Resilient Tunnelling in the Himalayas and Beyond: Lessons from Recent Projects
Bibliographic record
Abstract
The recent tunnel collapses at Silkyara (Uttarakhand, 2023) and SLBC Tunnel 1 (Telangana, 2025) have reshaped the way underground infrastructure projects are perceived and executed in India. While both incidents were tragic in scale and impact, they have provided critical insights into the gaps that exist in current tunnelling practices—particularly in geological investigations, risk anticipation, contract frameworks, and crisis response mechanisms.This extended abstract analyses these two case studies not only from a technical standpoint, but also from the broader lens of resilient engineering. At Silkyara, the collapse during re-profiling exposed the underestimated complexity of a shear-dominated zone and led to a dramatic 17-day rescue mission. At SLBC, a sudden inflow of water and debris caused the submergence of a TBM, bringing to light the risks of advancing through shear zones with limited predictive confidence.In both cases, the aftermath triggered a re-evaluation of excavation methods, safety protocols, and planning culture. The move from TBM to Drill and Blast (D and B) at SLBC, the application of real-time monitoring and GBRs at Silkyara, and the eventual incorporation of Aerial Electromagnetic (AEM) surveys reflect a transition toward adaptive, site-specific resilience in tunnel engineering.The paper concludes by synthesising the lessons learnt into actionable engineering strategies and policy recommendations for future projects in the Himalayas and beyond. It proposes that true resilience lies not just in reacting to failure but in anticipating it—and designing systems robust enough to prevent it or recover from it swiftly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".