Ensemble model for risk status evaluation of excavation

Abstract This study develops a risk status evaluation model by integrating the technique for order preference by similarity to an ideal solution (TOPSIS) and Monte Carlo simulation (MCS) methods. The excavation system is divided into subsystems based on the monitoring scheme. The TOPSIS method integrates the information on the influential factors, and MCS overcomes the uncertainty, fuzziness, and human errors in data collection. A comprehensive weight determination method is proposed to determine the weights of the influential factors. The developed model was applied to evaluate the risk status of excavation projects in Tianjin. The results were consistent with the actual conditions of the excavation system. The higher correlation factors in the evaluation results can be identified through correlation analysis. Finally, a value for the ideal parameter λ in the membership function is recommended through sensitivity analysis. The developed model provides guidelines for establishing early risk warnings and management for excavation engineering.

[1]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[2]  F. Hosseinzadeh Lotfi,et al.  Extension of TOPSIS for decision-making problems with interval data: Interval efficiency , 2009, Math. Comput. Model..

[3]  Xiaoyan Wang,et al.  An improved approach for water quality evaluation: TOPSIS-based informative weighting and ranking (TIWR) approach , 2018, Ecological Indicators.

[4]  Kok-Kwang Phoon,et al.  Evaluation of Geotechnical Property Variability , 1999 .

[5]  Ning Zhang,et al.  An extended TODIM-based model for evaluating risks of excavation system , 2021, Acta Geotechnica.

[6]  Shui-Long Shen,et al.  Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms , 2021 .

[7]  K. Binder,et al.  Monte Carlo Simulation in Statistical Physics , 1992, Graduate Texts in Physics.

[8]  Yang Wang,et al.  Monetary valuation of urban underground space: A critical issue for the decision-making of urban underground space development , 2017 .

[9]  Ye-Shuang Xu,et al.  Novel model for risk identification during karst excavation , 2021, Reliab. Eng. Syst. Saf..

[10]  Hangseok Choi,et al.  Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels , 2015 .

[11]  K. Phoon,et al.  Characterization of Geotechnical Variability , 1999 .

[12]  Shui-Long Shen,et al.  Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods , 2021 .

[13]  F. Peng,et al.  Monetary evaluation method of comprehensive benefits of complex underground roads for motor vehicles orienting urban sustainable development , 2021 .

[14]  Heng Li,et al.  Knowledge dynamics-integrated map as a blueprint for system development: Applications to safety risk management in Wuhan metro project , 2018, Automation in Construction.

[15]  Mohammad Izadikhah,et al.  An algorithmic method to extend TOPSIS for decision-making problems with interval data , 2006, Appl. Math. Comput..

[16]  Changjie Xu,et al.  Longitudinal Sliding Event during Excavation of Feng-Qi Station of Hangzhou Metro Line 1: Postfailure Investigation , 2018, Journal of Performance of Constructed Facilities.

[17]  G. Cai,et al.  Seepage on local stability of slurry trench in deep excavation of diaphragm wall construction , 2021 .

[18]  Changjie Xu,et al.  Investigation on performance of a large circular pit-in-pit excavation in clay-gravel-cobble mixed strata , 2018, Tunnelling and Underground Space Technology.

[19]  Ali Fakher,et al.  Design of deep urban excavations using life cycle cost in comparison with acceptable risk and conventional method , 2021 .

[20]  Myung Jin Chae,et al.  A 3D surface modeling system for intelligent excavation system , 2011 .

[21]  A. Brencich,et al.  Effect of deep excavations and deformable retaining structures on neighboring buildings: A case study , 2021, Engineering Failure Analysis.

[22]  Mu-Song Chen,et al.  Fuzzy clustering analysis for optimizing fuzzy membership functions , 1999, Fuzzy Sets Syst..

[23]  H. Lyu,et al.  Geological and hydrogeological environment in Tianjin with potential geohazards and groundwater control during excavation , 2018, Environmental Earth Sciences.

[24]  Shui-Long Shen,et al.  Foundation Pit Collapse on 8 June 2019 in Nanning, China: A Brief Report , 2019, Safety.

[25]  H. Lyu,et al.  Automatic control of groundwater balance to combat dewatering during construction of a metro system , 2021 .

[26]  Dongsheng Xu,et al.  A fuzzy evidential reasoning-based approach for risk assessment of deep foundation pit , 2020, Tunnelling and Underground Space Technology.

[27]  Tarik A. Rashid,et al.  Updating ground conditions and time-cost scatter-gram in tunnels during excavation , 2019, Automation in Construction.

[28]  Bin Wei,et al.  Is Basal Reinforcement Essential for Long and Narrow Subway Excavation Bottoming Out in Shanghai Soft Clay? , 2019, Journal of Geotechnical and Geoenvironmental Engineering.

[29]  G. Mavrotas,et al.  Determining objective weights in multiple criteria problems: The critic method , 1995, Comput. Oper. Res..

[30]  Yong Tan,et al.  Characterization of semi-top-down excavation for subway station in Shanghai soft ground , 2017 .

[31]  Samik Raychaudhuri,et al.  Introduction to Monte Carlo simulation , 2008, 2008 Winter Simulation Conference.

[32]  Fatemeh Jalayer,et al.  Probabilistic assessment of groundwater leakage in diaphragm wall joints for deep excavations , 2018 .

[33]  Francesca Pianosi,et al.  A simple and efficient method for global sensitivity analysis based on cumulative distribution functions , 2015, Environ. Model. Softw..

[34]  Jian-Hua Wang,et al.  Wall and Ground Movements due to Deep Excavations in Shanghai Soft Soils , 2010 .

[35]  Qihu Qian,et al.  Safety risk management of underground engineering in China: Progress, challenges and strategies , 2016 .

[36]  Fang-Le Peng,et al.  Remote-control technology performance for excavation with pneumatic caisson in soft ground , 2019 .

[37]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[38]  Ehsan Momeni,et al.  Reliability analysis and risk assessment of deep excavations using random-set finite element method and event tree technique , 2021, Transportation Geotechnics.

[39]  H. Lyu,et al.  Dewatering–Induced Building Settlement around a Deep Excavation in Soft Deposit in Tianjin, China , 2019, Journal of Geotechnical and Geoenvironmental Engineering.

[40]  Shui-Long Shen,et al.  Comprehensive environmental impact evaluation for concrete mixing station (CMS) based on improved TOPSIS method , 2021, Sustainable Cities and Society.

[41]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[42]  Youssef M A Hashash,et al.  Analysis of pillar stability of mined gas storage caverns in shale formations , 2015 .

[43]  Miodrag Brzaković,et al.  Human factor risk management procedures applied in the case of open pit mine , 2021 .

[44]  Peter E.D. Love,et al.  Predicting Safety Risks in Deep Foundation Pits in Subway Infrastructure Projects: Support Vector Machine Approach , 2017, J. Comput. Civ. Eng..

[45]  M. L. Menendez.  Shannon's entropy in exponential families: Statistical applications , 2000, Appl. Math. Lett..

[46]  Meng Li,et al.  An automated safety risk recognition mechanism for underground construction at the pre-construction stage based on BIM , 2018, Automation in Construction.

[47]  S. Shen,et al.  Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels. , 2020, Water research.

[48]  Tzung-Pei Hong,et al.  Finding relevant attributes and membership functions , 1999, Fuzzy Sets Syst..