الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
In this study, three models were developed application of artificial neural networks (ANN) in civil engineering. The first model for estimating the compressive strength of sustainable concrete that contains various amounts of fly ash, silica fume, slag and steel fiber has been investigated. For the training of ANN models, an experimental data base (1410 concrete mixtures from earlier published papers) has been utilized. The ANN model parameter statistics R2 is 0.888, 0.93, 0.9 for training, validation and test steps and indicate that ANN model makes effective prediction for compressive strength of sustainable concrete. The second model estimates Reynold number(RN) and ultra-pulse velocity (UPV) of sustainable concrete which contains cement, sand, coarse aggregate, water, fly ash, silica fume, blast furnace slag (BFS) and superplasticizer and connect between them using artificial neural network (ANN) which made relations between them in a model. For 663 concrete mixtures from previous published papers (23 papers) at different curing times and test ages at 3, 7, 28, 90,180 days and make concrete mixes in the field to test the model. The ANN model parameter statistics R2 is 0.999, 0.999, and 0.998 for training, validation and test steps and produce that ANN model gives effective prediction for compressive strength of sustainable concrete. The third model estimates compressive strength, (RN) and (UPV) of sustainable concrete which contains various amounts of fly ash, silica fume and blast furnace slag (BFS) and connect between them using artificial neural network (ANN) which made relations between them in a model. A total of 645 data set were collected for concrete mixtures from previous published papers at different curing times and test ages at 3,7,28,90,180 days and make a model with nine inputs and three outputs, The ANN model parameter statistics R2 is 0.99, 0.99, 0.99 for training, validation and test steps and indicate that ANN model gives good prediction for compressive strength, RN and UPV of sustainable concrete incorporating industrial by-products pozzolanic materials.