Long-Span Arch Bridge Warning Thresholds Using Reliability Criteria
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Solution Overview
Problem
Current methods for setting health monitoring warning thresholds in long-span arch bridges suffer from reliability issues, with data-driven approaches leading to false alarms due to weak structural state correlation and model-based methods producing excessively high thresholds that miss critical warnings, posing safety risks.
Innovation Solution
A method and system for setting health monitoring warning thresholds based on reliability criteria, involving a performance function, inverse reliability analysis, and conjugate search strategy to establish quantified relationships between structural reliability levels and serviceability states, enabling precise and dependable threshold determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If monitoring data-based thresholds are used, then the system can provide basic alerts, but the weak structural state correlation leads to reliability issues and false alarms
Solution Approach 1:
The patent introduces a performance function G(u,φ) as an intermediary that connects monitoring data to structural safety assessment. This function serves as a mediator that transforms raw monitoring data into meaningful safety indicators, resolving the contradiction by providing both operational simplicity and reliability through the standardized performance function framework
Solution Approach 2:
The patent changes the parameter representation from direct monitoring values to performance function values that incorporate reliability indices. By transforming the parameter space and using inverse reliability analysis to determine thresholds based on target reliability indices, the system achieves both ease of operation and high reliability in warning threshold setting
2Reliability
If model-based thresholds are used, then theoretical rigor is achieved, but the worst-case load assumptions and safety factors produce excessively high thresholds that may miss critical structural warnings
Solution Approach 1:
The patent inverts the traditional model-based approach by using inverse reliability analysis. Instead of calculating thresholds from worst-case assumptions and then checking safety, the method starts with target reliability indices and works backwards to determine appropriate thresholds, thereby achieving theoretical rigor while avoiding excessively conservative threshold values
Solution Approach 2:
The patent introduces dynamic adjustment of thresholds based on actual structural response and reliability targets. Rather than using fixed conservative thresholds from design codes, the system dynamically determines thresholds through iterative inverse reliability analysis that adapts to the actual structural behavior and desired reliability levels, improving measurement precision while maintaining theoretical rigor
3Ease of manufacture
If conventional threshold setting methods are used, then the process is simple, but subjectivity leads to inconsistent and unreliable warning thresholds
Solution Approach 1:
The patent implements feedback through the iterative inverse reliability analysis process. The system continuously refines threshold values by comparing actual structural response against target reliability indices, providing feedback that eliminates subjectivity and ensures consistent, reliable threshold setting while maintaining procedural simplicity through automated iteration
Data Source
AI summary
A method and a system for setting health monitoring warning thresholds of long-span arch bridges based on reliability criteria are provided. The method includes establishing a performance function based on structural ultimate responses and warning thresholds of the long-span arch bridge; determining target reliability indices, setting initial values of random variables and warning thresholds, and defining convergence tolerance errors; calculating gradient values of the performance function, computing scaling factors based on the gradient values, obtaining failure points of the performance function through the scaling factors combined with a conjugate search strategy, and updating warning thresholds at the failure points; iterating until errors become less than or equal to the convergence tolerance errors to establish reliability criteria for warning threshold configuration; and deriving warning thresholds corresponding to target reliability indices according to the reliability criteria.