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Geotechnical and Geological Engineering

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01.04.2014 | Original paper

Modeling and Interpretation of Pressuremeter Test Results with Artificial Neural Networks

verfasst von: Mohammad Emami, S. Shahaboddin Yasrobi

Erschienen in: Geotechnical and Geological Engineering | Ausgabe 2/2014

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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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American Society for Testing and Materials. Annual book of ASTM standards. D04-08, ASTM International Pub

Banimahd M, Yasrobi SS, Woodward PK (2005) Artificial neural network for stress–strain behavior of sandy soils: knowledge based verification. Comput Geotech 32:377–386CrossRef

Ghaboussi J, Sidarta DE (1998) New nested adaptive neural networks (NANN) for constitutive modeling. Comput Geotech 22:29–52CrossRef

Hagan MT, Menhaj MB (1994) Training feedforward networks with the marquartdt algorithm. IEEE Trans Neural Netw 5(6):989–992CrossRef

Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing, Boston, MA

Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366CrossRef

Lee SJ, Lee SR, Kim YS (2003) An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation. Comput Geotech 30:489–503CrossRef

Lu M, Abourizk SM, Hermann UH (2001) Sensitivity analysis of neural networks in spool fabrication productivity studies. J Comp Civil Eng 15(4):299–308CrossRef

Moosavi M, Yazdanpanah MJ, Doostmohammadi R (2006) Modeling the cyclic swelling pressure of mudrock using artificial neural networks. Eng Geol 87:178–194CrossRef

Nabney T (1999) Efficient training of RBF networks for classification. Proc 9th ICANN 1:210–215

Shahin MA, Jaksa MB (2005) Neural network prediction of pullout capacity of marquee ground anchors. Comput Geotech 32:153–163CrossRef

Shahin MA, Jaksa MB, Maier HR (2001) Department of Civil and Environmental Engineering, Adelaide University. Artificial neural network applications in geotechnical engineering

Titel: Modeling and Interpretation of Pressuremeter Test Results with Artificial Neural Networks
verfasst von: Mohammad Emami
S. Shahaboddin Yasrobi
Publikationsdatum: 01.04.2014
Verlag: Springer International Publishing
Erschienen in: Geotechnical and Geological Engineering / Ausgabe 2/2014
Print ISSN: 0960-3182
Elektronische ISSN: 1573-1529
DOI: https://doi.org/10.1007/s10706-013-9720-9

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