Implementation of Artificial Neural Network in Predicting the Mechanical Properties of Concrete at High Temperatures
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Concrete might be exposed to high temperatures caused by fire that adversely affect its mechanical properties. Therefore, the ability to predict the mechanical properties of concrete exposed to high temperatures is necessary. In this study, Artificial Neural Network (ANN) models were developed to assess the mechanical properties of concrete exposed to high temperatures. For this purpose, 728 experimental results were collected from the available literature to predict mechanical properties of concrete at high temperatures, including compressive strength, tensile strength, and modulus of elasticity. The input database contains the volumes of coarse aggregate, fine aggregate, water, cement, water-cement ratio, coarse aggregate type, percentage of supplementary cementitious materials as the cement replacement, temperatures, and test methods. The mechanical properties were defined as the output variable. Proposed models are in good agreement with the experimental data and can predict the mechanical properties of concrete exposed to high temperatures.
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Copyright © 2022 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2022
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ramziaraghi-implementationofartificialneuralnetworkin.pdf | 2023-05-05 | Public | Download |