Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review
Abstract
:1. Introduction
2. An Overview of the Bayesian Network
2.1. Overview of Bayesian Network Development
2.2. Bayesian Network Mathematical Model
3. Structural Health Monitoring
3.1. Definition of Structural Health Monitoring
3.2. Approach and Significance of Structural Health Monitoring
4. Application of Bayesian Network in Structural Health Monitoring
4.1. Application of Bayesian Network in Damage Identification
4.2. Application of Bayesian Network in Data Fusion
4.3. Application of Bayesian Network in Uncertainty Modeling
4.4. Application of Bayesian Network in Decision Support
5. Conclusions
Funding
Acknowledgments
Conflicts of Interest
References
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A | P(A) |
---|---|
True | 0.1 |
Not true | 0.9 |
B | P(B) |
---|---|
True | 0.4 |
Not true | 0.6 |
A | P(C) | |
---|---|---|
True | Not True | |
True | 0.8 | 0.1 |
Not true | 0.2 | 0.9 |
A | P(D) | |||
---|---|---|---|---|
B = True | B = Not True | |||
True | Not True | True | Not True | |
True | 0.8 | 0.6 | 0.6 | 0.3 |
Not true | 0.2 | 0.4 | 0.4 | 0.7 |
Method Type | Core Strengths | Main Drawback |
---|---|---|
BN | Highly interpretable, supporting causal reasoning Explicitly quantifies uncertainty Can integrate expert knowledge with data | High computational complexity (curse of dimensionality) Reliance on prior knowledge and structured data Dynamic modeling requires extensions (DBN) |
LSTM | Strong sequential modeling capability, capturing long-term dependencies Automatic feature extraction, adapting to complex temporal patterns | Black-box models, poor interpretability Requires large amounts of labeled data High computational resource consumption |
Traditional machine learning | High computational efficiency, suitable for small samples Some models are interpretable (e.g., decision trees) Robust to noise | Reliant on manual feature engineering Difficult to handle high-dimensional temporal or spatial data Unable to quantify uncertainty |
Deep learning | Automatically extract unstructured features (such as images, spectrograms) High precision, suitable for complex pattern recognition | Data demand is extremely high, and labeling costs are substantial Lacks the ability to model uncertainty High complexity in model deployment |
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Wang, Q.-A.; Lu, A.-W.; Ni, Y.-Q.; Wang, J.-F.; Ma, Z.-G. Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review. Sensors 2025, 25, 3577. https://doi.org/10.3390/s25123577
Wang Q-A, Lu A-W, Ni Y-Q, Wang J-F, Ma Z-G. Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review. Sensors. 2025; 25(12):3577. https://doi.org/10.3390/s25123577
Chicago/Turabian StyleWang, Qi-Ang, Ao-Wen Lu, Yi-Qing Ni, Jun-Fang Wang, and Zhan-Guo Ma. 2025. "Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review" Sensors 25, no. 12: 3577. https://doi.org/10.3390/s25123577
APA StyleWang, Q.-A., Lu, A.-W., Ni, Y.-Q., Wang, J.-F., & Ma, Z.-G. (2025). Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review. Sensors, 25(12), 3577. https://doi.org/10.3390/s25123577