الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Slump and compressive strength of concrete are commonly used criteria in evaluating fresh and hardened concrete. Accordingly, forecasting of such criteria is important for the quality assurance of the produced concrete. In this study, a Neural Network (NN) model is developed to forecast concrete compressive strength and slump for the ready mixes in Egypt. The Artificial Neural Network (ANN) model is developed, trained and tested using 1000 different concrete samples gathered from different ready mixes batch plants distributed all over the Arab Republic of Egypt as laboratory results from different construction projects using the same type of construction materials.
Important parameters that have noticeable effect on the compressive strength and slump are identified and used as the inputs for the ANN model. The developed model can be used either to forecast the compressive strength and slump for a given batch or to estimate the different components to achieve a targeted compressive strength after seven and twenty-eight days. To verify the results of the ANNs model, seventeen concrete samples are prepared and tested at laboratory and the same components of the mixes are used to forecast the strength and slump using the developed ANN model. The Root Mean Square Error (RMSE) of the results for the slump and the compressive strength after seven and 28 days equal 3.8, 1.79 and 3.05, respectively. These results showed the ability of the developed ANNs, as an effective tool, to forecast and estimate the compressive strength and slump of concrete in Egypt. The analysis of the test results leads to the conclusion that this idea can be used for the development of valid systems for specifications and standards.