Fuzzy Synthetic Evaluation of the Long-Term Health of Tunnel Structures

A tunnel is a coupled system of the surrounding rock and the supporting structure. The health status of a tunnel structure is complex and is influenced by various factors. In addition, these factors are coupled and interacted with each other, which calls for the linguistic description of the tunnel safety level. In this paper, we describe the health status of a highway tunnel structure in terms of four levels: safe; basically safe; potentially unsafe and unsafe. Based on the analysis of the safety characteristics of the tunnel structure and its proposed safety level, this research develops a multi-level fuzzy synthetic evaluation model for the long-term safety evaluation system of a tunnel structure. The Cang Ling Tunnel, which has embedded sensors to measure the stress values of the secondary lining and the contact pressure, is used as an example to study the proposed method. The results show that the structure of the entire Cang Ling Tunnel is in almost a safe condition under the current conditions, which is consistent with the actual operational situation.

[1]  X. W. Ye,et al.  Safety Monitoring of Railway Tunnel Construction Using FBG Sensing Technology , 2013 .

[2]  Rehan Sadiq,et al.  Translation of pipe inspection results into condition ratings using the fuzzy synthetic evaluation technique , 2006 .

[3]  I. L,et al.  SRC rock mass classification of tunnels under high tectonic stress excavated in weak rocks , 2012 .

[4]  Oreste S. Bursi,et al.  Structural monitoring for the cyclic behaviour of concrete tunnel lining sections using FBG sensors , 2016 .

[5]  Gangbing Song,et al.  Chattering-free fuzzy adaptive robust sliding-mode vibration control of a smart flexible beam , 2008 .

[6]  Yu Liu,et al.  Bridge Structural Health Evaluation Based on Multi-level Fuzzy Comprehensive Evaluation , 2011 .

[7]  G. Song,et al.  A Genetic Algorithm-based Two-phase Design for Optimal Placement of Semi-active Dampers for Nonlinear Benchmark Structure , 2010 .

[8]  Ai Min Yao,et al.  Fuzzy Comprehensive Evaluation of Open-Pit Slope Stability , 2014 .

[9]  Fang Qian,et al.  RESEARCH ON DESIGN OF SUBSEA TUNNEL STRUCTURAL HEALTH MONITORING , 2007 .

[10]  Gangbing Song,et al.  Innovative NARX recurrent neural network model for ultra-thin shape memory alloy wire , 2014, Neurocomputing.

[11]  T. Saaty.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[12]  Wang Bo.  Design and Application of Long-term Safety Monitoring System of Highway Tunnel under Complex Geological Conditions , 2013 .

[13]  Ying Lei,et al.  Structural Monitoring Techniques for the Largest Excavation Section Subsea Tunnel: Xiamen Xiang’an Subsea Tunnel , 2017 .

[14]  Zhang Zhongmiao,et al.  Application of structural health monitoring in Hangzhou Qiantang River tunnel , 2011, 2011 International Conference on Electric Technology and Civil Engineering (ICETCE).

[15]  Alexander Bucksch,et al.  STRUCTURAL MONITORING OF TUNNELS USING TERRESTRIAL LASER SCANNING , 2009 .

[16]  Yun Liu,et al.  Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring , 2014, Sensors.

[17]  Yunjie Liu,et al.  Extended Multi-Level Fuzzy Comprehensive Evaluation Model of Bridges Maintenance based on Fuzzy Set , 2012 .

[18]  L. L. Zhang,et al.  Optimal placement of sensors for structural health monitoring using improved genetic algorithms , 2004 .

[19]  Li Ming Wu,et al.  Fuzzy Comprehensive Evaluation Method and Application of Slope Stability , 2011 .

[20]  Pj Bennett,et al.  Optical fibre strain measurement for tunnel lining monitoring , 2010 .

[21]  V. U. Nguyen.  Rock mass classification using fuzzy sets , 1985 .

[22]  Daniel J. Inman,et al.  Impedance-Based Structural Health Monitoring with Artificial Neural Networks , 2000 .

[23]  Leang-San Shieh,et al.  A new neural network-based approach for self-tuning control of nonlinear SISO discrete-time systems , 2010, Int. J. Syst. Sci..

[24]  Weixu Wang,et al.  Using Geographically Weighted Poisson Regression for county-level crash modeling in California , 2013 .

[25]  Hans De Backer,et al.  Monitoring the Structural Response of the Liefkenshoek Rail Tunnel to Tidal Level Fluctuations , 2016 .

[26]  Xinping Yan,et al.  The research of optimal monitoring point placement for health monitoring of dredger based on analytic hierarchy process , 2010, 2010 Prognostics and System Health Management Conference.

[27]  Wei Zhang,et al.  Fuzzy Analytic Hierarchy Process Synthetic Evaluation Models for the Health Monitoring of Shield Tunnels , 2014, Comput. Aided Civ. Infrastructure Eng..

[28]  G. Önkal-Engin,et al.  Assessment of urban air quality in Istanbul using fuzzy synthetic evaluation , 2004 .

[29]  Luyu Li,et al.  Hybrid active mass damper (AMD) vibration suppression of nonlinear high‐rise structure using fuzzy logic control algorithm under earthquake excitations , 2011 .

[30]  Brian Veitch,et al.  RISK-BASED DECISION-MAKING FOR DRILLING WASTE DISCHARGES USING A FUZZY SYNTHETIC EVALUATION TECHNIQUE , 2004 .