Intelligent identification of the Lattice Discrete Particle Model (LDPM) parameters using neural networks

Bhanot, Naina
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Alnaggar, Mohammed
Symans, Michael D.
O'Rourke, Michael J.
Issue Date
Civil engineering
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This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
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Such comprehensive models are governed by a number of model parameters. The available experimental data does not provide complete information to calibrate these models, which leads to an inaccurate estimation of the parameters. An identification system that uses the limited available data to identify the governing model parameters is thus necessary to calibrate such comprehensive models. The use of artificial intelligence as a tool for parameter identification has gained popularity over the last few decades. Artificial Neural Network (ANN) has been used in the past to predict fracture parameters. However, ANN has not been used as a parameter identification tool for comprehensive models such as the LDPM. The application of ANN for approximating functions between input and desired output variables has been advantageously used in this research to identify the LDPM parameters. A multi-step procedure is proposed to identify the LDPM parameters, with limited information about the concrete properties used in the experiment, using a feed-forward backpropagation neural network. The missing concrete properties are estimated based on ACI Standards and relevant experimental literature.
May 2016
School of Engineering
Dept. of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, Troy, NY
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