This paper discusses how two artificial intelligence techniques were combined, neural networks and genetic algorithms for the development of a computational tool used for the estimation of mechanical properties such as tensile strength, uniaxial compressive strength and triaxial compressive strength in sandstones, from petrophysical properties using data from tests of Rock Mechanics Laboratory of the Colombian Petroleum Institute Ecopetrol SA as training data, to improve the design of non-destructive testing with some degree of confidence and resulting in cost reduction.
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