Gaussian process model for maximum and residual drifts of timber-steel hybrid building
Bibliographic record
Abstract
The current performance-based building design considers maximum interstorey drift (MISD) ratio as the main structural performance indicator. Observations from past earthquake and reported studies, however, have highlighted that residual interstory drift (RISD) ratio has become an important factor in assessing post-earthquake safety of buildings, and decision in economic feasibility of repair and reconstruction. Improving post-earthquake performance evaluation of buildings enables decision-makers prioritise repair and tag high-risk buildings. The MISD and RISD are subject to uncertainties and have non-linear relation with the input parameters. Thus, in this paper, analytical surrogate model of MISD and RISD ratios are developed using Gaussian process (GP). To show utility of the GP model, a new hybrid building system, cross laminated timber (CLT)–steel moment resisting frame hybrid system, was considered. The hybrid building was design for the seismicity of Vancouver, BC, and meets the current steel design code. For the GP surrogate model, the hybrid building input parameters considered were: infill pattern of the CLT, bracket spacing of the connection between the CLT and steel frame and panel thickness and strength of the CLT. In addition, sensitivity of four ground motion indicators was considered as surrogate input into the GP model: peak ground acceleration, ratio of peak ground acceleration/peak ground velocity, Arias intensity measure and significant duration. In general, the GP model showed good predictive performance of MISD and RISD ratios. In particular, the best predictions were obtained using the ratio of peak ground acceleration/peak ground velocity as a covariate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".