Abstract
The escalating construction industry has led to a drastic increase in the dimension stone demand in the construction, mining and industry sectors. Assessment and investigation of mining projects and stone processing plants such as sawing machines is necessary to manage and respond to the sawing performance; hence, the soft computing techniques were considered as a challenging task due to stochastic optimization of this issue and to handle complex optimization problems. In this study, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms as soft computing techniques were used to classify the dimension stones based on physical and mechanical properties and ampere consumption. For this purpose, varieties of dimension stones from 12 quarries located in Iran were investigated. Studied dimension stones were classified into two and three separate clusters using the optimization clustering techniques. The results showed that the applied soft computing technique makes it possible to evaluate the performance of sawing machines in different complex conditions and uncertain systems.
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