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A platform for research: civil engineering, architecture and urbanism
Predicting the crushing strength of cold-bonded artificial aggregates by genetic algorithms
The need for environmental sustainability has a great impact in the recycling of waste materials. Using such wastes for the production of lightweight aggregate (LWA) has a promising future in terms of better performance characteristics provided to concrete structures due to the decrease in self-weight. One of the most important performance characteristics of the LWA is its crushing strength. There are various factors affecting the crushing strength of LWA. Type and amount of the waste material used in LWA production, curing type and the size of the aggregates are the main factors influencing the strength. In this study, crushing strength of the LWAs produced with cold bonding of F type fly ash (FA) and ground granulated blast furnace slag (GGBFS) was predicted by a genetic algorithm (GA) technique which is known as gene expression programming (GEP). GA, which is inspired by evolutionary biology such as inheritance, mutation, selection, crossover (recombination), is a search technique that has been used in computing for finding precise or approximate solutions to optimization or search problems. Since GEP model expresses solutions based on mathematical model, it will be very useful tool for engineers. The proposed model in the study well agreed the experimental results such that R2 values of 0.94 and 0.90 were achieved for the train and test data, respectively.
Predicting the crushing strength of cold-bonded artificial aggregates by genetic algorithms
The need for environmental sustainability has a great impact in the recycling of waste materials. Using such wastes for the production of lightweight aggregate (LWA) has a promising future in terms of better performance characteristics provided to concrete structures due to the decrease in self-weight. One of the most important performance characteristics of the LWA is its crushing strength. There are various factors affecting the crushing strength of LWA. Type and amount of the waste material used in LWA production, curing type and the size of the aggregates are the main factors influencing the strength. In this study, crushing strength of the LWAs produced with cold bonding of F type fly ash (FA) and ground granulated blast furnace slag (GGBFS) was predicted by a genetic algorithm (GA) technique which is known as gene expression programming (GEP). GA, which is inspired by evolutionary biology such as inheritance, mutation, selection, crossover (recombination), is a search technique that has been used in computing for finding precise or approximate solutions to optimization or search problems. Since GEP model expresses solutions based on mathematical model, it will be very useful tool for engineers. The proposed model in the study well agreed the experimental results such that R2 values of 0.94 and 0.90 were achieved for the train and test data, respectively.
Predicting the crushing strength of cold-bonded artificial aggregates by genetic algorithms
Geyik, F. (author) / Gesoglu, M. (author) / Güneyisi, E. (author) / Mermerdas, K. (author) / Öz, H.Ö. (author)
2012
8 Seiten, 3 Bilder, 4 Tabellen, 8 Quellen
Conference paper
English
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