Rock Mechanics
J. Mohammadi; M. Ataei; R. Kakaie; R. Mikaeil; S. Shaffiee Haghshenas
Abstract
Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. ...
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Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Function (RBF) neural network, as two kinds of the soft computing method, are powerful tools for identifying and assessing the unpredicted and uncertain conditions. Hence, this work aims to develop prediction models for estimating the production rate of chain saw machines using the RBF neural network and GMDH type of neural network, and then to compare the results obtained from the developed models based on the performance indices including value account for, root mean square error, and coefficient of determination. For this purpose, the parameters of 98 laboratory tests on 7 carbonate rocks are accurately investigated, and the production rate of each test is measured. Some operational characteristics of the machines, i.e. arm angle, chain speed, and machine speed, and also the three important physical and mechanical characteristics including uniaxial compressive strength, Los Angeles abrasion test, and Schmidt hammer (Sch) are considered as the input data, and another operational characteristic of the machines, i.e. production rate, is considered as the output dataset. The results obtained prove that the developed GMDH model is able to provide highly promising results in order to predict the production rate of chain saw machines based on the performance indices.
R. Mikaeil; M. Abdollahi Kamran; G. Sadegheslam; M. Ataei
Abstract
Predicting the sawability of the dimension stone is one of the most important factors involved in production planning. Moreover, this factor can be used as an important criterion in the cost estimation and planning of the stone plants. The main purpose for carrying out this work was to rank the sawability ...
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Predicting the sawability of the dimension stone is one of the most important factors involved in production planning. Moreover, this factor can be used as an important criterion in the cost estimation and planning of the stone plants. The main purpose for carrying out this work was to rank the sawability of the dimension stone using the PROMETHEE method. In this research work, four important physical and mechanical properties of rocks including the uniaxial compressive strength, Schmiazek F-abrasivity, mohs hardness, and Young's modulus were evaluated as the criteria. During the research process, two groups of dimension stones were selected and analyzed. The rock samples were collected from a number of Iranian factories for the laboratory tests. The production rate of each sawn stone was selected to verify the proposed sawability ranking method. The results obtained showed that the new ranking method can be reliably used for evaluating the sawability of the dimension stone at any stone factory with different rocks only by the physical and mechanical properties testing.
