Sensitivity-based adaptive procedure (SAP) for optimal rehabilitation of sewer systems
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
Urban flood resilience requires high-performance sewer systems, and multi-objective optimization methods are widely used to improve sewer system efficiency. A non-polynomial complete (NP complete) optimization problem may exist in urban sewer rehabilitation, which may cause low efficiency in optimizing large network systems. In this research, a sensitivity-based adaptive procedure (SAP) is proposed, which can be integrated with optimization algorithms. SAP was integrated with Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO) methods. We further used the Hydraulics and Risk Combined Model (HRCM), which is a risk-informed multi-objective optimization model designed for drainage system rehabilitation to validate the performance of SAP procedure. Results showed that SAP can improve the optimizing performance, and SAP-NSGA-II exhibited superior performance in solving the combined problems of pipe breakage and overflooding.
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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 | 0.001 | 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