BIM-Based Seismic Loss Assessment for Instrumented Buildings
Bibliographic record
Abstract
A look at the most devastating earthquakes ever reported, emphasizes the essentiality of forecasting earthquake-induced loss of buildings with predictable seismic performances. This perspective is mostly adopted in performance-based earthquake engineering (PBEE) loss assessment frameworks. The current generation of PBEE framework developed by the Pacific Earthquake Engineering Research (PEER) center, PEER-PBEE (also known as FEMA P-58) is a state-of-the-art methodology among all the PBEE frameworks. It contains four stages of hazard analysis, structural analysis, damage analysis and loss analysis to predict the seismic loss estimation of buildings in terms of repair cost, downtime, and other decision variables. However, despite its numerous advantages, the quality of the PEER-PBEE framework can significantly be affected due to considerable sources of uncertainties. Lack of actual structural performance characteristics (structural analysis) and ineffective details of building components (damage analysis) are the main reasons identified by the previous studies to the incorporated uncertainties. Moreover, some attempts have been made before to employ innovative technologies such as seismic instrumentation and integrated BIM tools to tackle the associated uncertainties in structural and damage analysis, respectively. However, yet, no comprehensive systematic methodology has been dedicated to the full engagement of seismic instrumentation and integrated BIM tools in PBEE-based loss assessment frameworks. This objective of the thesis is to develop a systematic methodology to address the limitations associated with PEER-PBEE loss assessment framework by adopting innovative technologies such as seismic instrumentation of buildings and integrated BIM tools. For this purpose, a workflow of seismic loss estimation is developed for buildings with three main steps, including: (1) the measurement of structural dynamic response throughout an ambient vibration test and subsequent output-only system identification (SI), (2) experimental structural analysis by both the model-based and nonmodel-based approaches (3) automated seismic loss analysis through the developed Application Programming Interface (API) tool in BIM platform (based on FEMA P-58 framework). Moreover, the full functionality of the proposed methodology is validated through a real case study located in Montreal, Canada. \nConsequently, this study demonstrates the added values of the systematic utilization of seismic instrumentation as well as BIM-based API technology in seismic vulnerability assessment of buildings, which leads to a better interpretation of loss consequence predictions and subsequent decision-making process in disastrous situations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".