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

Abstract

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.

Effect of Freeze–Thaw Cycling on the Failure of Fibre-Cement Boards, Assessed Using Acoustic Emission Method and Artificial Neural Network

“…The experiments consisted of subjecting fibre-cement board specimens to 1 and 10 freeze–thaw cycles and then investigating them under three-point bending by means of the acoustic emission (AE) method. Artificial intelligence in the form of an artificial neural network [13] was employed to analyse the experimental results. Previous studies by the authors [11,12,14,15] presented the assessment of the effect of freeze–thaw cycling based solely on bending strength to be inadequate.…”

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“…An advantage of the artificial neural network is finding of a relationship between independent variables and a dependent variable even if the relationship is highly nonlinear [28]. As is stated in [29], the artificial neural network approximates a dependence between variables much better than any theoretical method.…”

Correlation between Cone Penetration Test parameters, soil type, and soil liquidity index using long short-term memory neural network

“…The success of this neural network is based on the fact that it is a very good approximator of a function or an operator. The best approximation of the values of this functional relation can be found by training based on examples of approximate functional dependence (see [2]). In [4], CPTU test parameters with auxiliary data were used to detect engineering parameters, for example, overconsolidation ratio (OCR), K o , M, c u .…”