Data-driven probabilistic quantification and assessment of the prediction error model in damage detection applications

Silionis, Nicholas E.; Anyfantis, Konstantinos N.

doi:10.1016/j.probengmech.2023.103412

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…They applied the wavelet transform, Hilbert-Huang transform, and Teager-Huang transform to diagnose a cable-stayed bridge, accounting for environmental variability. A data-driven damage localization approach [24] and the probabilistic quantification and assessment of prediction errors in damage detection [25] are some of the achievements made in data-driven structural damage identification.…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Alemu,

Lahmer,

Walther

2024

Eng

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Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

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Section: Introduction and State Of The Artmentioning

confidence: 99%

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Alemu,

Lahmer,

Walther

2024

Eng

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“…The novel data-driven experimental approach proposed in this study, which was an improvement to previous computational results [ 15 ], is robust to measurement noise and reveals a high effectiveness in damage detection due to the applied combination of both numerical and experimental results in the learning algorithm. The developed data-driven approach falls into the current trends in the development of SHM systems, a concept which was recently used, e.g., by the authors of [ 23 ]. It should be mentioned that the data used in this study were acquired from physical experiments and were additionally experimentally validated using the NDT ultrasonic testing technique.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Damage in Composite Structures Using an Optimized Sensor Network: A Data-Driven Experimental Approach

Ručevskis

Rogala

Katunin

2023

Sensors

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Due to the complexity of the fracture mechanisms in composites, monitoring damage using a vibration-based structural response remains a challenging task. This is also complex when considering the physical implementation of a health monitoring system with its numerous uncertainties and constraints, including the presence of measurement noise, changes in boundary and environmental conditions of a tested object, etc. Finally, to balance such a system in terms of efficiency and cost, the sensor network needs to be optimized. The main aim of this study is to develop a cost- and performance-effective data-driven approach to monitor damage in composite structures and validate this approach through tests performed on a physically implemented structural health monitoring (SHM) system. In this study, we combined the mentioned research problems to develop and implement an SHM system to monitor delamination in composite plates using data combined from finite element models and laboratory experiments to ensure robustness to measurement noise with a simultaneous lack of necessity to perform multiple physical experiments. The developed approach allows the implementation of a cost-effective SHM system with validated predictive performance.

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“…Data-driven models have gained popularity in recent years, and they establish numerical forecasting models based on feld measurement data instead of FE models [15][16][17]. Based on data mining, a novel time series prediction model with a combination of Kalman flter and autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity (ARIMA-GARCH) [18] improves the prediction accuracy of bridge structure deformation, which does not meet requirements for predicting nonstationary performance information with cyclical trends.…”

Section: Introductionmentioning

confidence: 99%

Bridge Performance Prediction Based on a Novel SHM-Data Assimilation Approach considering Cyclicity

Qu,

Sun,

Huang

2023

Structural Control and Health Monitoring

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Modern bridges are monitored by an increasing network of sensors that produce massive data for bridge performance prediction. Reasonably and dynamically predicting with monitored data for the time-variant reliability of the existing bridges has become one of the urgent problems in structural health monitoring (SHM). This study, taking the dynamic measure of structural stress over time as a time series, proposes a data assimilation approach to predicting reliability based on extreme stress data with cyclicity. To this aim, the objectives of this article are to present the following: (a) a Gaussian mixture model-based Bayesian cyclical dynamic linear model (GMM-BCDLM) based on extreme stress data with cyclicity and (b) a dynamic reliability prediction method in the combination of GMM-BCDLM and SHM data via first-order second-moment (FOSM) method. An in-service bridge for providing real-time monitored stress data is applied to illustrate the application and feasibility of the proposed method. Then, the effectiveness and prediction precision of the proposed models are proved to be superior compared to other prediction approaches to extreme stress data with cyclicity.

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Data-driven probabilistic quantification and assessment of the prediction error model in damage detection applications

Cited by 8 publications

References 21 publications

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Monitoring of Damage in Composite Structures Using an Optimized Sensor Network: A Data-Driven Experimental Approach

Bridge Performance Prediction Based on a Novel SHM-Data Assimilation Approach considering Cyclicity

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