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.info:eu-repo/semantics/publishedVersio

This study takes advantage of the high learning capabilities of data mining (DM) techniques towards to the development of a novel approach for jet grouting (JG) column diameter prediction. The high number of variables involved in JG technology as well as the complex phenomena related with the injection process make JG column diameter (D) prediction a difficult task. Therefore, in order to overcome it, the flexible learning capabilities of DM techniques were applied as an alternative approach of the traditional tools. The achieved results show that both artificial neural network and support vector machine algorithms can be trained to accurately predict D built in different soil types of clayey nature and using different JG systems. In both cases a coefficient of correlation ( ) very close to the unity was achieved. For models training, a set of eight input variables were considered. Among them, the rod withdrawal speed, flow rate of the grout slurry and the JG system were identified as the most relevant ones, although the grout pressure and the dynamic impact of the grout also revealed an important influence on D prediction. Moreover, additionally to the identification of the key model variables, it was also measured their effects on D prediction based on a data-based sensitivity analysis. These achievements represent a novel contribution for JG technology, mainly at the design level. Furthermore, the obtained results also underline the potential and contribution of DM to solving complex problem in geotechnical engineering.