Quantitative assessment of engineering geological suitability for multilayer Urban Underground Space

Abstract With the increasing exploitation of Urban Underground Space (UUS) increasingly in the cities of developing countries as result of urbanization, the near-surface UUS cannot satisfy the space capacity requirement. Urban Underground Infrastructure (UUI) in multilayer geologies is a solution to meet the need of rapid growing population. However, there is a lack of systematic assessment method for UUS multilayer geological conditions. In this paper, the multilayer UUS exploitation engineering geological suitability evaluation framework is presented. Fuzzy set Analytic hierarchy process and TOPSIS (FAHP-TOPSIS) is employed for the basic layer evaluation. Under the assumption of the Multilayer Number (MN) evolving 1–4, the Transferring Coefficient Matrix (TCM) is constructed as the multilayer effects basis. Combined with the basic layer evaluation and TCM, the UUS Geological Suitability Evaluation (UGSE) results are obtained. Meanwhile the geology analysis details containing the geological investigation, the impact factors and the hierarchy of factors, the fuzzy pair-wise comparison scales, in favor to the impact factors weights list completing, and the factors membership functions are developed to support UGSE. A study case of a Railway station area, in Central China, the UGSE simulation is conducted and the results are discussed. The present UUS geological suitability assessment frame can be applied to a comprehensive UUS suitability evaluation.

[1]  Henry C. W. Lau,et al.  A fuzzy analytic hierarchy process approach in modular product design , 2001, Expert Syst. J. Knowl. Eng..

[2]  Nikolai Bobylev,et al.  Mainstreaming sustainable development into a city's Master plan: A case of Urban Underground Space use , 2009 .

[3]  Huanqing Li,et al.  The Way to Plan a sustainable Deep City: From Economic and Strategic aspects , 2012 .

[4]  Chein-Shung Hwang,et al.  Using Cloud Model for Default Voting in Collaborative Filtering , 2011 .

[5]  H. Kunzi,et al.  Lectu re Notes in Economics and Mathematical Systems , 1975 .

[6]  Masoud Zare Naghadehi,et al.  A probabilistic systems methodology to analyze the importance of factors affecting the stability of rock slopes , 2011 .

[7]  Serkan Balli,et al.  Operating System Selection Using Fuzzy AHP and TOPSIS Methods , 2009 .

[8]  Mohammad Ataei,et al.  Ranking of geological risks in mechanized tunneling by using Fuzzy Analytical Hierarchy Process (FAHP) , 2015 .

[9]  A. Dziedzic,et al.  The methodology of a complex engineering-geological approach to establish a Geopark: case study of the Małopolska Vistula River Gorge , 2015 .

[10]  Ning Guo-min Engineering geological research on the underground space of Wuhan City , 2006 .

[11]  S. Pelizza,et al.  Subsurface geological-geotechnical modelling to sustain underground civil planning , 2008 .

[12]  Nicolas S. Holliman,et al.  Integration of regional to outcrop digital data: 3D visualisation of multi-scale geological models , 2009, Comput. Geosci..

[13]  Kari Rauhala,et al.  Underground space in land-use planning , 1998 .

[14]  Tetsuya Hanamura Japan's new frontier strategy: Underground space development , 1990 .

[15]  Ying-Ming Wang,et al.  Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment , 2006, Expert Syst. Appl..

[16]  Nikolai Bobylev SUSTAINABILITY AND VULNERABILITY ANALYSIS OF CRITICAL UNDERGROUND INFRASTRUCTURE , 2007 .

[17]  Miroslaw J. Skibniewski,et al.  A novel model for risk assessment of adjacent buildings in tunneling environments , 2013 .