IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i5p1714-d328887.html
   My bibliography  Save this article

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Author

Listed:
  • Ping Liu

    (School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Mengchu Xie

    (School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Jing Bian

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China)

  • Huishan Li

    (School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Liangliang Song

    (Institute of Engineering Management, Hohai University, Nanjing 211100, China)

Abstract

Incorporating safety risk into the design process is one of the most effective design sciences to enhance the safety of metro station construction. In such a case, the concept of Design for Safety (DFS) has attracted much attention. However, most of the current research overlooks the risk-prediction process in the application of DFS. Therefore, this paper proposes a hybrid risk-prediction framework to enhance the effectiveness of DFS in practice. Firstly, 12 influencing factors related to the safety risk of metro construction are identified by adopting the literature review method and code of construction safety management analysis. Then, a structured interview is used to collect safety risk cases of metro construction projects. Next, a developed support vector machine (SVM) model based on particle swarm optimization (PSO) is presented to predict the safety risk in metro construction, in which the multi-class SVM prediction model with an improved binary tree is designed. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. Finally, the proposed framework is applied to a case study of metro station construction. The prediction results show the PSO–SVM model is applicable and reasonable for safety risk prediction. This research also identifies the most important influencing factors to reduce the safety risk of metro station construction, which provides a guideline for the safety risk prediction of metro construction for design process.

Suggested Citation

  • Ping Liu & Mengchu Xie & Jing Bian & Huishan Li & Liangliang Song, 2020. "A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction," IJERPH, MDPI, vol. 17(5), pages 1-24, March.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:5:p:1714-:d:328887
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/5/1714/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/5/1714/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu Bai & Jianzhou Wang & Xuejiao Ma & Haiyan Lu, 2018. "Air Pollution Forecasts: An Overview," IJERPH, MDPI, vol. 15(4), pages 1-44, April.
    2. Zhongxin Chen & Feng Zhao & Jun Zhou & Panling Huang & Xutao Zhang, 2019. "Fault Diagnosis of Loader Gearbox Based on an ICA and SVM Algorithm," IJERPH, MDPI, vol. 16(23), pages 1-20, December.
    3. Ping Liu & Qiming Li & Jing Bian & Liangliang Song & Xiaer Xiahou, 2018. "Using Interpretative Structural Modeling to Identify Critical Success Factors for Safety Management in Subway Construction: A China Study," IJERPH, MDPI, vol. 15(7), pages 1-18, June.
    4. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, April.
    5. Shihang Wang & Zongmin Li & Yuhong Wang & Qi Zhang, 2019. "Machine Learning Methods to Predict Social Media Disaster Rumor Refuters," IJERPH, MDPI, vol. 16(8), pages 1-16, April.
    6. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    7. Chen, Shuixia & Wang, Jian-qiang & Zhang, Hong-yu, 2019. "A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 41-54.
    8. Juntao Fan & Mengdi Li & Fen Guo & Zhenguang Yan & Xin Zheng & Yuan Zhang & Zongxue Xu & Fengchang Wu, 2018. "Priorization of River Restoration by Coupling Soil and Water Assessment Tool (SWAT) and Support Vector Machine (SVM) Models in the Taizi River Basin, Northern China," IJERPH, MDPI, vol. 15(10), pages 1-15, September.
    9. Zhou, Zhifang & Xiao, Tian & Chen, Xiaohong & Wang, Chang, 2016. "A carbon risk prediction model for Chinese heavy-polluting industrial enterprises based on support vector machine," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 304-315.
    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. Ping Liu & Yu Wang & Tongze Han & Jiaming Xu & Qiangnian Li, 2022. "Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model," Sustainability, MDPI, vol. 14(16), pages 1-21, August.

    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. Alan Hevner & Isabelle Comyn-Wattiau & Jacky Akoka & Nicolas Prat, 2018. "A pragmatic approach for identifying and managing design science research goals and evaluation criteria," Post-Print hal-02283783, HAL.
    2. Tobias Knabke & Sebastian Olbrich, 2018. "Building novel capabilities to enable business intelligence agility: results from a quantitative study," Information Systems and e-Business Management, Springer, vol. 16(3), pages 493-546, August.
    3. Gao, Shuzhi & Zhang, Sixuan & Zhang, Yimin & Gao, Yue, 2020. "Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    4. Sunder Shyam, 2011. "Imagined Worlds of Accounting," Accounting, Economics, and Law: A Convivium, De Gruyter, vol. 1(1), pages 1-14, January.
    5. Fiori Stefano, 2005. "The emergence of instructions : some open problems in Hayek's theory," CESMEP Working Papers 200504, University of Turin.
    6. McCown, R. L., 2002. "Changing systems for supporting farmers' decisions: problems, paradigms, and prospects," Agricultural Systems, Elsevier, vol. 74(1), pages 179-220, October.
    7. Jin P. Gerlach & Ronald T. Cenfetelli, 2022. "Overcoming the Single-IS Paradigm in Individual-Level IS Research," Information Systems Research, INFORMS, vol. 33(2), pages 476-488, June.
    8. Xiaoyan Jiang & Sai Wang & Jie Wang & Sainan Lyu & Martin Skitmore, 2020. "A Decision Method for Construction Safety Risk Management Based on Ontology and Improved CBR: Example of a Subway Project," IJERPH, MDPI, vol. 17(11), pages 1-23, June.
    9. Chih‐Hsuan Wang & Chia‐Rong Chang, 2023. "Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1261-1274, August.
    10. Basile, Luigi Jesus & Carbonara, Nunzia & Pellegrino, Roberta & Panniello, Umberto, 2023. "Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making," Technovation, Elsevier, vol. 120(C).
    11. Loris Gaio, 2005. "A diversity-based approach to requirements tracing in new product development," ROCK Working Papers 031, Department of Computer and Management Sciences, University of Trento, Italy, revised 13 Jun 2008.
    12. B. A. Huberman & N. S. Glance, "undated". "Diversity and Collective Action," Working Papers _001, Xerox Research Park.
    13. Zhewei Zhang & Youngjin Yoo & Kalle Lyytinen & Aron Lindberg, 2021. "The Unknowability of Autonomous Tools and the Liminal Experience of Their Use," Information Systems Research, INFORMS, vol. 32(4), pages 1192-1213, December.
    14. Juval Portugali & Egbert Stolk, 2014. "A SIRN View on Design Thinking—An Urban Design Perspective," Environment and Planning B, , vol. 41(5), pages 829-846, October.
    15. Pan, Yongjun & Sun, Yu & Li, Zhixiong & Gardoni, Paolo, 2023. "Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    16. Gilbert Giacomoni & Adel Aloui, 2018. "Imaginaire et imitation du réel : genèse des idées et sciences de l’artificiel," Post-Print hal-01941661, HAL.
    17. Funk, Jeffery, 2009. "Components, systems and discontinuities: The case of magnetic recording and playback equipment," Research Policy, Elsevier, vol. 38(7), pages 1192-1202, September.
    18. Richard Holt & J. Barkley Rosser & David Colander, 2011. "The Complexity Era in Economics," Review of Political Economy, Taylor & Francis Journals, vol. 23(3), pages 357-369.
    19. David Stadelmann & Benno Torgler, 2012. "Bounded Rationality and Voting Decisions Exploring a 160-Year Period," Working Papers 2012.70, Fondazione Eni Enrico Mattei.
    20. Karén Hovhannissian & Marco Valente, 2004. "Modeling Directed Local Search Strategies on Technology Landscapes: Depth and Breadth," ROCK Working Papers 028, Department of Computer and Management Sciences, University of Trento, Italy, revised 17 Jun 2008.

    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:jijerp:v:17:y:2020:i:5:p:1714-:d:328887. 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.