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Modeling and Interpretation of Pressuremeter Test Results with Artificial Neural Networks

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Abstract

In this paper, three types of artificial neural network (ANN) are employed to prediction and interpretation of pressuremeter test results. First, multi layer perceptron neural network is used. Then, neuro-fuzzy network is employed and finally radial basis function is applied. All applied networks have shown favorable performance. Finally, different models have been compared and network with the most outstanding performance in two stages is determined. Contrary to conventional behavioral models, models based neural network do not demonstrate the effect of input parameters on output parameters. This research is response to this need through conducting sensitivity analysis on the optimal structure of proposed models.

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Acknowledgments

The authors wish to thank the reviewers for their useful comments and suggestions.

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  1. Mohammad Emami

    Present address: , Unit 6, No. 16, 3th St., Kamali Boulvared, Ashrafi Esfehani, St. Pounak, P.O. Box 1476964171, Tehran, Iran

Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Tarbiat Modares University, Intersection of Jalale Ale Ahmad and Shahid Chamran Highways, P.O. Box 14155-143, Tehran, Iran

    Mohammad Emami & S. Shahaboddin Yasrobi

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  1. Mohammad Emami
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  2. S. Shahaboddin Yasrobi
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Correspondence to Mohammad Emami.

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Emami, M., Yasrobi, S.S. Modeling and Interpretation of Pressuremeter Test Results with Artificial Neural Networks. Geotech Geol Eng 32, 375–389 (2014). https://doi.org/10.1007/s10706-013-9720-9

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  • DOI: https://doi.org/10.1007/s10706-013-9720-9

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