Abstract
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.
Highlights
Concrete is considered the world’s most widely used construction material
The generalization capacity of the genetic algorithm–artificial neural network (GA–artificial neural networks (ANNs)) model was evaluated on randomly selected test data (15% of the original database), which was unfamiliar to the mode and not previously presented
The generalization capacity of the genetic algorithms (GA)–ANN model was evaluated on randomly selected test data (15% of the original database), which was unfamiliar to the mode and not previously in the training elevenThe input testing data weredata introduced to the GA–ANN
Summary
Concrete is considered the world’s most widely used construction material. it is cost-effective and able to carry relatively high compressive loads, concrete is susceptible to micro-cracks, which can jeopardize the durability of civil infrastructure, inflicting multibillion dollar losses in premature degradation. Different mechanisms are responsible for autogenous (or intrinsic) self-healing in concrete, including (a) further hydration of anhydrous cement or cementitious minerals; (b) carbonation of calcium hydroxide; (c) expansion of the hydrated cement due to the swelling of calcium silicate hydrate; and (d) precipitation of impurities from the ingress water and spalling of loose concrete particles in cracks [13,14,15,16]. Wiktor and Jonkers [27] studied the effect of combining bacteria spores and calcium lactate in concrete They found that the combined effect can significantly enhance the concrete’s ability to self-heal its cracks independently. According to Adeli [29], ANNs offer a reliable tool that can model and predict complex problems It basically consists of computational devices inspired by biological learning in the brain. In the present study, the feasibility of using a hybrid genetic algorithm–artificial neural network (GA–ANN) for predicting the autogenous self-healing in concrete is investigated
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