[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.