Seismic reliability evaluation of a tall concrete‐timber hybrid structural system
Bibliographic record
Abstract
Summary Seismic reliability analysis is important for performance evaluation and structure design of novel structural systems since it can consider inherent uncertainties in seismic actions and structural properties. In this paper, seismic reliability analyses were performed on an innovative high‐rise concrete‐timber (FaB) hybrid structural system. The FaB system combines a concrete frame‐tube structure with large story height as the main structure and multi‐story timber structures on each concrete slab as substructures. To better assess the seismic reliability of the FaB system, response surface methods with either the Lasso regression approach or the machine‐learning‐based symbolic regression approach were conducted and compared. Uncertainties, including ground motions, elastic properties and ductility factor of the concrete main structure, mechanical properties of timber substructures and connections (i.e., rubber bearings), mass ratios, and fitting errors, were considered in formulating the limit‐state performance functions. Tens of thousands of non‐linear time‐history analyses were run to establish the response database of the FaB system, and the reliability indexes of both the concrete main structure and timber substructures were calculated. The results show that the symbolic regression approach can formulate simpler response surface functions with similar results compared to the Lasso regression approach. The maximum difference of reliability indexes between the two approaches is less than 8.3%. For the concrete main structure, the seismic reliability indexes are in a range of 1.068 to 4.223 under different performance objectives and hazard levels, while those of timber substructures are in a range of 0.892 to 2.28. The corresponding non‐exceedance probabilities of failure are between 81% and 99%, which reveals the safety and robustness of the FaB system. Meanwhile, the adoption of rubber bearings in the FaB system can enhance the seismic reliability indexes of timber substructures by 9% to 28% but has little influence on those of the concrete main structure. Outcomes of this study can serve as efficient tools to quantify the seismic performance and evaluate the performance‐based seismic design methodology for the proposed tall concrete‐timber hybrid structural system.
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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) | 0.000 | 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) | 0.001 | 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".