English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper has been written to achieve two objectives. The first objective is to provide a discussion of the practical limitations of numerical modelling in the field of geomechanical engineering. Too many discussions of numerical methods in geomechanical engineering are centred on the impressive ability of numerical tools to conduct complex and sophisticated analyses with relative ease and efficiency. Practitioners need to have grounded conversations on numerical modelling regarding the reality that geomechanical designs are often data-limited with high degrees of uncertainty. When data limits and uncertainty are overlooked, geomechanical engineers are at risk of introducing unforeseen fatal flaws into engineering design. The second objective is to provide “how to” guidelines for model calibration using a variety of data types to qualify and quantify ground reaction. Model calibration is truly the only means to reduce numerical uncertainties. Formal training in numerical modelling is often focused on software utilization and computational methods; however, there are few opportunities for formal training on how to calibrate a model for practical engineering applications. This paper provides guidelines for calibration methods and procedures.

canadian geotechnical journal

2019 Canadian Geotechnical Colloquium: Mitigating a fatal flaw in modern geomechanics: understanding uncertainty, applying model calibration, and defying the hubris in numerical modelling