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Deep excavations introduce complex geotechnical behaviours that are highly sensitive to soil variability, construction staging, and groundwater conditions. Even well-designed support systems can experience unanticipated deformation patterns when the real ground response diverges from assumed parameters. Traditionally, designers rely on static numerical models, while monitoring teams collect data separately. This disconnect delays the detection of emerging risks, making correc

The mining sector in Brazil is undergoing a profound transformation. Following the Mariana (2015) and Brumadinho (2019) dam failures, the industry has strengthened its commitment to geotechnical safety and data transparency. Regulations now demand continuous monitoring and clear traceability in decision-making. Yet, many mines still manage monitoring and geotechnical data in isolation, making it difficult to detect anomalies early or validate model assumptions. The key challe

Characterising rock mass conditions at the tunnel face is a critical part of every underground project. Traditionally, this process requires manual visual assessment, note-taking, and later calculation of rock mass parameters — a workflow that is often time-consuming and subjective. The new DAARWIN Rock Characterisation Tool, co-developed with ACCIONA , transforms this process. By automated data interpretation, DAARWIN enables engineers to evaluate the Rock Mass Rating (RMR)

In tunneling and underground construction, understanding the ground is the first line of defense against uncertainty. The Rock Mass Rating (RMR)  system has long been a cornerstone for assessing rock quality, guiding support design, and predicting excavation performance.However, when RMR is determined manually , engineers face significant challenges that can compromise both accuracy and efficiency. In complex tunneling environments, the reliability of design decisions depends