Case Studies with Innovative Technology


Inverse Formulation for Burger Model Parameter Estimation

Arindam Dey1 and Prabir K. Basudhar2

1Department of Civil Engineering, Indian Institute of Technology Guwahati,
Guwahati, Assam, 781039, India

2Department of Civil Engineering, Indian Institute of Technology Kanpur,
Kanpur, Uttar Pradesh, 208016, India


This paper reports the development of a generalized inverse analysis formulation for the parameter estimation of four-parameter Burger model used to represent the time-dependent behavior of viscoelastic soil. A brief account of the suitability of the model that makes it a promising predictive tool in the field of geotechnical engineering has been described. The various key issues related to the inverse analysis to determine model parameters from observational data have been addressed. Back-estimation of rheological parameters of the model is carried out by formulating the problem as one of mathematical programming after identification of the design vector, the objective function and the design constraints. Thereafter, the formulated constrained problem has been solved with the aid of fmincon: an in-built constrained optimization solver module available in MatLab. In order to gain experience and confidence on the developed methodology, a synthetic case study is considered wherein a settlement-time history has been generated using a known set of parameters and the efficacy of the inverse analysis technique is checked. Key issues such as the determination and setting up of bounds of the design variables and the stating-point dependency of the problem has been studied in detail and reported. The results revealed that the developed inverse analysis technique is quite efficient in back predicting the rheological parameters for the Burger model. It has been observed that the best result is obtained when the design variables are constrained by a lower bound with a full slack on the upper bound. No starting-point dependency is observed in the present study. The efficacy of the technique is supported by the fact that only 4 data-points from the settlement-time input is sufficient to back-predict the parameters, using which, a forward analysis, is able to generate and replicate the entire time-settlement history under consideration. The power of the developed technique has also been demonstrated here in by taking up two field case studies.

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