Machine learning for rock mechanics problems; an insight

Abstract

Due to inherent heterogeneity of geomaterials, rock mechanics involved with extensive lab experiments and empirical correlations that often lack enough accuracy needed for many engineering problems. Machine learning has several characters that makes it an attractive choice to reduce number of required experiments or develop more effective correlations. The timeliness of this effort is supported by several recent technological advances. Machine learning, data analytics, and data management have expanded rapidly in many commercial sectors, providing an array of resources that can be leveraged for subsurface applications. In the last 15 years, deep learning in the form of deep neural networks, has been used very effectively in diverse applications, such as computer vision, seismic inversion, and natural language processing. Despite the remarkable success in these and related areas, deep learning has not yet been widely used in the field of scientific computing specially when it comes to subsurface applications due to the lack of large amount of data to train algorithms. In this paper, we review such efforts and try to envision future game-changing advances that may impact this field.

Advancements in Understanding Interface Friction: A Combined Experimental and Machine Learning Approach Using Multiple Linear and Random Forest Regressions

“…Despite these known factors, as yet, no detailed study that combines all of these into a single, easy-to-understand model has been conducted, mainly because these factors interact in complicated ways. In artificial intelligence, machine learning algorithms have helped solve complex problems in the geotechnical field, such as understanding soil mechanics behaviour [11][12][13] and improving the use of recycled materials in soil stabilisation [14][15][16][17][18][19].…”