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

2013 ◽  
Vol 61 (1) ◽  
pp. 39-50 ◽  
Author(s):  
M. Lefik
Keyword(s):  
Neural Network ◽  
Inverse Problem ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Civil Engineering ◽  
Constitutive Law ◽  
Finite Element Code ◽  
Hybrid Finite Element ◽  
Artificial Neural ◽  
Theoretical Results

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

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals Artificial Neural Network-Based Method for Seismic Analysis of Concrete-Filled Steel Tube Arch Bridges

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhen Liu ◽  
Shibo Zhang
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Seismic Analysis ◽  
Steel Tube ◽  
Arch Bridge ◽  
Concrete Filled Steel Tube ◽  
Cfst Arch Bridge ◽  
Artificial Neural

Seismic analysis of concrete-filled steel tube (CFST) arch bridge based on finite element method is a time-consuming work. Especially when uncertainty of material and structural parameters are involved, the computational requirements may exceed the computational power of high performance computers. In this paper, a seismic analysis method of CFST arch bridge based on artificial neural network is presented. The ANN is trained by these seismic damage and corresponding sample parameters based on finite element analysis. In order to obtain more efficient training samples, a uniform design method is used to select sample parameters. By comparing the damage probabilities under different seismic intensities, it is found that the damage probabilities of the neural network method and the finite element method are basically the same. The method based on ANN can save a lot of computing time.

Development of Surrogate Models of Orthopedic Screws to Improve Biomechanical Performance

2012 ◽  
pp. 138-159
Author(s):  
Ching-Chi Hsu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Objective Function ◽  
Bending Strength ◽  
Pullout Strength ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

An optimization approach was applied to improve the design of the lag screws used in double screw nails. However, finite element analyses with an optimal algorithm may take a long time to find the best design. Thus, surrogate methods, either artificial neural networks or multiple linear regressions, were used to substitute for the finite element models. The results showed that an artificial neural network method can accurately develop the objective functions of the lag screws for both the bending strength and the pullout strength. A multiple linear regression method can successfully develop the objective function of the lag screws for the pullout strength, but it failed to construct the objective function for the bending strength. The optimal design of the lag screws could be obtained using the artificial neural network method and genetic algorithms.

Multi-physics Analysis of Multi-chip LED Light Source Based on Finite Element Method and Artificial Neural Network

2019 ◽  
Vol 40 (6) ◽  
pp. 795-802 ◽  
Author(s):  
刘宏伟 LIU Hong-wei ◽  
牛萍娟 YU Dan-dan ◽  
郭 凯 NIU Ping-juan ◽  
张建新 ZHANG Zan-yun ◽  
王 闯 GUO Kai ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Light Source ◽  
Led Light ◽  
Artificial Neural ◽  
Element Method

Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Damage Detection ◽  
Ann Model ◽  
Element Analysis ◽  
Severity Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kertas kerja ini memaparkan kajian berkenaan keberkesanan Artificial Neural Network (ANN) dalam mengenal pasti kerosakan di dalam struktur. Data dari getaran seperti frekuensi semula jadi dan mod bentuk digunakan sebagai data masukan bagi ANN untuk meramalkan lokasi dan tahap kerosakan bagi struktur lantai. Analisis unsur terhingga (Finite Element Analysis) telah digunakan untuk memperoleh ciri–ciri dinamik bagi struktur–struktur rosak dan tidak rosak untuk ‘melatih’ model ‘neural network’. Senario kerosakan yang berbeza disimulasikan dengan mengurangkan kekukuhan elemen pada lokasi yang berbeza sepanjang struktur tersebut. Berbagai kombinasi data masukan bagi mod yang berbeza telah digunakan untuk memperolehi model ANN yang terbaik. Hasil kajian ini menunjukkan ANN mampu memberikan keputusan yang baik dalam meramal kerosakan pada struktur lantai tersebut. Kata kunci: Ramalan kerosakan struktur, Artificial Neural Network This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration–based data has been used as the input to ANN for location and severity prediction of damages in a slab–like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure. Key words: Structural damage detection, artificial neural network

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