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Predicting Compressive Strength of High Strength Concrete Using Artificial Neural Networks

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dc.contributor.author Gaily, Ahmed Mohammed
dc.contributor.author Supervisor, - Mahmoud Ahmad Mohammed Khogali
dc.date.accessioned 2018-03-13T11:31:48Z
dc.date.available 2018-03-13T11:31:48Z
dc.date.issued 2017-08-10
dc.identifier.citation Gaily, Ahmed Mohammed . Predicting Compressive Strength of High Strength Concrete Using Artificial Neural Networks / Ahmed Mohammed Gaily ; Mahmoud Ahmad Mohammed Khogali .- Khartoum: Sudan University of Science and Technology, college of Engineering, 2017 .- 93p. :ill. ;28cm .- M.Sc. en_US
dc.identifier.uri http://repository.sustech.edu/handle/123456789/20487
dc.description Thesis en_US
dc.description.abstract The advancement of materials technology has led to production of higher grades of concrete strength. The application of High Strength Concrete “HSC” in civil engineering structures has increased significantly. The principal reasons for its popularity are economy, superior strength, increased stiffness and great durability. The production of HSC requires better quality for the basic material and additional special materials such as: silica fume, Fly Ash, super plasticizer and extra quality control procedures. Obtaining test values (after 28-day) of the strength of concrete takes time and high cost, for these reasons Artificial Neural Networks (ANNs) were used to predict compressive strength of High Strength Concrete (HSC). Artificial Neural Networks (ANNs) modeling technique was used in this research to predict compressive strength of High Strength Concrete “HSC”. One ANN Model was built of three layers feed-forward with back propagation system and consists of seven input nodes, seven hidden layer nodes and one output node. The ANN Model was developed for predicting compressive strength of cubes at the age 28 days by the Optimization Modeling System "Solver" in the Microsoft Office Excel 2010 and using 193 set of actual and reliable data collected from previous studies. Strength of concrete is tested after 28 days (cube test). The studied parameters were matched with literature, and were found to be in a good agreement. Furthermore the ANN was used to study the impact of factors influencing the compressive strength. As a result, compressive strength values of High Strength Concretes can be predicted in the multilayer feed forward artificial neural networks model without attempting any experiments in a quite short period of time with tiny error rates. It is found that Artificial Neural Networks is a powerful tool in solving problems containing multiple variables, and has a good ability in performing parameters analysis. en_US
dc.description.sponsorship Sudan University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science and Technology en_US
dc.subject Civil Engineering en_US
dc.subject Artificial Neural Networks en_US
dc.subject High Strength Concrete en_US
dc.title Predicting Compressive Strength of High Strength Concrete Using Artificial Neural Networks en_US
dc.title.alternative التنبؤ بقوة الضغط للخرسانة عالية القوة باستخدام الخلايا العصبية الإصطناعية en_US
dc.type Thesis en_US


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