History and Evolution of Deep Foundations and Excavation Shoring in Toronto, Canada
Bibliographic record
Abstract
Green Infrastructure Partners (GIP) is an experienced specialty contractor who installs deep foundation and excavation shoring. The last 50 plus years has given us a unique perspective by witnessing firsthand the evolution of deep foundation and shoring works across Canada, but specifically in the province of Ontario and the city of Toronto. Toronto is Canada’s largest city, with the population doubling over the past forty years. The volume and complexity of construction projects has kept pace with this rapid urban growth. In this paper, we explore the massive increases in construction complexity by delving into the past half-century of archives of a Toronto deep foundations contractor. Fifty years ago, two to three levels of underground parking with driven pile foundations was considered a complex project. Today, Toronto sees the construction of basements eight parking levels deep, secant pile headwalls upwards of 60 m (200 feet) deep, and drilled shafts to even greater depths. Projects sites in Toronto have also increased in complexity over the years, with more projects of greater depth being built on less desirable sites such as in the infilled Lake Ontario waterfront neighbourhood. As construction complexity has increased, equipment and monitoring technology has also improved over the years as tighter movement limitations on construction have become the norm, especially in infrastructure projects. We will explore the local philosophy of shoring construction, including how it has evolved over the years to suit the continuously more complex projects. An emphasis will be placed on deep secant walls which have served as cutoff walls up to 60m deep and excavation support over 30m (100 feet) deep. We will use several historical and modern case study examples to illustrate this chronicle as Toronto contractors evolved over the years to be leaders in installing these deep foundations and excavation shoring for these unceasingly more complicated and risky projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".