Publicação
New models for strength and deformability parameter calculation in rock masses using data mining techniques
| Resumo: | Due to the inherent geological complexity and characterisation 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 analysed, these data can provide valuable information that can be used in situations where knowledge of the rock mass is limited. Yet, this implies the organisation 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 very 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). In this work, a first approach of a KDD process applied in the context of rock mechanics is presented. The main goal was to find new models to evaluate strength and deformability parameters. In this process, a large database of geotechnical data were assembled concerning an important underground structure built in a predominantly granite rock formation. These innovative methodologies and tools were used to analyse and extract new and useful knowledge. The procedure allowed developing new, simple, and reliable models to predict geomechanical parameters, namely friction angle, cohesion and deformability modulus using different sets of input data which can be used in different situations of information availability. |
|---|---|
| Autores principais: | Miranda, Tiago F. S. |
| Outros Autores: | Correia, A. Gomes; Santos, Manuel; Sousa, L. R.; Cortez, Paulo |
| Assunto: | Strength Deformability parameters Data Mining Databases Data analysis Artificial intelligence Geotechnical models Underground structures |
| Ano: | 2011 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Due to the inherent geological complexity and characterisation 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 analysed, these data can provide valuable information that can be used in situations where knowledge of the rock mass is limited. Yet, this implies the organisation 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 very 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). In this work, a first approach of a KDD process applied in the context of rock mechanics is presented. The main goal was to find new models to evaluate strength and deformability parameters. In this process, a large database of geotechnical data were assembled concerning an important underground structure built in a predominantly granite rock formation. These innovative methodologies and tools were used to analyse and extract new and useful knowledge. The procedure allowed developing new, simple, and reliable models to predict geomechanical parameters, namely friction angle, cohesion and deformability modulus using different sets of input data which can be used in different situations of information availability. |
|---|