Data acquisition-interpretation-aggregation for dynamic design of rock tunnel support

Abstract

During the rock tunnel construction, one of the critical aspects lies on the support design to secure the construction safety. Due to the extreme complex underground geological and geotechnical condition, the support design needs to be dynamic and ideally should consider all related data and information comprehensively and timely. Different Internet of things (IoT) and other related information technologies (IT) have been widely applied during tunnel construction to collect a large amount of monitoring data, which in turn demands real time or just-in-time (JIT) data processing for decision making. To understand the state-of-the-art IoT-based dynamic tunnel support design, a comprehensive review is conducted from the perspectives of real time or just-in-time data acquisition, data interpretation and data aggregation. For different types of technologies, their time consumptions, technology strengths and drawbacks were thoroughly analyzed in a full and seamless “data acquisition-interpretation-aggregation” workflow linking to the dynamic tunnel support optimum design. As a result of the review, three primary research gaps are identified, i.e., the high time consumption of data interpretation, dilemmas of conventional and AI-supported aggregation methods, and long retrieval time for similar design cases. Focusing on these three gaps, three key concepts, namely, time consumption, accuracy, and degree of automation, are proposed as key indicators for the tunnel support design. A conceptual framework, just-in-time tunnel support design is further proposed, where the most appropriate and efficient methods can be conceptually integrated and lead towards technical implementation. This review contributes to the comprehensive understanding of timely dynamic tunnel support design and provides future insights of promoting JIT tunnel support optimum design.