2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 7 , Issue 2 , PP: 110-123, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Lobna Osman 1 *
Doi: https://doi.org/10.54216/FPA.070205
For the last years, a bibliometric examination of risk evaluation approaches for excavating systems has been presented in this publication. To develop an early warning system, it's essential to compile a list of possible dangers that can arise during excavation. Failure Mode and Effects Analysis (FMEA) is a useful approach. Traditional risk assessment techniques have been criticized for a variety of reasons, including a lack of correlation between risk variables, difficult arithmetic operations, and a lack of correctness and preciseness in the evaluations. A unique method of risk analysis in FMEA that uses digraphs and matrix approaches underneath the Pythagorean fuzzy scenario is presented in this research. To get started, we'll defy Pythagorean fuzzy numbers in a triangle form. Both language terminology and risk factor data and information are expressed using them (inclusive of occurrence, severity, and detection). The Pythagorean fuzzy digraph thus captures the interrelationships between the risk variables and the relative importance of each one, as seen in the figure. After that, we create a Pythagorean fuzzy test indicated for each identified failure mode and compute risk priority indexes to determine risk priorities. Using a metro station excavation as a case study, the accuracy of risk assessments in excavation is improved.
decision making , Multi-criteria , Pythagorean fuzzy , Digraph matrix , Risk Assessment , Excavation System , decision support
[1] H.-M. Lyu, W.-H. Zhou, S.-L. Shen, and A.-N. Zhou, “Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen,” Sustainable Cities and Society, vol. 56, p. 102103, 2020.
[2] H.-M. Lyu, S.-L. Shen, A. Zhou, and J. Yang, “Risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP,” Science of the Total Environment, vol. 717, p. 135310, 2020.
[3] Y.-X. Wu, H.-M. Lyu, S.-L. Shen, and A. Zhou, “A three-dimensional fluid-solid coupled numerical modeling of the barrier leakage below the excavation surface due to dewatering,” Hydrogeology Journal, vol. 28, no. 4, pp. 1449–1463, 2020.
[4] Y.-X. Wu, S.-L. Shen, H.-M. Lyu, and A. Zhou, “Analyses of leakage effect of waterproof curtain during excavation dewatering,” Journal of Hydrology, vol. 583, p. 124582, 2020.
[5] Y.-X. Wu, H.-M. Lyu, J. Han, and S.-L. Shen, “Dewatering–induced building settlement around a deep excavation in soft deposit in Tianjin, China,” Journal of Geotechnical and Geoenvironmental Engineering, vol. 145, no. 5, p. 5019003, 2019.
[6] Y. Zhou, W. Su, L. Ding, H. Luo, and P. E. D. Love, “Predicting safety risks in deep foundation pits in subway infrastructure projects: support vector machine approach,” Journal of Computing in Civil Engineering, vol. 31, no. 5, p. 4017052, 2017.
[7] H. U. Qunfang and Q. I. N. Jiabao, “Statistical analysis on accidents of subway tunnel construction from 2003 to 2011 in China,” Chinese Journal of Underground Space and Engineering, vol. 9, no. 3, pp. 705– 710, 2013.
[8] M. Hu, B. Chen, and Q. Xia, “Design and Application of Remote Monitoring System Based on CAD for Foundation Pit,” in Recent Advances in Computer Science and Information Engineering, Springer, 2012, pp. 583–591.
[9] S.-S. Lin, S.-L. Shen, A. Zhou, and Y.-S. Xu, “Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods,” Automation in Construction, vol. 122, p. 103490, 2021.
[10] S. Zhang, W. CHEN, H. WANG, and M. LI, “Construction risk evaluation of deep foundation pit based on G-FAHP,” Journal of Civil Engineering and Management, vol. 33, no. 5, pp. 104–109, 2016.
[11] J. C. Jan, S.-L. Hung, S. Y. Chi, and J. C. Chern, “Neural network forecast model in deep excavation,” Journal of Computing in Civil Engineering, vol. 16, no. 1, pp. 59–65, 2002.
[12] F. ZHANG, C. You-liang, and P. A. N. Zhen-tao, “Analysis of lateral deformation of deep excavation based on back propagation neural network and fuzzy logical control,” Rock and Soil Mechanics, vol. 7, no. 26, pp. 1148–1152, 2005.
[13] Y. Zhou, S. Li, C. Zhou, and H. Luo, “Intelligent approach based on random forest for safety risk prediction of deep foundation pit in subway stations,” Journal of Computing in Civil Engineering, vol. 33, no. 1, p. 5018004, 2019.
[14] L. Zhang, X. Wu, L. Ding, M. J. Skibniewski, and Y. Yan, “Decision support analysis for safety control in complex project environments based on Bayesian Networks,” Expert Systems with Applications, vol. 40, no. 11, pp. 4273–4282, 2013.
[15] Z. Z. Wang and C. Chen, “Fuzzy comprehensive Bayesian network-based safety risk assessment for metro construction projects,” Tunnelling and Underground Space Technology, vol. 70, pp. 330–342, 2017.
[16] Y. Zhou, C. Li, C. Zhou, and H. Luo, “Using Bayesian network for safety risk analysis of diaphragm wall deflection based on field data,” Reliability Engineering & System Safety, vol. 180, pp. 152–167, 2018.
[17] D. H. Stamatis, Failure mode and effect analysis: FMEA from theory to execution. Quality Press, 2003.
[18] W. Song, X. Ming, Z. Wu, and B. Zhu, “A rough TOPSIS approach for failure mode and effects analysis in uncertain environments,” Quality and Reliability Engineering International, vol. 30, no. 4, pp. 473– 486, 2014.
[19] Z. Xu, “An overview of methods for determining OWA weights,” International journal of intelligent systems, vol. 20, no. 8, pp. 843–865, 2005.
[20] K.-H. Chang and T.-C. Wen, “A novel efficient approach for DFMEA combining 2-tuple and the OWA operator,” Expert Systems with Applications, vol. 37, no. 3, pp. 2362–2370, 2010.
[21] H.-C. Liu, L. Liu, and N. Liu, “Risk evaluation approaches in failure mode and effects analysis: A literature review,” Expert systems with applications, vol. 40, no. 2, pp. 828–838, 2013.
[22] H.-C. Liu, J.-X. You, X.-J. Fan, and Q.-L. Lin, “Failure mode and effects analysis using D numbers and grey relational projection method,” Expert Systems with Applications, vol. 41, no. 10, pp. 4670–4679, 2014.
[23] M. Bertolini, G. Esposito, and G. Romagnoli, “A TOPSIS-based approach for the best match between manufacturing technologies and product specifications,” Expert Systems with Applications, vol. 159, p. 113610, 2020.
[24] L. A. Zadeh, “Information and control,” Fuzzy sets, vol. 8, no. 3, pp. 338–353, 1965.
[25] K.-H. Chang and C.-H. Cheng, “Evaluating the risk of failure using the fuzzy OWA and DEMATEL method,” Journal of Intelligent Manufacturing, vol. 22, no. 2, pp. 113–129, 2011.
[26] Z. Zhang and X. Chu, “Risk prioritization in failure mode and effects analysis under uncertainty,” Expert Systems with applications, vol. 38, no. 1, pp. 206–214, 2011.
[27] H.-C. Liu, L. Liu, N. Liu, and L.-X. Mao, “Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment,” Expert Systems with Applications, vol. 39, no. 17, pp. 12926–12934, 2012.
[28] Y.-M. Wang, K.-S. Chin, G. K. K. Poon, and J.-B. Yang, “Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean,” Expert systems with applications, vol. 36, no. 2, pp. 1195–1207, 2009.
[29] J. B. Bowles and C. E. Peláez, “Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis,” Reliability Engineering & System Safety, vol. 50, no. 2, pp. 203–213, 1995.
[30] J. C. R. Alcantud, A. E. Biondo, and A. Giarlotta, “Fuzzy politics I: The genesis of parties,” Fuzzy Sets and Systems, vol. 349, pp. 71–98, 2018.
[31] Z. Ayağ and R. G. Özdemir, “A fuzzy AHP approach to evaluating machine tool alternatives,” Journal of intelligent manufacturing, vol. 17, no. 2, pp. 179–190, 2006.
[32] C.-T. Chen, “Extensions of the TOPSIS for group decision-making under fuzzy environment,” Fuzzy sets and systems, vol. 114, no. 1, pp. 1–9, 2000.
[33] H.-C. Liu, L. Liu, Q.-H. Bian, Q.-L. Lin, N. Dong, and P.-C. Xu, “Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory,” Expert Systems with Applications, vol. 38, no. 4, pp. 4403–4415, 2011.
[34] S. Mandal and J. Maiti, “Risk analysis using FMEA: Fuzzy similarity value and possibility theory based approach,” Expert Systems with Applications, vol. 41, no. 7, pp. 3527–3537, 2014.
[35] M. Rojc and I. Mlakar, “A new fuzzy unit selection cost function optimized by relaxed gradient descent algorithm,” Expert Systems with Applications, vol. 159, p. 113552, 2020.
[36] R. R. Yager and A. M. Abbasov, “Pythagorean membership grades, complex numbers, and decision making,” International Journal of Intelligent Systems, vol. 28, no. 5, pp. 436–452, 2013.
[37] M. Akram, A. Habib, and J. C. R. Alcantud, “An optimization study based on Dijkstra algorithm for a network with trapezoidal picture fuzzy numbers,” Neural Computing and Applications, vol. 33, no. 4, pp. 1329–1342, 2021.
[38] R. Joshi, “A novel decision-making method using R-Norm concept and VIKOR approach under picture fuzzy environment,” Expert Systems with Applications, vol. 147, p. 113228, 2020.
[39] A. Luqman, M. Akram, and J. C. R. Alcantud, “Digraph and matrix approach for risk evaluations under Pythagorean fuzzy information,” Expert Systems with Applications, vol. 170, p. 114518, 2021.
