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Safety Evaluation of Reinforced Concrete Structures Using Multi-Source Fusion Uncertainty Cloud Inference and
Zhao Liu1, Huiyong Guo1, Bo Zhang1
1School of Civil Engineering, Chongqing University, Chongqing 400045, China.
Sensors (Basel, Switzerland)
|October 28, 2023
Summary
This study introduces a new method for structural health monitoring (SHM) that quantifies structural safety by analyzing multi-source data and accounting for uncertainties. The approach effectively evaluates component and structure health, outperforming existing models.
Area of Science:
- Civil Engineering
- Structural Health Monitoring (SHM)
- Artificial Intelligence in Engineering
Background:
- Structural damage detection and safety evaluations are critical in structural health monitoring (SHM).
- Existing methods face challenges with multi-source monitoring data and uncertainties from defects and errors.
Purpose of the Study:
- To develop a comprehensive method for evaluating structural safety by quantifying structural health status.
- To characterize the relationship between condition indexes and structural performance using multi-source data.
Main Methods:
- Developed a Multi-Source Fusion Uncertainty Cloud Inference (MFUCI) method.
- Utilized cloud theory to establish a qualitative rule base from condition index cloud drops.
- Employed a multi-source fusion generator to derive joint certainty degrees and quantitative evaluations.
Main Results:
- MFUCI effectively quantifies the health of structural components (RC T-beam) and structures (RC frame) based on stiffness degradation.
- The method demonstrates reliability and robustness through noise immunity and cross-domain inference.
- MFUCI outperforms Bayesian Neural Network (BNN) in uncertainty estimations and Long Short-Term Memory (LSTM) in point estimations.
Conclusions:
- The proposed MFUCI method provides a reliable and robust approach for quantitative structural health evaluation.
- Incorporating uncertainty information enhances the accuracy and performance of structural safety assessments.
- MFUCI offers a significant advancement over existing models for SHM applications.
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