IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v234y2020i3p512-526.html
   My bibliography  Save this article

Analyzing safety level and recognizing flaws of commercial centers through data mining approach

Author

Listed:
  • Abdorrahman Haeri

Abstract

The construction industry, including buildings and commercial centers, is a dynamic industry with diverse and complex nature, which makes its safety provision difficult. The aim of this study is to evaluate the safety status of commercial centers and their classification based on common features; and to uncover the hidden relationships between characteristics of the commercial centers under study by means of data mining techniques. Data required for this study were collected based on a 75-item checklist designed for this study. Indeed, this study included 108 commercial centers. Thereafter, the commercial centers under study were divided into three categories, labeled unsafe, normal, and safe by means of K-means algorithm. The results obtained from the implementation of classification method showed that the two resources, namely, fire protection systems and buildings, played a critical role in the safety of studied commercial centers. The results of in-depth analysis on unsafe commercial centers indicated that these centers have common weaknesses. These weak areas include such items as deficiency of the standards required for the equipment associated with some resources, insufficient training in the use of firefighting equipment, the necessity of the employment of redundant approaches for exit from the building in emergency conditions, and non-feasibility of conducting of operations for firefighting vehicles and lifts. Urban planners and managers and safety officials of the buildings, particularly commercial centers, can apply the results of this study as strategic guidelines.

Suggested Citation

  • Abdorrahman Haeri, 2020. "Analyzing safety level and recognizing flaws of commercial centers through data mining approach," Journal of Risk and Reliability, , vol. 234(3), pages 512-526, June.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:3:p:512-526
    DOI: 10.1177/1748006X19889812
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X19889812
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X19889812?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, February.
    2. Plasse, Marie & Niang, Ndeye & Saporta, Gilbert & Villeminot, Alexandre & Leblond, Laurent, 2007. "Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 596-613, September.
    3. Mohammad Rabiei & Seyyed-Mahdi Hosseini-Motlagh & Abdorrahman Haeri, 2017. "Using text mining techniques for identifying research gaps and priorities: a case study of the environmental science in Iran," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 815-842, February.
    Full references (including those not matched with items on IDEAS)

    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. Mark Gilchrist & Deana Lehmann Mooers & Glenn Skrubbeltrang & Francine Vachon, 2012. "Knowledge Discovery in Databases for Competitive Advantage," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 3(2), pages 2-15, April.
    2. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
    3. Jérome SARACCO & Marie CHAVENT & Vanessa KUENTZ, 2010. "Clustering of categorical variables around latent variables," Cahiers du GREThA (2007-2019) 2010-02, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    4. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    5. Sebastian Büsch & Volker Nissen & Arndt Wünscher, 0. "Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    6. Yucel, Ahmet & Dag, Ali & Oztekin, Asil & Carpenter, Mark, 2022. "A novel text analytic methodology for classification of product and service reviews," Journal of Business Research, Elsevier, vol. 151(C), pages 287-297.
    7. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    8. Saljooghi, Saeed & Safisamghabadib, Azamdokht, 2016. "Analyzing Semiconductor component's market sales data to create an Expert Fuzzy inference system," MPRA Paper 79846, University Library of Munich, Germany.
    9. Ramin Vakili & Mojdeh Khorsand, 2022. "A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies," Energies, MDPI, vol. 15(23), pages 1-28, November.
    10. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
    11. Chen, Kunlong & Zheng, Fangdan & Jiang, Jiuchun & Zhang, Weige & Jiang, Yan & Chen, Kunjin, 2017. "Practical failure recognition model of lithium-ion batteries based on partial charging process," Energy, Elsevier, vol. 138(C), pages 1199-1208.
    12. Emrouznejad, Ali & De Witte, Kristof, 2010. "COOPER-framework: A unified process for non-parametric projects," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1573-1586, December.
    13. Shaheen, Muhammad & Khan, Muhammad Zeb, 2016. "A method of data mining for selection of site for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1225-1233.
    14. Asil Oztekin, 2018. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 20(2), pages 223-238, April.
    15. Sebastian Büsch & Volker Nissen & Arndt Wünscher, 2017. "Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques," Information Systems Frontiers, Springer, vol. 19(5), pages 1085-1099, October.
    16. Wenge Rong & Baolin Peng & Yuanxin Ouyang & Kecheng Liu & Zhang Xiong, 2015. "Collaborative personal profiling for web service ranking and recommendation," Information Systems Frontiers, Springer, vol. 17(6), pages 1265-1282, December.
    17. Renhe Hu & Zihan Hui & Yifan Li & Jueqi Guan, 2023. "Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments," Sustainability, MDPI, vol. 15(15), pages 1-16, July.
    18. Danuta Piróg & Joanna Fidelus-Orzechowska & Łukasz Wiejaczka, 2022. "Local Authority vs Community Visions of Dam Project Land Development: a Text Mining Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1833-1848, April.
    19. Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    20. Mehri, Ali & Darooneh, Amir H., 2011. "The role of entropy in word ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(18), pages 3157-3163.

    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:sae:risrel:v:234:y:2020:i:3:p:512-526. 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: SAGE Publications (email available below). General contact details of provider: .

    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.