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Open AccessArticle

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

Department of Civil and Environmental Engineering, Western University, London, ON N6A 5B9, Canada
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Author to whom correspondence should be addressed.
Academic Editor: Nele De Belie
Materials 2017, 10(2), 135; https://doi.org/10.3390/ma10020135
Received: 17 November 2016 / Revised: 1 February 2017 / Accepted: 2 February 2017 / Published: 7 February 2017
(This article belongs to the Special Issue Self-Healing Concrete)
This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. View Full-Text
Keywords: autogenous; self-healing; crack width; artificial neural network; genetic algorithm autogenous; self-healing; crack width; artificial neural network; genetic algorithm
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MDPI and ACS Style

Ramadan Suleiman, A.; Nehdi, M.L. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network. Materials 2017, 10, 135.

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