Recurrent and Constructive‐Algorithm Networks For Sand Behavior Modeling

Romo, Miguel P.; García, Silvia Núñez; Mendoza, Manuel J.; Taboada-Urtuzuástegui, Vı́ctor M.

doi:10.1061/(asce)1532-3641(2001)1:4(371)

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…The database consisting of case studies of 23 dry and 23 wet slopes with 29 failed and 17 stable slopes, available in Sah et al (1994), is considered. The input data consist of parameters like height of slope H (m), unit weight γ (kN/m 3 ), cohesion C (kPa), internal friction angle φ ( ), slope angle β ( ), and pore pressure parameters r u .…”

Section: De Neural Networkmentioning

confidence: 99%

Artificial Neural Networks in Geotechnical Engineering

Das

2013

Metaheuristics in Water, Geotechnical and Transport Engineering

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Section: De Neural Networkmentioning

confidence: 99%

Artificial Neural Networks in Geotechnical Engineering

Das

2013

Metaheuristics in Water, Geotechnical and Transport Engineering

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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%

“…An "intelligent shell" created over the data base using ANNs of special construction assures a search for various, hidden correlation among the data. We quote some papers that account for this very important application of ANNs: [19,31].…”

Section: 2mentioning

confidence: 99%

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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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“…Over the last decade, alternate procedures to mathematically-based such as Neural Networks (NNs) and Neuro Fuzzy Systems (NFS) have been successfully used to study geotechnical problems (i.e., Ghaboussi et al 1991;Ghaboussi and Sidarta 1998;Romo 1999;Romo et al 2001;García et al 2000;Rahman 2002;Garcia et al 2007). These studies have shown that NNs and NFS are trustworthy alternatives to presently-used procedures.…”

Section: Introductionmentioning

confidence: 99%

Rockfill Strength Evaluation Using Cascade Correlation Networks

García

Romo

2008

Geotech Geol Eng

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Performing comprehensive laboratory test programs to estimate rockfill strength for rockfill dam projects is a lengthy and onerous task because of the large sample-size. Accordingly, it has become a common practice to carry out limited experimental investigation, and extrapolate the results to the expected conditions in actual embankments. A number of investigators have established a function of the type s = ar b , where s and r are the shear and normal stresses, respectively, and the constants a and b, which result from a fitting procedure, have no physical meaning. Results of laboratory tests on a variety of rockfills have shown that in addition to effective confining stresses the relative density, uniformity coefficient, maximum particle size and particle-breaking load influence rockfill strength. Thus these parameters must be included in any function for computing rockfill strength. Other parameters, whose influence is understood partially, are not included here. Given the non linearmultidimensional nature of the problem, in this paper a neuronal procedure is developed. This approach takes into account the influence of each of the parameters mentioned before. The network used in this article was defined after comparing the results obtained with a variety of algorithms. After several attempts, the Cascade Correlation Network (CCN) was found to yield most accurate strength predictions.

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Recurrent and Constructive‐Algorithm Networks For Sand Behavior Modeling

Cited by 17 publications

References 10 publications

Artificial Neural Networks in Geotechnical Engineering

Artificial Neural Networks in Geotechnical Engineering

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

Rockfill Strength Evaluation Using Cascade Correlation Networks

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