A novel framework integrating downhole array data and site response analysis to extract dynamic soil behavior

Seismic site response analysis is commonly used to predict ground response due to local soil effects. An increasing number of downhole arrays are deployed to measure motions at the ground surface and within the soil profile and to provide a check on the accuracy of site response analysis models. Site response analysis models, however, cannot be readily calibrated to match field measurements. A novel inverse analysis framework, self-learning simulations (SelfSim), to integrate site response analysis and field measurements is introduced. This framework uses downhole array measurements to extract the underlying soil behavior and develops a neural network-based constitutive model of the soil. The resulting soil model, used in a site response analysis, provides correct ground response. The extracted cyclic soil behavior can be further enhanced using multiple earthquake events. The performance of the algorithm is successfully demonstrated using synthetically generated downhole array recordings.

[1]  G Pande,et al.  Finite elements with artificial intelligence , 2002 .

[2]  H. Bolton Seed,et al.  An analysis of ground motions during the 1957 San Francisco earthquake , 1968 .

[3]  S. Elachachi,et al.  An identification procedure of soil profile characteristics from two free field acclerometer records , 2005 .

[4]  Musharraf Zaman,et al.  Modeling of soil behavior with a recurrent neural network , 1998 .

[5]  Youssef M A Hashash,et al.  Systematic update of a deep excavation model using field performance data , 2003 .

[6]  Jamshid Ghaboussi,et al.  Autoprogressive training of neural network constitutive models , 1998 .

[7]  Anil K. Chopra,et al.  Dynamics of Structures: Theory and Applications to Earthquake Engineering , 1995 .

[8]  K. Wen,et al.  Strong Ground Motion in the Taipei Basin from the 1999 Chi-Chi, Taiwan, Earthquake , 2005 .

[9]  Amr S. Elnashai,et al.  Modeling of Hysteretic Behavior of Beam -Column Connections Based on Self -Learning Simulation , 2006 .

[10]  Duhee Park,et al.  Evaluation of seismic site factors in the Mississippi Embayment. I. Estimation of dynamic properties , 2005 .

[11]  Youssef M A Hashash,et al.  Non-linear one-dimensional seismic ground motion propagation in the Mississippi embayment , 2001 .

[12]  Imad A. Basheer,et al.  Selection of Methodology for Neural Network Modeling of Constitutive Hystereses Behavior of Soils , 2000 .

[13]  Ahmed Elgamal,et al.  Dynamic Soil Properties, Seismic Downhole Arrays and Applications in Practice , 2001 .

[14]  Rui Zhao,et al.  Stress-Strain Modeling of Sands Using Artificial Neural Networks , 1995 .

[15]  J H Garrett,et al.  KNOWLEDGE-BASED MODELLING OF MATERIAL BEHAVIOUR WITH NEURAL NETWORKS , 1991 .

[16]  Dayakar Penumadu,et al.  Triaxial compression behavior of sand and gravel using artificial neural networks (ANN) , 1999 .

[17]  M. P. Romo,et al.  The Mexico Earthquake of September 19, 1985—Relationships between Soil Conditions and Earthquake Ground Motions , 1988 .

[18]  G. N. Pande,et al.  On self-learning finite element codes based on monitored response of structures , 2000 .

[19]  Ahmed Elgamal,et al.  Lotung Downhole Array. II: Evaluation of Soil Nonlinear Properties , 1995 .

[20]  Jamshid Ghaboussi,et al.  Constitutive modeling of geomaterials from non-uniform material tests , 1998 .

[21]  James H. Garrett,et al.  Knowledge-Based Modeling of Material Behavior with Neural Networks , 1992 .

[22]  Steven D. Glaser,et al.  System identification estimation of soil properties at the Lotung site , 2000 .