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Development of Artificial Neural Network for the Fatigue Life Assessment of Self Compacting Concrete
Modern infrastructure needs to endure repeated loading for quick and sustainable development. Highway pavements, offshore supporting structures, bridge decks, machine foundations etc. are subjected to repeated loadings. This type of concrete structure deteriorates when subjected to cyclic loading. The heterogeneous nature of concrete combined with varying parameters of fatigue loading make the analysis complex and thereby produce approximate solution. As a result, a probabilistic approach is more suitable to this type of problem than a deterministic approach since variations and uncertainties exist. An Artificial Neural Network (ANN) can be used as a powerful computational tool that uses a probabilistic approach. The purpose of this study is to develop a generalized artificial neural network, which can predict the fatigue life of special concrete like Self Compacting Concrete (SCC). Several parameters affecting concrete fatigue life, such as material and fracture mechanics properties, are identified and are provided as input into the system. The created neural network is then trained using the available experimental data and validated. This developed model can therefore be used to assess the fatigue life of concrete with considerable accuracy.
Development of Artificial Neural Network for the Fatigue Life Assessment of Self Compacting Concrete
Modern infrastructure needs to endure repeated loading for quick and sustainable development. Highway pavements, offshore supporting structures, bridge decks, machine foundations etc. are subjected to repeated loadings. This type of concrete structure deteriorates when subjected to cyclic loading. The heterogeneous nature of concrete combined with varying parameters of fatigue loading make the analysis complex and thereby produce approximate solution. As a result, a probabilistic approach is more suitable to this type of problem than a deterministic approach since variations and uncertainties exist. An Artificial Neural Network (ANN) can be used as a powerful computational tool that uses a probabilistic approach. The purpose of this study is to develop a generalized artificial neural network, which can predict the fatigue life of special concrete like Self Compacting Concrete (SCC). Several parameters affecting concrete fatigue life, such as material and fracture mechanics properties, are identified and are provided as input into the system. The created neural network is then trained using the available experimental data and validated. This developed model can therefore be used to assess the fatigue life of concrete with considerable accuracy.
Development of Artificial Neural Network for the Fatigue Life Assessment of Self Compacting Concrete
Lecture Notes in Civil Engineering
Marano, Giuseppe Carlo (editor) / Rahul, A. V. (editor) / Antony, Jiji (editor) / Unni Kartha, G. (editor) / Kavitha, P. E. (editor) / Preethi, M. (editor) / Rabin Gani, B. (author) / Simon, Keerthy M. (author) / Bharati Raj, J. (author)
International Conference on Structural Engineering and Construction Management ; 2022 ; Angamaly, India
2022-10-30
10 pages
Article/Chapter (Book)
Electronic Resource
English
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