Prediction of Compression Index of Saturated Clays Using Robust Optimization Model

Document Type: Regular Article

Authors

1 Department of Civil Engineering, Faculty of Engineering, University of Manisa Celal Bayar University

2 Department of Civil Engineering, Gonbad KavousUniversity, Gonbad, Iran

3 Department of Civil Engineering, Imam Khomeini International University

4 Department of Physics, Faculty of Science, University of Dokuz Eylul

Abstract

Compression index (Cc) of normally consolidated (NC) clays determined by the oedometer experiments is utilized for calculating the consolidation settlement of shallow foundations. The determination of the Cc from the tests takes much more time and so empirical correlations based on clay properties can be a suitable alternative for the prediction of settlement. However, uncertainty in the measurements of input parameters has always been a major concern. Robust optimization is very popular due to its computational tractability for many classes of uncertainty sets and problem types. Therefore, in this research, an innovative method based on robust optimization has been used to investigate the effect of such uncertainties. To achieve these, the results of 433 oedometer tests taken from geotechnical investigation locations in Mazandaran province of Iran have been used. Based on Frobenius norm of the data points, uncertainty definition is presented and examined against the correlation coefficients for several empirical models for predicting Cc value and thus optimum values are determined. The results in compare with previous models indicate the robust method is a better pattern recognition tool for datasets with degrees of uncertainty. The variation of the Cc values with soil properties, namely, water content (ω_n ), initial void ratio (e_o ), and liquid limit (LL), by considering different value of uncertainties (0, 5 and 10%) was considered and indicated that the effect of e_o is more than other two physical parameters (ω_n and LL). The best model performance during in deterministic valuation and considering uncertainty is further shown.

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