Integrated Life-Cycle Framework for Optimal Inspection, Monitoring and Maintenance under Uncertainty: Applications to Highway Bridges and Naval Ship Structures
Bibliographic record
Abstract
Existing engineering structures are continuously deteriorating and their lifetimes are limited. In order to help ensure the structural safety and extend the service life of existing deteriorating structures, significant research efforts for establishing cost-effective maintenance strategies have been made. A life-cycle analysis usually depends on structural assessment and prediction models under uncertainty. The accuracy associated with these models can be considerably improved if the data from structural health monitoring (SHM) are used efficiently. Therefore, integration of SHM into maintenance management has recently been considered as a significant tool for rational maintenance planning. Improved accuracy of structural performance assessment and prediction by SHM can lead to timely and appropriate maintenance interventions, resulting in reduction of both expected failure cost and expected maintenance cost of deteriorating structural systems. In order to maximize this potential benefit of SHM, information from monitoring has to be used appropriately, and an effective optimum monitoring planning is necessary. Furthermore, lifetime optimization of inspection, monitoring, and maintenance strategies needs to be investigated in a life-cycle management framework. The main focus of this study is the development of a rational probabilistic integrated framework for optimum inspection, monitoring and maintenance planning. Based on concepts of probability and reliability, novel approaches to assess and predict the structural performance using SHM data are developed and applied to existing highway bridges. For optimum inspection and monitoring planning under uncertainty, several probabilistic approaches are developed in this study. Optimization formulations for these approaches are based on the concepts of availability, damage detection delay, and time-based safety margin. The inspection or monitoring plan is a solution of a multi-objective optimization problem under uncertainty. The uncertainties associated with damage occurrence and propagation, and quality of inspection method are considered within the optimization problem. These approaches are applied to deteriorating structures (i.e., highway bridges, naval ships) under various deterioration mechanisms (i.e., corrosion, fatigue). Furthermore, considering the effects of probabilities of damage detection and repair on future structural performance, the optimum inspection and maintenance strategy under uncertainty are addressed to extend the lifetime of deteriorating structures.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".