Predicting the compressive strength and slump of high strength concrete using neural network |
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| Affiliation: | 1. Civil Engineering Department, University of Gaziantep, Gaziantep, Turkey;2. Technical Programs Department, Kilis MYO, University of Gaziantep, Kilis, Turkey;3. Civil Engineering Department, University of Sakarya, Sakarya, Turkey;4. Civil and Environmental Engineering, University of Iowa, Iowa City, IA, USA;1. College of Civil Engineering, Nanjing Tech University, Nanjing 211816, China;2. State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, China;3. School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA 5005, Australia;4. School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China;1. Department of Civil and Environmental Engineering, Incheon National University, Republic of Korea;2. Incheon Disaster Prevention Research Center, Incheon National University, Republic of Korea;3. Public Works and Civil Engineering Department, Mansoura University, Egypt;4. Department of Civil Engineering, National Institute of Technology Patna, India;1. Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall, West Lafayette, IN 47907-2051, USA;2. University of Mohaghegh Ardabili, Daneshgah Street, 56199-11367 Ardabil, Iran;3. Engineering Faculty, Civil Engineering Department, Firat University, 23119 Elazig, Turkey;1. Department of Civil Engineering, Government College of Technology, Coimbatore, India;2. Agni College of Technology, Chennai, India;3. Division of Structural Engineering, Anna University, Chennai, India;4. Department of Structural Engineering, Government College of Technology, Coimbatore, India |
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| Abstract: | High Strength Concrete (HSC) is defined as concrete that meets special combination of performance and uniformity requirements that cannot be achieved routinely using conventional constituents and normal mixing, placing, and curing procedures. HSC is a highly complex material, which makes modelling its behavior very difficult task. This paper aimed to show possible applicability of neural networks (NN) to predict the compressive strength and slump of HSC. A NN model is constructed, trained and tested using the available test data of 187 different concrete mix-designs of HSC gathered from the literature. The data used in NN model are arranged in a format of seven input parameters that cover the water to binder ratio, water content, fine aggregate ratio, fly ash content, air entraining agent, superplasticizer and silica fume replacement. The NN model, which performs in Matlab, predicts the compressive strength and slump values of HSC. The mean absolute percentage error was found to be less then 1,956,208% for compressive strength and 5,782,223% for slump values and R2 values to be about 99.93% for compressive strength and 99.34% for slump values for the test set. The results showed that NNs have strong potential as a feasible tool for predicting compressive strength and slump values. |
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