Constitutive modeling of geomaterials from non-uniform material tests

Sidarta, Djoni E.; Ghaboussi, Jamshid

doi:10.1016/s0266-352x(97)00035-9

Cited by 74 publications

(33 citation statements)

References 3 publications

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“…In [14][15][16][17] the non-linear constitutive behavior of the system has been represented by ANN and directly incorporated in a Finite Element (FE) code, while in [18] ANNs are used for the identification of the parameters of a constitutive law. In [15,[19][20][21][22][23][24][25][26][27]] interesting reviews of possible applications of ANNs in nonlinear mechanics can be found.…”

Section: 2mentioning

confidence: 99%

Some aspects of application of artificial neural network for numerical modeling in civil engineering

Lefik¹

2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. In order to obtain reliable results of computations in civil engineering, the numerical procedures that are used at the stage of design should be calibrated by comparison of the theoretical results with an observed behavior of previously modeled and then executed structures. The hybrid Finite Element code with an Artificial Neural Network inserted as a representation of a constitutive law, offers a possibility to adjust not only parameters of the constitutive relationships but also its qualitative form. Because of this, the representation of constitutive law by the ANN is presented in this paper. The constitutive data should be calibrated to fit well the observable values, measured in experiments. If the constitutive law is expressed by ANN, the inverse problem can be reduce to a training of the ANN inserted into the Finite Element code. An example of a solution of the inverse problem in calibration of constitutive law is presented. An identification of parameters of flow of pollutant in soils is described as another example of application of ANN in engineering.

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Section: 2mentioning

confidence: 99%

Some aspects of application of artificial neural network for numerical modeling in civil engineering

Lefik¹

2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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“…This model was identified using a series of triaxial compression experiments, and has the ability to simulate unload-reload cycles and accounts for the impact of grain size distributions. Sidarta and Ghaboussi [87] modeled the soil constitutive behavior when subjected to non-uniform stress conditions. The proposed neural network model was calibrated using finite element simulations of triaxial compression tests.…”

Section: Nonparametric Modelingmentioning

confidence: 99%

A survey of geotechnical system identification techniques

Oskay

Zeghal

2011

Soil Dynamics and Earthquake Engineering

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Identification and inverse problem techniques play an important role in the characterization and modeling of geotechnical systems. These techniques have been used in estimation of system parameters, model development and calibration, as well as simulation of earthquake ground motions. The recent availability of high quality seismic records of sites equipped with downhole accelerometer arrays led to a burgeoning of identification and inverse problem studies involving geotechnical systems. This paper presents a survey of system identification techniques and analyses of full-and small-scale soil systems, with an emphasis on geotechnical earthquake engineering problems.

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“…Ghaboussi et al [4], Hashash et al [5], Pande and Shin [6], Hashash et al [7], Shin and Pande [8], Sidarta and Ghaboussi [9], Haj-Ali et al [10], Ghaboussi et al [11], Ghaboussi and Sidarta [12]). Artificial neural networks are massively parallel assemblage of interacting units with simple functions called artificial neurons.…”

Section: Constitutive Modeling With Neural Networkmentioning

confidence: 99%