Application Of Artificial Intelligence To Problems Of Rock Mechanics

INTRODUCTION Numerical methods commonly used in rock mechanics, such as finite element, displacement discontinuity, and boundary integral equation methods, offer solutions to problems which can be described by differential equations subject to boundary conditions. These numerical methods use approximations leading to solutions in terms of matrix algebra and numerical integration. Many rock mechanics problems which are beyond the scope of such approximations can be treated effectively by application of techniques developed in the field of artificial intelligence (AI). Among the possible applications are: site characterization (Duda, 1975), geological interpretation (Smith and Baker, 1983), solution of problems involving rock joint networks, and underground construction. Each of these applications utilize particular features of Al. Here we shall present results of AI applications which utilize primarily search and to lesser extent learning or pattern recognition. Specific examples presented are in the field of rock fracture network problems and underground construction. ARTIFICIAL INTELLIGENCE Artificial intelligence techniques attempt to achieve human-like reasoning and learning capabilities. This reasoning capability is valuable in applications where repetition, complexity, or tediousness makes human-like intervention impractical. In rock mechanics this occurs, for example, in stochastic rock mass modeling applications where some complex decision-making is required for each of a large number of iterations.