MWD data analysis for optimization of tunnel excavation

Alla Sapronova*, Paul Johannes Unterlaß, Kazuo Sakai, Shuntaro Miyanaga, Abdallah Ahmed Fouad Elsayed Soliman, Thomas Marcher

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

The drill and blast tunneling method applies to various rock mass conditions and is widely used in underground construction. Optimization of drill and blast requires careful planning and currently depends on the engineers’ ability to execute the art of blasting. Intelligent analysis of measurement while drilling (MWD) data from blast holes can be used for process optimizations, responsible resource utilization, and risk minimization. For example, an Artificial Intelligence (AI) -based decision support system (DSS) can suggest the volume and content of explosive material. However, to develop a reliable and trustworthy DSS, one needs to understand the relation between MWD data logs and the underlying lithology conditions, like composition or type of rock mass. This work provides an overview of the most common methods for MWD data analysis. Selected methods are then utilized to develop predictive machine-learning (ML) models, which are further validated with available MWD data.
Original languageEnglish
Title of host publicationProceedings of the ISRM 15th International Congress on Rock Mechanics and Rock Engineering & 72nd Geomechanics Colloquium
Subtitle of host publicationChallenges in Rock Mechanics and Rock Engineering
EditorsWulf Schubert, Alexander Kluckner
Place of PublicationSalzburg
PublisherAustrian Society for Geomechanics
Pages610-615
ISBN (Electronic)978-3-9503898-3-8
Publication statusPublished - 14 Oct 2023

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