Abstract
In recent years, excavation work of high-rise buildings has been becoming increasingly complex in the congested urban areas and frequently exposed to substantial hazards, impediment to safety as well as financial loses. For these reasons, advanced sensor-based systems have been used to accurately monitor and diagnose the failure risk of an excavation work; however, it is often tricky to anticipate and assess the impacts of changeable variables, such as human factors, site conditions, material loadings, and mobile equipment, because of the lack of proper tools to explain the unforeseen phenomena. This study proposed a decision support model to concretize effectively experts’ and practitioners’ subjectivities and to quantify the failure risk. The model is fundamentally constructed on fuzzy analytic hierarchy process, which weights the environmental influences that can derive a failure. The outcomes are used as an input for fuzzy comprehensive operations to compute the quantitative failure risk. Three illustrative cases have been examined to explain the capabilities of the proposed model. The results have shown the possibility that the model can be helpful for developing safety precautions with a warning signal during the planning and controlling stages of a new or ongoing excavation work.
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References
An, M., Baker, C., and Zeng, J. (2005). “A fuzzy-logic-based approach to qualitative risk modeling in the construction process.” World Journal of Engineering, Vol. 2, No. 1, pp. 1–12, DOI: 10.1007/978-3-642-11628-5_11.
Bevilacqua, M. and Braglia, M. (2000). “The analytic hierarchy process applied to maintenance strategy selection.” Reliability Engineering and System Safety, Vol. 70, No. 1, pp. 71–83, DOI: 10.1016/S0951-8320(00)00047-8.
Blockley, D. I. (1980). The nature of structural design and safety, John Wiley and Sons, New York, N.Y.
Buckley, J. J. (1985). “Fuzzy hierarchy analysis.” Fuzzy Sets and Systems, Vol. 17, No. 3, pp. 233–247, DOI: 10.1016/0165-0114(85)90090-9.
Canto-Perello, J. and Curiel-Esparza, J. (2001). “Human factors engineering in utility tunnel design.” Tunneling and Underground Space Technology, Vol. 16, No. 3, pp. 211–215, DOI: 10.1016/S0886-7798(01)00041-4.
Chan, A. P. C., Yung, E. H. K., Lam, P. T. I., Tam, C. M., and Cheung, S. O. (2001). “Application of Delphi method in selection of procurement systems for construction projects.” Construction Management and Economics, Vol. 19, No. 7, pp. 699–718, DOI: 10.1080/01446190110066128.
Chen, C. T. (2000). “Extension of the TOPSIS for group decisionmaking under fuzzy environment.” Fuzzy Sets and Systems, Vol. 114, No. 1, pp. 1–9, DOI: 10.1016/S0165-0114(97)00377-1.
Cheung, F. K. T., Kuen, J. L. F., and Skitmore, M. (2002). “Multi-criteria evaluation model for the selection of architecture consultants.” Construction Management and Economics, Vol. 20, No. 3, pp. 569–580, DOI: 10.1080/01446190210159818.
Corral, G. and Whittle, A. J. (2010). Re-analysis of deep excavation collapse using a generalized effective stress soil model, Earth Retention Conference, Washington, USA, pp. 124–127.
Dzeng, R. Z. and Pan, N-.F. (2006). “Learning heuristics for determining slurry wall panel lengths.” Automation in Construction, Vol. 15, No. 3, pp. 303–313, DOI: 10.1016/j.autcon.2005.06.003.
Doloi, H. (2008). “Application of AHP in improving construction productivity from a management perspective.” Construction Management and Economics, Vol. 26, No. 8, pp. 841–854, DOI: 10.1080/01446190802244789.
Fedorowicz, J., Oz, E., and Berger, P. D. (1992). “A learning curve analysis of expert system use.” Decision Science, Vol. 23, No. 4, pp. 797–818, DOI: 10.1111/j.1540-5915.1992.tb00420.x.
Jung, I.-S. and Lee, C.-S. (2012). “Fuzzy inference and AHP-based alternative evaluation tool in the development of sustainable residential land.” KSCE Journal of Civil Engineering, Vol. 16, No. 3, pp. 273–282, DOI: 10.1007/s12205-012-1394-y.
Kaming, P. F., Olomolaiye, P. O., Holt, G. D., and Harris, F. C. (1997). “Factors influencing construction time and cost overruns on highrise projects in Indonesia.” Construction Management and Economics. Vol. 15, No. 1, pp. 83–94, DOI: 10.1080/014461997373132.
Khan, A. H. and Irfan, M. (2011). “Probabilistic analysis of deep excavation design and construction practices in Pakistan.” Pakistan Academy of Science, Vol. 48, No. 1, pp. 1–11, DOI: 10.3233/978-1-61499-297-4-505.
Klir, G. J. and Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and application, Prentice-Hall, New Jersey, N.J.
Lee, S-.Y. (2004). “Variation in accident risk level by perspectives.” KSCE Journal of Civil Engineering, Vol. 8, No. 2, pp. 157–163, DOI: 10.1007/BF02829115.
Lee, S. and Halpin, D. W. (2003). “Predictive tool for estimating accident risk.” Journal of Construction Engineering and Management. Vol. 129, No. 4, pp. 431–436, DOI: 10.1061/(ASCE)0733-9364(2003)129:4(431).
Lin, H-.M. and Hadipriono, F. C. (1990). “Problems in deep foundation construction in Taiwan.” Journal of Performance of Constructed Facilities, Vol. 4, No. 4, pp. 259–270, DOI: 10.1061/(ASCE)0887-3828(1990)4:4(259).
Liong, G. T. (2012). “Deep excavation failures, can they be prevented?” International Symposium on Sustainable Geosynthetics and Green Technology for Climate Change, Bangkok, Thailand, pp. 112–119.
Lloyd, R. A. (1979). “Experience curve analysis.” Applied Economics, Vol. 11, No. 2, pp. 221–234, DOI: 10.1080/758529064.
Mahdi, I.M. and Alreshaid, K. (2005). “Decision support system for selecting the proper project delivery method using Analytical Hierarchy Process (AHP).” International Journal of Project Management, Vol. 23, No. 3, pp. 564–572, DOI: 10.1016/j.ijproman.2005.05.007.
Mamdani, E. H. (1974). “Application for fuzzy algorithms for the control of a dynamic plant.” IEEE Proc., Vol. 121, No. 4, pp. 1585–1588, DOI: 10.1049/piee.1974.0328.
Pan, N.-F. (2008). “Fuzzy AHP approach for selecting the suitable bridge construction method.” Automation in Construction, Vol. 17, No. 8, pp. 958–965, DOI: 10.1016/j.autcon.2008.03.005.
Pan, N.-F. (2009). “Selecting an appropriate excavation construction method based on qualitative assessments.” Expert Systems with Applications, Vol. 36, No. 5, pp. 5481–5490, DOI: 10.1016/j.eswa.2008.06.097.
Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, Resource Allocation, McGraw Hill, New York, N.Y.
Sachs, T. and Tiong, R. L. K. (2009). “Quantifying qualitative information on risks: development of the QQIR method.” Journal of Construction Engineering and Management, Vol. 135, No. 1, pp. 56–71, DOI: 10.1061/(ASCE)0733-9364(2009)135:1(56).
Saito, M. and Sinha, K. (1991). “Delphi study on bridge condition rating and effects of improvements.” Journal of Transportation Engineering, Vol. 117, No. 3, pp. 320–334, DOI: 10.1061/(ASCE)0733-947X(1991)117:3(320)
Sawacha, E., Naoum, S., and Fong, D. (1999). “Factors affecting safety performance on construction sites.” International Journal of Project Management, Vol. 17, No. 5, pp. 300–315, DOI: 10.1016/S0263-7863(98)00042-8.
Shaheen, A. A. and Fayek, A. R. (2007). “Fuzzy numbers in cost range estimating.” Journal of Construction Engineering and Management, Vol. 133, No. 4, pp. 325–334, DOI: 10.1061/(ASCE)0733-9364(2007)133:4(325).
Shapira, A. and Goldenerg, M. (2005). “AHP-based equipment model for construction projects.” Journal of Construction Engineering and Management, Vol. 131, No. 12, pp. 1263–1273, DOI: 10.1061/(ASCE)0733-9364(2005)131:12(1263).
Skibniewski, M. J. and Chao, L. C. (1992). “Evaluation of advanced construction technology with AHP method.” Journal of Construction Engineering and Management, Vol. 118, No. 3, pp. 577–593, DOI: 10.1061/(ASCE)0733-9364(1992)118:3(577).
Wardhana, K. and Hadipriono, F. C. (2003). “Analysis of recent bridge failures in the United States.” Journal of Performance of Constructed Facilities, Vol. 17, No. 3, pp. 144–150, DOI: 10.1061/(ASCE)0887-3828(2003)17:3(144).
Zadeh, L. A. (1965). “Fuzzy Sets.” Information and Control, Vol. 8, pp. 338–353, DOI: 10.4249/scholarpedia.2031.
Zadeh, L. A. (1983). “The role of fuzzy logic in the management of uncertainty in expert system.” Fuzzy Sets and Systems, Vol. 11, No. 1, pp. 197–198, DOI: 10.1016/S0165-0114(83)80081-5.
Zeng, J., An, M., and Chan, A. H. C. (2005). “A fuzzy reasoning decision making approach based multi-expert judgment for construction project risk analysis.” 21 th Annual Conference: Association of Researchers in Construction Management, London, UK, pp. 841–852.
Zeng, J., An, M., and Smith, M. J. (2007). “Application of a fuzzy based decision making methodology to construction project risk management.” International Journal of Project Management, Vol. 25, No. 6, pp. 589–600, DOI: 10.1016/j.ijproman.2007.02.006.
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Kim, DI., Yoo, W.S., Cho, H. et al. A fuzzy AHP-based decision support model for quantifying failure risk of excavation work. KSCE J Civ Eng 18, 1966–1976 (2014). https://doi.org/10.1007/s12205-014-0538-7
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DOI: https://doi.org/10.1007/s12205-014-0538-7