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Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

Author

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  • Nahyun Kwon

    (Department of Architectural Engineering, Hanyang University, Ansan 15588, Korea)

  • Joosung Lee

    (Department of Architectural Engineering, Hanyang University, Ansan 15588, Korea)

  • Moonsun Park

    (Department of Architectural Engineering, Hanyang University, Ansan 15588, Korea)

  • Inseok Yoon

    (Department of Architecture and Architectural Engineering, Seoul National University, Seoul 08826, Korea)

  • Yonghan Ahn

    (Department of Architectural Engineering, Hanyang University, Ansan 15588, Korea)

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.

Suggested Citation

  • Nahyun Kwon & Joosung Lee & Moonsun Park & Inseok Yoon & Yonghan Ahn, 2019. "Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning," Sustainability, MDPI, vol. 11(3), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:871-:d:204201
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    References listed on IDEAS

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    Cited by:

    1. Nahyun Kwon & Kwonsik Song & Moonseo Park & Youjin Jang & Inseok Yoon & Yonghan Ahn, 2019. "Preliminary Service Life Estimation Model for MEP Components Using Case-Based Reasoning and Genetic Algorithm," Sustainability, MDPI, vol. 11(11), pages 1-17, May.
    2. Sojin Park & Nahyun Kwon & Yonghan Ahn, 2019. "Forecasting Repair Schedule for Building Components Based on Case-Based Reasoning and Fuzzy-AHP," Sustainability, MDPI, vol. 11(24), pages 1-17, December.

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