Lessons from the 2016 Kaikōura earthquake for design of cut and fill slopes in New Zealand
Bibliographic record
Abstract
The 2016 M 7.8 Kaikōura earthquake in New Zealand caused severe damage to transport infrastructure across the northeastern South Island from coseismic landslides, debris flows, rock falls, failure of retaining walls, and slumping of embankments. Over 200 km of the road and rail networks were affected, with coseismic landslides blocking the coastal rail and road corridors through Kaikōura for 10 months and 13 months, respectively, causing severe disruption to a nationally important transport route. Compilation of a detailed inventory of over 2,300 slope failures along the transport corridors and back-analysis of selected failed slopes highlights the importance of slope geometry and geological controls on the characteristic failure mechanisms and the consequent impacts on infrastructure. The principal landslide types that caused the most disruption to the transport infrastructure were shallow-seated disaggregated rock avalanches in highly fractured Mesozoic greywacke bedrock and deep-seated structurally controlled slides in greywacke and Tertiary sedimentary rocks. These landslides produced the longest outage time for earthmoving to clear debris and then implementation of engineered risk mitigation measures. Extensive damage to earth fill embankments was also caused by the strong ground shaking, which resulted in difficult access for the initial emergency response and often required lengthy outage for repair of the failed sections. Progressive thickening of the fills for road realignment without geotechnical engineering design, a lack of geogrid reinforcement or subsoil drainage measures, and inclusion of unsuitable soils within the fill materials were all contributing factors to the poor performance of these slopes. The damage caused by cut and fill slope failures in the Kaikōura earthquake highlights the need to understand the key mechanisms driving slope failure, assess the response of slopes to strong ground shaking and the consider the consequences of failure and use a resilience-based framework for slope design, which are lacking from commonly-used design approaches. The findings from this research have been used to develop recommendations for resilient design of slopes and proactive management of landslide hazards along infrastructure corridors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".