SHM-informed life-cycle intelligent maintenance of fatigue-sensitive detail using Bayesian forecasting and Markov decision process

The degradation of bridge structural performance arises from the combined influence of various factors. Performance assessment and reliable prediction of bridge performance degradation through effective utilizing of detection information updates is a challenging problem. In this paper, the concept of performance indicators is redefined, employing to delineate bridge performance degradation. A bridge performance degradation model (the error ≤8%) is formulated, considering the multiple-variable Bayesian dynamic linear method (MBDLM) and revealing the coupling mechanisms among factors influencing bridge performance degradation. On this basis, the prediction performance of the model is quantitatively evaluated by three metrics: mean squared error, predictive mean squared error and mean absolute percentage error. A methodology is presented for the assessment, prediction, and maintenance reinforcement of in-service bridge structural performance degradation. This approach holds promise for future applications in safety assessments and the decision-making process for preventive maintenance of operational bridges.

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An unsupervised context-free forecasting method for structural health monitoring by generative adversarial networks with progressive growing and self-attention

Gao,

Zou,

Zhou

et al. 2024

Structural Health Monitoring

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Ensuring the robust operation of bridges demands swift and precise forecasting of structural performance within the health monitoring system. However, challenges arise in the realm of long-time series forecasting context-free data. These challenges encompass scenarios where there is a lack of reference data pre- and postforecasting, instances of missing data before forecasting (near-forecasting), or predictions of the distant future (far-forecasting). Addressing these issues, a current imperative is the development of a framework adept at efficiently and directly forecasting context-free long-time series data. This article introduces a framework, the convolutional generative adversarial network with progressive growing and self-attention (PSA-CGAN) mechanisms, tailored for forecasting context-free data. The approach employs generative adversarial networks in tasks related to long-time series. Additionally, progressive growing and self-attention mechanisms are harnessed to capture both long- and short-term features in the time series, notably enhancing the efficiency and accuracy of the forecasting method. The proposed method undergoes validation through application to two distinct bridge cases, confirming its generality and real-time forecasting prowess. On two bridges, PSA-CGAN can effectively predict acceleration data in various context-free scenarios and is capable of forecasting progressively changing damage data. It provides a valuable reference for predicting damage data. Additionally, the results indicate that PSA-CGAN is a promising and practical solution for the prediction of context-free data. It represents an efficient and rapid tool for damage prediction.

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SHM-informed life-cycle intelligent maintenance of fatigue-sensitive detail using Bayesian forecasting and Markov decision process

Cited by 4 publications

References 55 publications

Digital twins-boosted intelligent maintenance of ageing bridge hangers exposed to coupled corrosion–fatigue deterioration

Digital twins-boosted intelligent maintenance of ageing bridge hangers exposed to coupled corrosion–fatigue deterioration

Bridge performance degradation model based on the multi-variate bayesian dynamic linear method

An unsupervised context-free forecasting method for structural health monitoring by generative adversarial networks with progressive growing and self-attention

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