Structural condition evaluation using unsupervised Bayesian optimized BiLSTM networks

Civil structures are susceptible to performance deterioration during their service lives. Therefore, it is essential to periodically evaluate structural conditions for preventing potential catastrophic failure. Data-driven methods have been widely adopted for this purpose, which often utilize deep l...

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Bibliographic Details
Published in:Journal of civil structural health monitoring Vol. 15; no. 7; pp. 2359 - 2375
Main Authors: Zheng, Jin-Ling, Fang, Sheng-En
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2025
Springer Nature B.V
Subjects:
Algorithms
Artificial intelligence
Bayesian analysis
Civil Engineering
Control
Deep learning
Dynamical Systems
Engineering
Errors
Finite element method
Fourier transforms
Genetic algorithms
Great Depression
Machine learning
Measurement Science and Instrumentation
Neural networks
Noise levels
Optimization
Optimization algorithms
Original Paper
Performance degradation
Sensors
Steel frames
Time series
Unsupervised learning
Vibration
Wavelet transforms
Wasserstein distance
Structural condition evaluation
Bayesian optimization
Bidirectional long-short term memory networks
Unsupervised learning
ISSN:2190-5452, 2190-5479
Online Access:Get full text
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Summary:Civil structures are susceptible to performance deterioration during their service lives. Therefore, it is essential to periodically evaluate structural conditions for preventing potential catastrophic failure. Data-driven methods have been widely adopted for this purpose, which often utilize deep learning (DL) algorithms that generally require extensive training data considering various condition scenarios. However, finite element (FE) simulation data are practically affected by uncertainties such as modeling errors and operational environmental variations, limiting the applicability of supervised DL algorithms. Due to this, an unsupervised learning framework has been proposed for structural condition evaluation by establishing a correlation model for the response data measured at different locations of a healthy structure using bidirectional long short-term memory (BiLSTM) networks. During the training process, the optimal hyperparameters of a BiLSTM network is objectively, instead of ‘subjectively’, determined through Bayesian optimization (BO) without requiring labeled measurement data, improving the network generalization performance. During the testing process, response data from healthy and unknown scenarios are input into the BO-BiLSTM network, and the errors between the reconstructed and actual data are taken as the latent features. Then, the feature similarity between the unknown scenarios and the healthy structure is calculated using the Wasserstein distance as a structural condition indicator. The feasibility of the proposed method has been validated using the IASC-ASCE benchmark frame and an experimental steel frame, demonstrating that the proposed condition indicator is sensitive to structural damage and robust to different noise levels. As structural degradation developed, the condition indicators for the two frames increased from 0.090 and 0.583 to 1.182 and 0.825, respectively. The structural conditions were successfully evaluated without the labeled measurement data, validating the engineering applicability of the proposed method.
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ISSN:2190-5452
2190-5479
DOI:10.1007/s13349-025-00944-8