L. R. Sousa

New Models for Strength and Deformability Parameter Calculation in Rock Masses Using Data-Mining Techniques

Due to the inherent geological complexity and characterization difficulties in rock formations, the evaluation of geomechanical parameters is very complex, mostly in the initial project stages and in small-scale geotechnical works, where information is scarce for the definition of an accurate geotechnical model. However, in large geotechnical projects, a great amount of data are produced and used to establish near-homogeneous geotechnical zones. If properly analyzed, these data can provide valuable information that can be used in situations where knowledge of the rock mass is limited. Yet, this implies the organization of geotechnical data in formats for proper analysis using advanced tools which is not normally done. Data-mining techniques have been successfully used in many fields but scarcely in geotechnics. They seem to be adequate as an advanced technique for analyzing large and complex databases that can be built with geotechnical information within the framework of an overall process of knowledge d...

Application of Data Mining Techniques for the Development of New Rock Mechanics Constitutive Models

Data Mining (DM) techniques have been successfully used in many fields and more recently also in geotechnics with good results in different applications. They are adequate as an advanced technique for analysing large and complex databases that can be built with geotechnical information within the framework of an overall process of Knowledge Discovery in Databases (KDD). A KDD process is carried out in the context of rock mechanics using the geotechnical information of two hydroelectric schemes built in Portugal and at DUSEL (Deep Underground Science and Engineering Laboratory). The purpose was to find new models to evaluate strength and deformability parameters and also empirical geomechanical indexes. Databases of geotechnical data were assembled and DM techniques used to analyse and extract new and useful knowledge. The procedure allowed developing new, simple, and reliable models for geomechanical characterization using different sets of input data which can be applied in different situations of information availability.

The Use of Data Mining Techniques in Rockburst Risk Assessment

Abstract Rockburst is an important phenomenon that has affected many deep underground mines around the world. An understanding of this phenomenon is relevant to the management of such events, which can lead to saving both costs and lives. Laboratory experiments are one way to obtain a deeper and better understanding of the mechanisms of rockburst. In a previous study by these authors, a database of rockburst laboratory tests was created; in addition, with the use of data mining (DM) techniques, models to predict rockburst maximum stress and rockburst risk indexes were developed. In this paper, we focus on the analysis of a database of in situ cases of rockburst in order to build influence diagrams, list the factors that interact in the occurrence of rockburst, and understand the relationships between these factors. The in situ rockburst database was further analyzed using different DM techniques ranging from artificial neural networks (ANNs) to naive Bayesian classifiers. The aim was to predict the type of rockburst—that is, the rockburst level—based on geologic and construction characteristics of the mine or tunnel. Conclusions are drawn at the end of the paper.