Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Karakostas, Christos; Quaranta, Giuseppe; Chatzi, Eleni; Zülfikar, Abdullah Can; Çetindemir, Oğuzhan; De Roeck, Guido; Döhler, Michael; Limongelli, Maria Pina; Lombaert, Geert; Apaydın, Nurdan Memişoğlu; Pakrashi, Vikram; Papadimitriou, Costas; Yeşilyurt, Ali

doi:10.1007/s10518-023-01819-3

Cited by 9 publications

(2 citation statements)

References 272 publications

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“…Today, as we aim to implement intelligent systems capable of automatically providing an estimate of the condition of the monitored structure and deciding whether or not to raise an alarm, the issue is becoming increasingly relevant. In this context, it is important to use algorithms based on threshold-based analysis techniques to accurately establish the health status of monitored structures [33]. Starting from the understanding that the likelihood of incorrectly assessing the presence of damage is less in the case of critical events of considerable intensity, such as strong earthquakes, from the perspective of developing automatic systems for damage assessment, it is also crucial to reduce the uncertainties related to the variation in vibration frequencies related to phenomena not necessarily indicative of damage.…”

Section: Introductionmentioning

confidence: 99%

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

Ditommaso,

Ponzo

2024

Buildings

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In recent years, the development of quick and streamlined methods for the detection and localization of structural damage has been achieved by analysing key dynamic parameters before and after significant events or as a result of aging. Many Structural Health Monitoring (SHM) systems rely on the relationship between occurred damage and variations in eigenfrequencies. While it is acknowledged that damage can affect eigenfrequencies, the reverse is not necessarily true, particularly for minor frequency variations. Thus, reducing false positives is essential for the effectiveness of SHM systems. The aim of this paper is to identify scenarios where observed changes in eigenfrequencies are not caused by structural damage, but rather by non-stationary combinations of input and system response (e.g., wind effects, traffic vibrations), or by stochastic variations in mass, damping, and stiffness (e.g., environmental variations). To achieve this, statistical variations of thresholds were established to separate linear non-stationary behaviour from nonlinear structural behaviour. The Duffing oscillator was employed in this study to perform various nonlinear analyses via Monte Carlo simulations.

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Section: Introductionmentioning

confidence: 99%

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

Ditommaso,

Ponzo

2024

Buildings

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“…including its operation maintenance but also the need for both the hardware and software to be adapted to each particular construction site. Various papers, including reviews [1,5,6],…”

Section: Introductionmentioning

confidence: 99%

Design of an Instant Vibration-Based Warning System and Its Operation during Relocation Works of Historic Facades

Lorenzana,

Villacorta,

Magdaleno

et al. 2024

Buildings

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Preserved listed building facades may require large-scale and highly technical work when the supporting building structure is at serious risk of collapse. Such is the case described in this paper, where vast facades must be cut into large panels up to 200 m2 and 150 t in weight and carefully laid on the ground. Various engineering works must be carried out to ensure the structural integrity of the panels to be safeguarded. Each panel must be reinforced by a temporary lattice steel structure prior to the disengagement from the supporting building frame. The operations require the use of cutting tools, hitting demolition machines and heavy cranes, which can induce potentially damaging vibrations that should be monitored and processed so that workers can be alerted in real time if certain thresholds are exceeded so that they can proceed more carefully. The paper describes the specifically designed monitoring system, its electronic parts, how they operate and how the data are processed and displayed. The monitoring system, once verified in laboratory tests, is applied to the detachment and overturning activities of a representative full-scale panel, tracking vibration levels and tilting rates. After days of operation and visual observation, it is possible to correlate vibration levels with incipient damage, establishing that peaks below 0.5 m/s2 or RMS values of 0.05 m/s2 are permissible, but that above 1.0 m/s2 or 0.3 m/s2, respectively, activities should be halted. The proposed system has proven to be useful for the intended purposes, making it possible to know the acceptable thresholds and trigger the necessary alarms in real time for the successful course of the work.

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Deep learning architectures for data-driven damage detection in nonlinear dynamic systems under random vibrations

Joseph,

Quaranta,

Carboni

et al. 2024

Nonlinear Dyn

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The primary goal of structural health monitoring is to detect damage at its onset before it reaches a critical level. In the present work an in-depth investigation addresses deep learning applied to data-driven damage detection in nonlinear dynamic systems. In particular, autoencoders and generative adversarial networks are implemented leveraging on 1D convolutional neural networks. The onset of damage is detected in the investigated nonlinear dynamic systems by exciting random vibrations of varying intensity, without prior knowledge of the system or the excitation and in unsupervised manner. The comprehensive numerical study is conducted on dynamic systems exhibiting different types of nonlinear behavior. An experimental application related to a magneto-elastic nonlinear system is also presented to corroborate the conclusions.

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Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Cited by 9 publications

References 272 publications

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

Design of an Instant Vibration-Based Warning System and Its Operation during Relocation Works of Historic Facades

Deep learning architectures for data-driven damage detection in nonlinear dynamic systems under random vibrations

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