A Study on the Application of Artificial Neural Networks on Green Self Consolidating Concrete (SCC) under Hot Weather

2016 ◽  
Vol 677 ◽  
pp. 254-259 ◽  
Author(s):  
Mohamed Al Khatib ◽  
Samer Al Martini
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fly Ash ◽  
Ambient Temperature ◽  
Back Propagation ◽  
Self Consolidating Concrete ◽  
Feed Forward Back Propagation ◽  
Slump Flow ◽  
Ann Models ◽  
Hot Weather

Self-consolidating concrete (SCC) has recently drawn attention to the construction industry in hot weather countries, due to its high fresh and mechanical properties. The slump flow is routinely used for quality control of SCC. Experiments were conducted by the current authors to investigate the effects of hot weather conditions on the slump flow of SCC. Self-consolidating concrete mixtures were prepared with different dosages of fly ash and superplasticizer and under different ambient temperatures. The results showed that the slump flow of SCC is sensitive to changes in ambient temperature, fly ash dosage, and superplasticizer dosage. In this paper, several artificial neural networks (ANNs) were employed to predict the slump flow of self-consolidating concrete under hot weather. Some of the data used to construct the ANNs models in this paper were collected from the experimental study conducted by the current authors, and other data were gathered from literature. Various parameters including ambient temperature and mixing time were used as inputs during the construction of ANN models. The developed ANN models employed two neural networks: the Feed-Forward Back Propagation (FFBP) and the Cascade Forward Back Propagation (CFBP). Both FFBP and CFBP showed good predictability to the slump flow of SCC mixtures. However, the FFBP network showed a slight better performance than CFBP, where it better predicted the slump flow of SCC than the CFBP network under hot weather. The results in this paper indicate that the ANNs can be employed to help the concrete industry in hot weather to predict the quality of fresh self-consolidating concrete mixes without the need to go through long trial and error testing program.Keywords: Self-consolidating concrete; Neural networks; Hot weather, Feed-forward back-propagation, Cascade-forward back propagation.

A trial of artificial neural networks for automatically estimating the age of fish

1999 ◽  
Vol 50 (1) ◽  
pp. 73 ◽  
Author(s):  
Simon G. Robertson ◽  
Alexander K. Morison
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Error Rates ◽  
Feed Forward Back Propagation ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Pixel Brightness ◽  
Age Estimates

Artificial neural networks (ANNs) have the potential to automate routine ageing of fish with the benefit of increased speed in processing, greater objectivity and repeatability of estimates, and a mechanism for quantifying uncertainty of age estimates. ANN models were tested as a means of objectively replicating the age estimates of an experienced human reader. Feed-forward back- propagation ANNs, with three layers of neurons (input, hidden and output), were trained to classify the age of previously aged samples of three temperate species. Three ANN structures, where the number of neurons in the hidden layer was varied, were tested for each species. Inputs to each ANN were pixel brightness values along transects across images of sectioned otoliths. The ANN predicted age-class membership by the position of the neuron in the output layer with the highest value. After training, at least one of the three ANN structures correctly classified the age of fish from unseen transects for two members of the Sparidae family Acanthopagrus butcheri and Pagrus auratus at an accuracy level approaching that of an expert reader. For a member of the Merlucciidae family, Macruronus novaezelandiae, however, which is a species with more complex otolith structure, error rates were high for all three ANN structures tested.

Fresh, Durability and Mechanical Behavior of Self Consolidating Concrete Incorporating Fly Ash and Admixture under Hot Weather

2021 ◽  
Vol 904 ◽  
pp. 453-457
Author(s):  
Samer Al Martini ◽  
Reem Sabouni ◽  
Abdel Rahman Magdy El-Sheikh
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Water Absorption ◽  
Weather Conditions ◽  
Chloride Penetration ◽  
The Self ◽  
Fresh Properties ◽  
Self Consolidating Concrete ◽  
Slump Flow ◽  
Hot Weather

The self-consolidating concrete (SCC) become the material of choice by concrete industry due to its superior properties. However, these properties need to be verified under hot weather conditions. The paper investigates the behavior of SCC under hot weather. Six SCC mixtures were prepared under high temperatures. The SCC mixtures incorporated polycarboxylate admixture at different dosages and prolonged mixed for up to 2 hours at 30 °C and 40 °C. The cement paste was replaced with 20% of fly ash (FA). The fresh properties were investigated using slump flow, T50, and VSI tests. The compressive strength was measured at 3, 7, and 28 days. The durability of SCC mixtures was evaluated by conducting rapid chloride penetration and water absorption tests.

scholarly journals Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time

2013 ◽  
Vol 14 (1) ◽  
pp. 10-17
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Potable Water ◽  
Back Propagation ◽  
Ann Model ◽  
Data Set ◽  
Test Set ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Comparison Of The Results

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration (expressed as carbon and bromine) with an ANN model, i.e., capturing non-linear relationships among the water quality variables. Thus, carbon and bromine concentrations in potable water (the second chosen due to the toxicity of brominated trihalomethanes, THMs) were predicted using artificial neural networks (ANNs) based mainly on multi-layer perceptrons (MLPs) architecture. The chlorination (detention) time as much as 58 hours in Athens distributed network, comprised the input variables to the ANNs models. Moreover, to develop an ANN model for estimating carbon and bromine, the available data set was partitioned into training, validation and test set. In order to reach an optimum amount of hidden layers or nodes, different architectures were tested. The quality of the ANN simulations was evaluated in terms of the error in the validation sample set for the proper interpretation of the results. The calculated sum-squared errors for training, validation and test set were 0.056, 0.039 and 0.060 respectively for the best model selected. Comparison of the results showed that a two-layer feed-forward back propagation ANN model could be used as an acceptable model for predicting carbon and bromine contained in potable water THMs.

Stream Flow Modeling of River Swat Using Regression and Artificial Neural Networks (ANNs) Techniques

2011 ◽  
Vol 255-260 ◽  
pp. 679-683 ◽  
Author(s):  
Suleman Daud ◽  
Khan Shahzada ◽  
M. Tufail ◽  
M. Fahad
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Stream Flow ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Flow Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Intelligent Model

This paper presents the utility of Artificial Neural Networks and Regression analysis for the stream flow modeling in Swat River at five discharge gauge station. As an appropriate intelligent model is identified, Artificial Neural Networks (ANNs) is evaluated by comparing it to regression analysis and the available field data. ANNs namely feed forward back propagation neural network (FFBPNN) and regression analysis are introduced and implemented. The research study successfully compared the performance of the ANN and regression models that validated the utility of the two modeling techniques as effective applications to stream flow forecasting.

Implementation of Artificial Neural Networks for Prediction of Chloride Penetration in Concrete

2018 ◽  
Vol 7 (2.28) ◽  
pp. 47
Author(s):  
Osama Ahmed Mohamed ◽  
Modafar Ati ◽  
Waddah Al Hawat
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fly Ash ◽  
Silica Fume ◽  
Chloride Penetration ◽  
Critical Parameters ◽  
Experimental Program ◽  
Fine Aggregate ◽  
Ann Models ◽  
Artificial Neural

Artificial Neural Networks (ANN) has received a great attention from researchers in previous decade to predict different aspect of engineering problems. The aim of this research is to present an implementation of ANN to predict the Chloride penetration of self-consolidating concrete (SCC), containing various amounts of cement replacement minerals including fly ash, silica fume, and slag.  The ability of concrete to resist chloride penetration is measured using Rapid Chloride Penetration (RCP) test through an experimental program. One- and two-layer ANN models were developed by controlling the critical parameters affecting chloride penetration to predict the results of RCP test.  The ANN models were developed using various parameters including ratio of water-to-binder (w/b), course aggregate, fine aggregate, fly ash, and silica fume. It was shown that the prediction accuracy of ANN models was sensitive to combinations of learning rate and momentum. Data used to train and test the ANN were obtained through an experimental program conducted by the authors.  

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