Evaluation of liquefaction potential of soil deposits using artificial neural networks


Hanna A. M. , Ural D., Saygili G.

ENGINEERING COMPUTATIONS, cilt.24, ss.5-16, 2007 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 24
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1108/02644400710718547
  • Dergi Adı: ENGINEERING COMPUTATIONS
  • Sayfa Sayıları: ss.5-16

Özet

Purpose - In the literature, several empirical methods can be found to predict the occurrence of nonlinear soil liquefaction in soil layers. These methods are limited to the seismic conditions and the parameters used in developing the model. This paper seeks to present General Regression Neural Network (GRNN) model that addresses the collective knowledge built in simplified procedure. Design/methodology/approach - The GRNN model incorporates the soil and seismic parameters of the region. It was developed in four phases; identification, collection, implementation, and verification. The data used consisted of 3,895 case records, mostly from the cone penetration test (CPT) results produced from the two major earthquakes that took place in Turkey and Taiwan in 1999. The case records were divided randomly into training, testing and validation datasets. Soil liquefaction decision in terms of seismic demand and seismic capacity is determined by the stress-based method and strain-based method, and further tested with the well-known Chinese criteria. Findings - The results produced by the proposed GRNN model explore effectively the complex relationship between the soil and seismic input parameters and further forecast the liquefaction potential with an overall success ratio of 94 percent. Liquefaction decisions were further validated by the SPT, confirming the viability of the SPT-to-CPT data conversion, which is the main limitation of most of the simplified methods. Originality/value - The proposed GRNN model provides a viable tool to geotechnical engineers to predict seismic condition in sites susceptible to liquefaction. The model can be constantly updated when new data are available, which will improve its predictability.