Liquefaction Prediction Using Rough Set Theory

Evaluation of liquefaction is one of the most important issues in geotechnical engineering. Liquefaction prediction depends on many factors and the relationship between these factors is non-linear and complex. Different methods have been proposed by different authors for liquefaction prediction. These methods are mostly based on statistical approaches and neural network. In this paper a new approach based on Rough Set data mining procedure is presented for liquefaction prediction. The Rough set theory is a mathematical approach for analysis of imperfect knowledge or unclear description of objects. In this approach the decision rules are derived from conditional attributes in Rough Set analysis and the results are compared with actual field observations. The results of this study indicate that using this method can be helpful for liquefaction prediction and can reduce unnecessary costs in site investigation process.

[1]  Mihailo D. Trifunac.  Empirical criteria for liquefaction in sands via standard penetration tests and seismic wave energy , 1995 .

[2]  Russell A. Green,et al.  Energy-Based Evaluation and Remediation of Liquefiable Soils , 2004 .

[3]  Mohammad Hassan Baziar,et al.  Assessment of liquefaction triggering using strain energy concept and ANN model: Capacity Energy , 2007 .

[4]  A. Hanna,et al.  Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data , 2007 .

[5]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[6]  M Arabani,et al.  Water treatment plant site location using rough set theory , 2016, Environmental Monitoring and Assessment.

[7]  Shun-Chieh Hsieh,et al.  Energy-based probabilistic evaluation of soil liquefaction , 2005 .

[8]  I. M. Idriss,et al.  SIMPLIFIED PROCEDURE FOR EVALUATING SOIL LIQUEFACTION POTENTIAL , 1971 .

[9]  Terri L. Moore,et al.  Regression Analysis by Example , 2001, Technometrics.

[10]  Mahyar Arabani,et al.  APPLICATION OF ROUGH SET THEORY AS A NEW APPROACH TO SIMPLIFY DAMS LOCATION , 2006 .

[11]  H. Bolton Seed,et al.  Ground Motions and Soil Liquefaction During Earthquakes , 1982 .

[12]  Jun Wang,et al.  Fuzzy neural network models for liquefaction prediction , 2002 .

[13]  Timothy D. Stark,et al.  Liquefaction Resistance Using CPT and Field Case Histories , 1995 .

[14]  Thomas Bräunl,et al.  A framework of adaptive T-S type Rough-Fuzzy Inference Systems (ARFIS) , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[15]  Sandra Maurer,et al.  Ground Motions And Soil Liquefaction During Earthquakes , 2016 .

[16]  Sia Nemat-Nasser,et al.  Energy dissipation in inelastic flow of saturated cohesionless granular media , 1994 .

[17]  石原 研而,et al.  SAND LIQUEFACTION IN HOLLOW CYLINDER TORSION UNDER IRREGULAR EXCITATION , 1975 .

[18]  H. Bolton Seed,et al.  Closure of "Soil Liquefaction and Cyclic Mobility Evaluation for Level Ground during Earthquakes" , 1979 .

[19]  Lale Özbakir,et al.  Generating prediction rules for liquefaction through data mining , 2009, Expert Syst. Appl..

[20]  F. Y. Yokel,et al.  Prediction of pore water pressure buildup and liquefaction of sands during earthquakes by the cyclic strain method , 1982 .

[21]  Lashteh Neshaei,et al.  Rough sets theory in site selection decision making for water reservoirs , 2010 .

[22]  Robert E. Kayen,et al.  Assessment of Liquefaction Potential during Earthquakes by Arias Intensity , 1997 .

[23]  Mahyar Arabani,et al.  EVALUATING THE PARAMETERS AFFECTING URBAN TRIP-GENERATION , 2007 .

[24]  Jia-Ruey Chang,et al.  Rough Set Theory in Pavement Maintenance Decision , 2009, ICIC.