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The use of recycled tire rubber in asphalt pavements to improve the overall performance, economy, and sustainability of pavements has gained considerable attention over the last few decades. Several studies have indicated that recycled tire rubber can reduce the permanent deformation of flexible pavements and enhance its resistance to rutting, reduce pavement construction and maintenance costs, and improve the resistance to fatigue damage. This paper provides a systematic and critical overview of the research on and practice of using recycled tire rubber in asphalt pavements in terms of engineering properties, performance, and durability assessment. This critical analysis of the state-of-the-art should enhance the understanding of using recycled tire rubber in asphalt pavements, define pertinent recommendations, identify knowledge gaps, and highlight the need for concerted future research.
Saud Alfayez; Ahmed Suleiman; Moncef Nehdi. Recycling Tire Rubber in Asphalt Pavements: State of the Art. Sustainability 2020, 12, 9076 .
AMA StyleSaud Alfayez, Ahmed Suleiman, Moncef Nehdi. Recycling Tire Rubber in Asphalt Pavements: State of the Art. Sustainability. 2020; 12 (21):9076.
Chicago/Turabian StyleSaud Alfayez; Ahmed Suleiman; Moncef Nehdi. 2020. "Recycling Tire Rubber in Asphalt Pavements: State of the Art." Sustainability 12, no. 21: 9076.
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.
Ahmed Ramadan Suleiman; Moncef L. Nehdi. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network. Materials 2017, 10, 135 .
AMA StyleAhmed Ramadan Suleiman, Moncef L. Nehdi. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network. Materials. 2017; 10 (2):135.
Chicago/Turabian StyleAhmed Ramadan Suleiman; Moncef L. Nehdi. 2017. "Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network." Materials 10, no. 2: 135.