Gaussian process model for maximum and residual drifts of timber-steel hybrid building
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it