IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i11p3074-d235912.html
   My bibliography  Save this article

Preliminary Service Life Estimation Model for MEP Components Using Case-Based Reasoning and Genetic Algorithm

Author

Listed:
  • Nahyun Kwon

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

  • Kwonsik Song

    (Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward St., 2340 G.G. Brown Building, Ann Arbor, MI 48109, USA)

  • Moonseo Park

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

  • Youjin Jang

    (School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA)

  • 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

In recent decades, building maintenance has been recognized as an important issue as the number of deteriorating buildings increases around the world. In densely populated cities, building maintenance is essential for ensuring sustainable living and safety for residents. Improper maintenance can not only cause enormous maintenance costs, but also negatively affect residents and their environment. As a first step, the service life of building components needs to be estimated in advance. Mechanical, electrical, and plumbing (MEP) components especially produce many maintenance-related problems compared to other components. In this research, a model was developed that applies the genetic algorithm (GA) and case-based reasoning (CBR) methodologies to estimating the service life of MEP components. The applicability of the model was tested by comparing the outputs of 20 randomly selected test cases with those of retrieved similar cases. The experimental results demonstrated that the overall similarity scores of the retrieved cases were over 90%, and the mean absolute error rate (MAER) of 10-NN was approximately 7.48%. This research contributes to the literature for maintenance management by not only presenting an approach to estimating the service life of building components, but also by helping convert the existing maintenance paradigm from reactive to proactive measures.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3074-:d:235912
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/11/3074/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/11/3074/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    2. Adolfo Crespo Márquez, 2007. "The Maintenance Management Framework," Springer Series in Reliability Engineering, Springer, number 978-1-84628-821-0, March.
    3. Sanghyo Lee & Yonghan Ahn, 2018. "Analyzing the Long-Term Service Life of MEP Using the Probabilistic Approach in Residential Buildings," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    4. Moonsun Park & Nahyun Kwon & Joosung Lee & Sanghyo Lee & Yonghan Ahn, 2019. "Probabilistic Maintenance Cost Analysis for Aged Multi-Family Housing," Sustainability, MDPI, vol. 11(7), pages 1-13, March.
    5. Soyeon Park & Yonghan Ahn & Sanghyo Lee, 2018. "Analyzing the Finishing Works Service Life Pattern of Public Housing in South Korea by Probabilistic Approach," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    6. 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.
    7. I. M. Shohet & M. Puterman & E. Gilboa, 2002. "Deterioration patterns of building cladding components for maintenance management," Construction Management and Economics, Taylor & Francis Journals, vol. 20(4), pages 305-314.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shu-Shun Liu & Muhammad Faizal Ardhiansyah Arifin, 2021. "Preventive Maintenance Model for National School Buildings in Indonesia Using a Constraint Programming Approach," Sustainability, MDPI, vol. 13(4), pages 1-24, February.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Moonsun Park & Nahyun Kwon & Joosung Lee & Sanghyo Lee & Yonghan Ahn, 2019. "Probabilistic Maintenance Cost Analysis for Aged Multi-Family Housing," Sustainability, MDPI, vol. 11(7), pages 1-13, March.
    3. Youngduk Cho & Sanghyo Lee & Joosung Lee & Jaejun Kim, 2021. "Analysis of the Repair Time of Finishing Works Using a Probabilistic Approach for Efficient Residential Buildings Maintenance Strategies," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    4. Yong-Sik Yoon & Yong-Han Ahn & Xiao-Yong Wang & Seung-Jun Kwon, 2021. "Total Repair Cost Simulation Considering Multiple Probabilistic Measures and Service Life," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    5. Kwonsik Song & Yonghan Ahn & Joseph Ahn & Nahyun Kwon, 2019. "Development of an Energy Saving Strategy Model for Retrofitting Existing Buildings: A Korean Case Study," Energies, MDPI, vol. 12(9), pages 1-17, April.
    6. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    7. Sanghyo Lee & Yonghan Ahn, 2018. "Analyzing the Long-Term Service Life of MEP Using the Probabilistic Approach in Residential Buildings," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    8. Yuhan Zhang & Youqi Wang & Yiru Bai & Ruiyuan Zhang & Xu Liu & Xian Ma, 2023. "Prediction of Spatial Distribution of Soil Organic Carbon in Helan Farmland Based on Different Prediction Models," Land, MDPI, vol. 12(11), pages 1-15, October.
    9. Bharat Prasad Bhandari & Subodh Dhakal & Ching-Ying Tsou, 2024. "Assessing the Prediction Accuracy of Frequency Ratio, Weight of Evidence, Shannon Entropy, and Information Value Methods for Landslide Susceptibility in the Siwalik Hills of Nepal," Sustainability, MDPI, vol. 16(5), pages 1-25, March.
    10. Niu, Gang & Yang, Bo-Suk & Pecht, Michael, 2010. "Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 786-796.
    11. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    12. Salari, Mahmoud & Javid, Roxana J., 2016. "Residential energy demand in the United States: Analysis using static and dynamic approaches," Energy Policy, Elsevier, vol. 98(C), pages 637-649.
    13. Agnieszka Blokus & Przemysław Dziula, 2021. "Relations of Imperfect Repairs to Critical Infrastructure Maintenance Costs," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
    14. Lu Gan & Yuanyuan Wang & Yusheng Wang & Benjamin Lev & Wenjing Shen & Wen Jiang, 2021. "Coupling coordination analysis with data-driven technology for disaster–economy–ecology system: an empirical study in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2123-2153, July.
    15. Sholeh Hadi Pramono & Mahdin Rohmatillah & Eka Maulana & Rini Nur Hasanah & Fakhriy Hario, 2019. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System," Energies, MDPI, vol. 12(17), pages 1-16, August.
    16. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    17. Changrui Deng & Xiaoyuan Zhang & Yanmei Huang & Yukun Bao, 2021. "Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting," Energies, MDPI, vol. 14(13), pages 1-14, July.
    18. Syed Muhammad Raza Abidi & Mushtaq Hussain & Yonglin Xu & Wu Zhang, 2018. "Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development," Sustainability, MDPI, vol. 11(1), pages 1-21, December.
    19. Yaquelin Verenice Pantoja-Pacheco & Armando Javier Ríos-Lira & José Antonio Vázquez-López & José Alfredo Jiménez-García & Martha Laura Asato-España & Moisés Tapia-Esquivias, 2021. "One Note for Fractionation and Increase for Mixed-Level Designs When the Levels Are Not Multiple," Mathematics, MDPI, vol. 9(13), pages 1-20, June.
    20. Radhakrishnan Angamuthu Chinnathambi & Anupam Mukherjee & Mitch Campion & Hossein Salehfar & Timothy M. Hansen & Jeremy Lin & Prakash Ranganathan, 2018. "A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets," Forecasting, MDPI, vol. 1(1), pages 1-21, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3074-:d:235912. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.