2024 Terzaghi Lecture: Soil Models in Prediction, Design, and Geotechnical Problem-Solving
Bibliographic record
Abstract
This is the written version of the Karl Terzaghi Lecture presented at the ASCE Geo-Congress in Vancouver, British Columbia, Canada, February 27, 2024. It discusses the development of robust, commercial software for numerical analyses of soil continua that has transformed the practice of geotechnical engineering over the course of the author’s professional career. Embedded in these formulations are constitutive equations that represent deformation and shear strength properties of saturated soils. This paper shows how advances in soil modeling have been closely linked to the understanding of soil behavior and then consider the lessons learned in diverse applications from excavation support systems, to staged embankment construction and driven pile capacity. While advanced clay models (MIT-E3) achieve better predictions of performance, simplified methods such as numerical limit analyses (rigid plastic soil behavior) are invaluable for undrained stability analyses. A unified framework for clays and sands (MIT-S1) introduces void ratio as an independent state parameter and represents particle breakage as the primary compression mechanism in granular soils. These concepts explain important scale effects in the performance of foundations on sands. The model also predicts the occurrence of instability in undrained shear associated with prior consolidation stress history and hence, can be used to estimate the potential for triggering of static liquefaction events. Recent work on viscoplasticity (MIT-SR) provides a unifying framework for coupling creep and consolidation, but does not resolve measurements showing different scaling properties that are likely related to specific microstructures of clays. Future advances in soil models will undoubtedly invoke relations between microstructural features and macroscopic properties. These are illustrated in the modeling Old Alluvium, where the breakdown of the intact microstructure affects macroscopic swelling. For complex soil-structure interaction (SSI) problems, it may prove impractical to achieve reliable analyses of soil continua. Here, soil models can be used to calibrate reduced-order models to represent SSI. Hyperplastic inertial macroelements developed for the seismic loading of bridge-abutments are shown to achieve comparable predictions to much more computationally demanding continuum analyses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.017 |
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".