One-class machine learning approach for localized damage detection

Güneş, Burcu

doi:10.1007/s13349-022-00599-9

Cited by 4 publications

(1 citation statement)

References 19 publications

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“… Structural health monitoring: Sensors and ML algorithms may be combined to track the performance and well-being of RC structures throughout their useful lives. For condition-based maintenance and performance optimization, this real-time monitoring delivers useful data [40].  Design code compliance: Engineers may benefit from ML by ensuring that their designs adhere to all applicable construction rules and standards.…”

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confidence: 99%

Transformative role of machine learning in design optimization of reinforced concrete frames

2024

IJATEE

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Design optimization in reinforced concrete (RC) frames is crucial for achieving more efficient, cost-effective, and safe constructions. This involves determining optimal section sizes and reinforcing schemes to enhance structural performance, reduce construction expenses, and conserve natural resources. An optimized design contributes to streamlined construction procedures, shorter construction times, and improved overall sustainability. This paper critically evaluates the current literature on machine learning (ML) applications in the design optimization of RC structures, focusing on the period from 1997 to 2023. The study employs a comprehensive methodology, including bibliometric analysis, to analyze trends, research collaborations, and publication patterns. The results highlight the increasing interest in ML for RC frame optimization. Key applications of ML in design optimization include material characterization, design exploration, optimization algorithms, sensitivity analysis, predictive modeling, structural health monitoring, design code compliance, uncertainty quantification, data-driven design decisions, and design collaboration. The study identifies significant ML algorithms used in optimization, such as the trial-and-error method, linear programming (LP), and nonlinear programming (NLP). Overall, the paper provides insights into the evolving landscape of ML applications in RC frame design, emphasizing the potential for interdisciplinary collaboration and future research directions.

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Section: Data Visualization Was Conducted By Utilizingmentioning

confidence: 99%

Transformative role of machine learning in design optimization of reinforced concrete frames

2024

IJATEE

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Localizing structural damage based on auto-regressive with exogenous input model parameters and residuals using a support vector machine based learning approach

Gunes

2024

Front. Struct. Civ. Eng.

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Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion. Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state. In this paper, the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features. These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine, an unsupervised classifier generating a decision function using only patterns belonging to this baseline state. Structural damage, once detected by the trained machine, a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage. The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated. Subsequently, vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.

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Unsupervised deep learning approach for structural anomaly detection using probabilistic features

Wan,

Zhu,

Luo

et al. 2024

Structural Health Monitoring

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Civil structures may deteriorate during their service life due to degradation or damage imposed by natural hazards such as earthquakes, wind, and impact. Structural performance anomaly detection is essential to provide an early warning of structural degradation limit states in order to prevent potential catastrophic failure. Data-driven machine learning approaches have been widely used for this, due to their capability in capturing features sensitive to damage-induced anomalies from structural health monitoring (SHM) data, assuming that such data are available. Although machine learning models have been used, many are challenged by the vast operational and environmental variability that can corrupt SHM data and by (typically) strongly correlated information from different sensors in the SHM data. This paper proposes an unsupervised deep learning approach for the detection of structural anomaly based on a deep convolutional variational autoencoder (DCVAE) for feature extraction coupled with support vector data description (SVDD) for anomaly detection. The proposed DCVAE-SVDD method has several appealing strengths. First, the variational latent encoding is used to capture the features of monitoring data through a probability distribution. The integration of the Kullback–Leibler divergence in the loss function provides accurate estimation of the probability distributions. Second, the DCVAE designed with convolutional and deconvolutional operations utilizes the correlation among multisensor data to avoid loss of correlation features and achieve better performance in feature extraction. Third, the SVDD is utilized to create a minimum-volume hypersphere that contains the anomaly-sensitive statistical features of the state. The hypersphere accurately separates anomaly-sensitive statistical features of reference states of structure from the anomalous ones. A computational frame model and a laboratory grandstand model are used to evaluate the performance of the proposed method for detecting structural anomaly. The results demonstrate the superiority of the proposed DCVAE-SVDD in detection accuracy over the other commonly used structural anomaly detection methods (deep autoencoder combined with SVDD autoregressive model with one-class support vector machine, and principal component analysis).

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One-class machine learning approach for localized damage detection

Cited by 4 publications

References 19 publications

Transformative role of machine learning in design optimization of reinforced concrete frames

Transformative role of machine learning in design optimization of reinforced concrete frames

Localizing structural damage based on auto-regressive with exogenous input model parameters and residuals using a support vector machine based learning approach

Unsupervised deep learning approach for structural anomaly detection using probabilistic features

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