Estimation Of Los Angeles And Micro Deval Abrasion Values For Gneiss And Granite Using Anfis Approach
Copyright policy )Estimation Of Los Angeles And Micro Deval Abrasion Values For Gneiss And Granite Using Anfis Approach
Gneisses and granites mechanical properties knowledge such as the Los Angeles and Micro-Deval coefficients is very important for the engineer when designing and building civil engineering works. Laboratories test often performed to obtain Los Angeles and Micro-Deval values are expensive and time consuming. For this, the depths (parallel and perpendicular to the foliation planes) obtained during a rock drilling test will be used to estimate its coefficients Los Angeles and Micro-Deval. In this study, an ANFIS approach is used to estimate gneisses and granites Los Angeles and Micro-Deval that have been compared to the Multiple Linear Regression method. For this purpose, we have used a database of a sample size of 80 to determine the parameters of the ANFIS and Multiple Linear Regression models, and a second sample of size 15 used for the validation tests. The results obtained show us that we can estimate the mechanical properties (Los Angeles and Micro-Deval) of gneisses and granites with ANFIS approach.
- University of Lomé Togo
Artificial intelligence, FOS: Mechanical engineering, Adaptive neuro fuzzy inference system, Risk Assessment, MLR, Engineering, FOS: Mathematics, Abrasion (mechanical), Civil engineering, Linear regression, ANFIS, rock drilling test, Civil and Structural Engineering, Chromatography, Mechanical Engineering, Sample (material), Statistics, Comminution in Mineral Processing, Gneiss, Geology, FOS: Earth and related environmental sciences, prediction, Computer science, Mechanical engineering, Fuzzy logic, Geotechnical engineering, Chemistry, Geochemistry, Fuzzy control system, Physical Sciences, Los Angeles abrasion test (LA), Micro-Deval abrasion test (MD), Prediction of Tunnel Boring Machine Performance, FOS: Civil engineering, Mathematics, Metamorphic rock
Artificial intelligence, FOS: Mechanical engineering, Adaptive neuro fuzzy inference system, Risk Assessment, MLR, Engineering, FOS: Mathematics, Abrasion (mechanical), Civil engineering, Linear regression, ANFIS, rock drilling test, Civil and Structural Engineering, Chromatography, Mechanical Engineering, Sample (material), Statistics, Comminution in Mineral Processing, Gneiss, Geology, FOS: Earth and related environmental sciences, prediction, Computer science, Mechanical engineering, Fuzzy logic, Geotechnical engineering, Chemistry, Geochemistry, Fuzzy control system, Physical Sciences, Los Angeles abrasion test (LA), Micro-Deval abrasion test (MD), Prediction of Tunnel Boring Machine Performance, FOS: Civil engineering, Mathematics, Metamorphic rock
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