Rapid estimation of earthquake damage on instrumented steel frame buildings using simplified tools: towards 'city scale' building simulation
Bibliographic record
Abstract
In the context of Performance-Based Earthquake Engineering (PBEE) there is an increasing need for the development of rapid damage assessment methodologies for buildings that will enable stakeholders to take informed decisions for management of rescue resources, for reliable estimation of economic losses after an earthquake event in an urban area and for seismic rehabilitation of damaged infrastructure. An important difficulty to assess the post-earthquake functionality of a steel building is the fact that after an earthquake a detailed engineering inspection is typically required. These inspections cause long delays in getting back to operational stage even in those buildings that would most likely be classified as safe and functional after the inspection. This research thesis proposes a computationally efficient methodology for rapid earthquake damage on instrumented steel buildings located in an urban area that can facilitate these needs. A continuous model is employed as part of the proposed methodology that is calibrated within seconds with a computationally efficient optimization scheme, which uses an improved version of a general pattern search in combination with the modal minimization method. Based on the calibrated numerical model, the maximum story drift profile along the height of an instrumented steel building is obtained, given the recorded earthquake response at the instrumented floors of the same building. This drift profile is then used with drift-based fragility curves that express the probability of reaching or exceeding pre-described damage states in pre-qualified beam-to-column fully restraint moment connections. The proposed methodology is validated with a number of instrumented steel frame buildings located in a highly seismic urban area that experienced an actual earthquake event. Results show near perfect mapping of structural damage for light, intermediate and severely damaged steel frame buildings after a comparison with the actual damage inspections of the same buildings after the earthquake. The proposed methodology for rapid earthquake damage assessment on instrumented buildings is employed to demonstrate the concept of city-scale building simulation to facilitate emergency actions after a seismic event in an urban area. For this reason, we employed data from 22 stations in California that recorded the 1994 Northridge earthquake. Generalized structural damage maps are developed with the use of GIS for steel frame buildings around the Los Angeles area to illustrate the concept of "city-scale" structural damage assessment. The utilization of the proposed methodology is also demonstrated through an Android cell-phone application that has been developed that allows a structural engineer to conduct a qualitative structural damage assessment of an instrumented steel building after an earthquake on-the-fly while being on the building site.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".