Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo, Arman; Özer, Ekin; AlHamaydeh, Mohammad; Girolami, Mark

doi:10.1177/14759217211036880

Cited by 221 publications

(139 citation statements)

References 345 publications

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“…An important research direction that is also currently being explored is full understanding of data collected by currently existing strain sensing techniques and the assessment of capabilities of strain measurements to be used for the prediction of future structural behaviors using data-driven approaches (statistics and machine learning, e.g., [ 126 , 127 ]). While this direction does not directly address the development of sensors, it is extremely important as it emphasizes the importance of sensors beyond simple strain measurement and has the potential of enabling smart structures and transforming them into cyber-physical systems (e.g., [ 128 ]).…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Concise Historic Overview of Strain Sensors Used in the Monitoring of Civil Structures: The First One Hundred Years

Glišić

2022

Sensors

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Strain is one of the most frequently monitored parameters in civil structural health monitoring (SHM) applications, and strain-based approaches were among the first to be explored and applied in SHM. There are multiple reasons why strain plays such an important role in SHM: strain is directly related to stress and deflection, which reflect structural performance, safety, and serviceability. Strain field anomalies are frequently indicators of unusual structural behaviors (e.g., damage or deterioration). Hence, the earliest concepts of strain sensing were explored in the mid-XIX century, the first effective strain sensor appeared in 1919, and the first onsite applications followed in the 1920′s. Today, one hundred years after the first developments, two generations of strain sensors, based on electrical and fiber-optic principles, firmly reached market maturity and established themselves as reliable tools applied in strain-based SHM. Along with sensor developments, the application methods evolved: the first generation of discrete sensors featured a short gauge length and provided a basis for local material monitoring; the second generation greatly extended the applicability and effectiveness of strain-based SHM by providing long gauge and one-dimensional (1D) distributed sensing, thus enabling global structural and integrity monitoring. Current research focuses on a third generation of strain sensors for two-dimensional (2D) distributed and quasi-distributed sensing, based on new advanced technologies. On the occasion of strain sensing centenary, and as an homage to all researchers, practitioners, and educators who contributed to strain-based SHM, this paper presents an overview of the first one hundred years of strain sensing technological progress, with the objective to identify relevant transformative milestones and indicate possible future research directions.

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Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Concise Historic Overview of Strain Sensors Used in the Monitoring of Civil Structures: The First One Hundred Years

Glišić

2022

Sensors

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“…The book by Farrar and Worden [28] presents an in-depth analysis of all aspects of SHM related to ML, including main applications, data collection and processing, and ML algorithms. More recent surveys on SHM driven by ML were carried out by Khan and Yairi [29], Azimi et al [30], Toh and Park [31], Lin et al [32], Bao and Li [33], Malekloo et al [34], Lei et al [247] and Zhao et al…”

Section: Structural Health Monitoring By Machine Learningmentioning

confidence: 99%

“…Varanis and Pederiva [252] compares some feature selection methods and concludes that Linear Discriminant Analysis (LDA) is suitable for non-stationary cases, while Principal component analysis (PCA) is convenient for stationary signals and independent component analysis (ICA) for problems with combined faults. Malekloo et al [34] also reviews several supervised and unsupervised feature selection methods. The classical ML approach in SHM is addressed by Worden and Manson [260] for damage detection, localization, and assessment problems.…”

Section: Structural Health Monitoring By Machine Learningmentioning

confidence: 99%

“…Malekloo et al [34] provides a complete review of all processes that should be considered in an ML-based SHM analysis, from the excitation source, going to data acquisition, data normalization, data cleaning, data compression, feature extraction and selection, data fusion, and the prediction. Hereafter, some ML-based SHM works using vibroacoustic signals are presented to exemplify applications in damage detection and diagnosis -including damage location, extent, and typeand in the prognosis of RUL and mission planning.…”

Section: Structural Health Monitoring By Machine Learningmentioning

confidence: 99%

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A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

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The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD&V and guides the reader to an advanced understanding of progress and prospects in the field.

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“…Meanwhile, the corresponding signal processing methods such as wavelet transform and empirical mode decomposition has also been substantially studied and applied [ 4 ]. Recently, the application of smartphones, high-resolution cameras, unmanned aerial vehicles, and other non-contact sensing technologies gained prominent growth in SHM due to their advantages of low labor cost, high applicative efficiency, and so forth [ 5 ]; in line with these advancements, a myriad of machine learning and deep learning-based algorithms, typified by support vector machine, Gaussian mixture, convolutional neural network, and long short-term memory network, have been studied and proposed to process sheer amount and various dimensions of data collected, as well as to provide intelligent solutions for conventional damage detection methods [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Baseline-Free Damage Detection Method Based on Path Scanning of Lamb Waves Using Mobile Transducers

Yuan

Zhou

et al. 2022

Sensors

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The baseline-free damage detection method of Lamb waves has the potential to obtain damage information efficiently in plate structures through damage scattering signals. However, the missing detection of damage occurs occasionally due to the angular scattering characteristic of Lamb waves. To solve this problem, a novel baseline-free damage detection approach based on path scanning at the detection region edges using mobile piezoelectric transducers is proposed herein. Several sensing points carrying separated damage scattering signals were picked out from the scanning paths. By removing the direct and boundary reflected signals, the damage signals were extracted and exported to a delay-and-sum imaging method to locate the damage. Two experiments with and without mobile transducers were conducted to validate the proposed method on an aluminum plate with artificially fabricated crack-like damage. The results show that the proposed baseline-free approach can locate the crack-like damage with high accuracy and efficiency and avoid potential loss of damage information. The proposed baseline-free method provides a novel and practical damage detection approach when considering the angular-dependent scattering characteristic of Lamb waves and can enhance the credibility of results in damage detection.

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Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Cited by 221 publications

References 345 publications

Concise Historic Overview of Strain Sensors Used in the Monitoring of Civil Structures: The First One Hundred Years

Concise Historic Overview of Strain Sensors Used in the Monitoring of Civil Structures: The First One Hundred Years

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

A Novel Baseline-Free Damage Detection Method Based on Path Scanning of Lamb Waves Using Mobile Transducers

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