A Non‐linear Manifold Strategy for SHM Approaches

Dervilis, Nikolaos; Antoniadou, Ifigeneia; Cross, Elizabeth J.; Worden, Keith

doi:10.1111/str.12143

Cited by 12 publications

(11 citation statements)

References 29 publications

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“…Mapping function does not need to be known a priory Choice of the kernel and multiple refitting are required Dervilis et al, 144 Xiao et al 145 Nonlinear ICA and its variations Back projection/reconstruction can be implemented More complex than ICA Dervilis and colleagues, 146,147 Sun et al 148 LLE Accurate in preserving local structure Less accurate in preserving global structure difficulty on non-convex manifolds Flexa et al, 77 García-Macías and Ubertini, 115 Nguyen et al, 149 Zhang et al 150 AANN Mapping function does not need to be known a priory High computational complexity Autoencoders Ma et al, 151 Wang et al 152 DA Can find different levels of features Liu et al, 106 Mboo and Hameyer 124 SA Can be inefficient for massive data…”

Section: Kpca and Its Variationsmentioning

confidence: 99%

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

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

246

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Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

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Section: Kpca and Its Variationsmentioning

confidence: 99%

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

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

246

View full text Add to dashboard Cite

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“…Zheng et al (2019, 2021) proposed a bridge influence surface identification method based on empirical mode decomposition to study the deterioration of bridges caused by vehicles during long-term service life. Dervilis et al (2015) proposed a novel detection method based on a nonlinear manifold strategy. The proposed method can prevent false alarms in bridge performance warnings.…”

Section: Introductionmentioning

confidence: 99%

Cable effective length model error-based bridge performance warning method under thermal action

Wang

Yang

Zhou

et al. 2021

Advances in Structural Engineering

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The cables of long-span cable-stayed bridges are subjected to substantial tension during long-term service and are more susceptible to corrosion and fatigue failure than concrete structures. Most existing structural health monitoring (SHM) systems do not have monitoring equipment to directly measure cable length, and long-term monitoring of the change in cables is less involved. The displacement response of a bridge is induced by the combination of dynamic effects (wind and highways) and quasi-static effects (temperature). In this paper, the dynamic responses were eliminated by averaging the displacement data for 10 min, and the relationship between temperature and displacement was studied. Based on the monitoring data, the distribution of the thermal field for the bridge was studied and the time variability of the tower displacement was investigated. The correlation was analyzed to study the relationship between the temperature and the tower displacements, the north tower–south tower distance and the tower–girder distances. A strong linear relationship between the temperature and quasi-static responses of the displacements was observed. The thermal expansion coefficient of the effective length of cables was proposed as a quantitative index for long-term cable monitoring. The error in the cable effective length is proposed as the warning index for performance warning research. The results show that the proposed performance warning method can monitor cables and perform warnings when the cable is damaged.

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“…However, EOP-born variations in measured structural responses, known to mimic real damage states of the structure, underline the necessity of utilization of SHM strategies which rely on elimination or integration of environmental factors from/with obtained structural performance indicators [1][2][3][4]6]. On the other hand, structures characterized with time-varying dynamics are resilient to traditionally applied Operational Modal Analysis (OMA)-based methods limited to implementation with time invariant systems [7].…”

Section: Introduction and Conceptmentioning

confidence: 99%