during the preconstruction phase. Case-based reasoning (CBR) has recently been applied to noise prediction, but some challenges remain to be addressed. In particular, problems with the distance measurement method have been recognized as a recurring issue. In this research, the accuracy of the prediction results was examined for two distance measurement methods: The weighted Euclidean distance (WED) and a combination of the Jaccard and Euclidean distances (JED). The differences and absolute error rates confirmed that the JED provided slightly more accurate results than the WED with an error ratio of approximately 6%. The results showed that different methods, depending on the attribute types, need to be employed when computing similarity distances. This research not only contributes an approach to achieve reliable prediction with CBR, but also contributes to the literature on noise management to ensure a sustainable environment by elucidating the effects of distance measurement depending on the attribute types.
Highlights
Environmental issues are a growing concern, and environmental pollution is globally recognized as a serious problem in modern society that adversely affects people and their surroundings [1,2,3,4,5,6,7,8]
They conducted experiments to validate the applicability of the developed model, and the results showed that the model could be applied to noise prediction during the preconstruction phase when there is insufficient noise-related information, with an acceptable error ratio of below 5%
The results demonstrated that the machine-learning regression model outperformed the multiple linear regression (MLR) model [58]
Summary
Environmental issues are a growing concern, and environmental pollution is globally recognized as a serious problem in modern society that adversely affects people and their surroundings [1,2,3,4,5,6,7,8]. The developed model for the prediction of the noise level during the preconstruction phase consists of three sub-modules: (1) Case-base establishment, (2) attribute weighting, and (3) case retrieval. The proposed model demonstrates the effect of the similarity distance measurement on the prediction results This model should help improve noise prediction accuracy in the preconstruction phase, which will help in the management of noise onsite and the establishment of noise-related measures in advance. The results of this research can be useful for construction noise management, and for cost estimation, market selection, facility maintenance, medical diagnosis, and risk analysis in which CBR is applicable
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