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Record W4415043075 · doi:10.1002/eqe.70074

Dealing With Existing Foundation Piles: Challenges of Urban Redevelopment in Densely Built Japanese Cities With Implications for Earthquake Response

2025· article· en· W4415043075 on OpenAlexaff
R. Kadota, Andrés Reyes, Masayoshi Nakashima, Mahdi Taiebat, Farzad Naeim

Bibliographic record

VenueEarthquake Engineering & Structural Dynamics · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsBGC Engineering (Canada)University of British Columbia
Fundersnot available
KeywordsPileFoundation (evidence)RedevelopmentDemolitionReuseStiffness

Abstract

fetched live from OpenAlex

ABSTRACT In recent years, Japan's major cities have undergone vigorous redevelopment, leading to the demolition of many existing buildings. A major challenge in this process is how to deal with the existing foundation piles, specifically, how to execute the removal of these piles and the subsequent backfilling. This Short Communication reports on the current situation in Japan. It first outlines the ongoing redevelopment efforts and provides an overview of common methods adopted for pile removal in Japan. It then presents a case study of actual pile removal and backfilling work, showing that the stiffness of the surrounding ground after backfilling, particularly within about 2 m of the removal location, tends to decrease to about 60%–80% of the original ground's stiffness. It further suggests that staged‐construction analysis with advanced numerical codes has the potential to reasonably simulate the pile removal process. Finally, the article highlights future technical challenges, including how to account for the reduced stiffness near the backfilled zone and how to control the properties of the backfill material, particularly with respect to their influence on the horizontal resistance of piles during earthquakes. It also discusses how pile removal and backfilling methods can adapt to emerging concepts such as carbon neutrality and the circular economy. Specifically, it touches on the reuse of existing piles in new buildings and the option of leaving old piles in place rather than removing them.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.230
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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