Structural Health Monitoring of existing structures and infrastructures combining on-site and satellite data

Health Monitoring ◽

Structural Damage ◽

Rapid Evolution ◽

Mode Shapes ◽

Operational Conditions ◽

Structural Health ◽

Existing Structures ◽

Research Fields ◽

Over Time

In the last years, existing structures and transport infrastructures, especially those made using reinforced concrete, have experienced significant safety criticalities implying also a relevant social and economic impacts. Structural Health Monitoring techniques represent a reliable response to the problem available to scientists and engineers. A multidisciplinary approach combing knowledge from several research fields and using different kind of technologies would be preferable for this type of application. Most of developed methods for structural damage detection on civil structures and infrastructures is generally based on the evaluation of displacements, eigenfrequencies, damping factors, mode shapes, etc., and their variation over time, by means of on-site installed sensors. In recent years, thanks to the rapid evolution of interferometric SAR processing techniques, a large amount of &#8220;satellite measurements&#8221; are available for both geophysical phenomena and building monitoring in terms of displacement rate over time. This paper presents an overview on the 2019-2021 WP6 Reluis Project aiming to contribute to the discussion about the opportunity and the modalities to merge information retrieved both by on-site and remote sensing measurements and to define a shared strategy to detect damage on existing structures and infrastructures in operational conditions.

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

Advances in Structural Engineering ◽

10.1177/13694332211038444 ◽

2021 ◽

pp. 136943322110384

Author(s):

Xingyu Fan ◽

Jun Li ◽

Hong Hao

Keyword(s):

Health Monitoring ◽

Structural Damage ◽

Low Frequency ◽

Data Driven ◽

Mode Shapes ◽

Monitoring Methods ◽

Destructive Testing ◽

Electromechanical Impedance ◽

Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

The role of dynamic response parameters in damage prediction

10.1177/0954406219841083 ◽

2019 ◽

Vol 233 (13) ◽

pp. 4620-4636 ◽

Cited By ~ 5

Author(s):

Behzad Ahmed Zai ◽

MA Khan ◽

Kamran A Khan ◽

Asif Mansoor ◽

Aqueel Shah ◽

...

Keyword(s):

Dynamic Response ◽

Health Monitoring ◽

Structural Damage ◽

Mode Shapes ◽

Future Research ◽

Modal Parameter ◽

Structural Health ◽

Structural Applications

This article presents a literature review of published methods for damage identification and prediction in mechanical structures. It discusses ways which can identify and predict structural damage from dynamic response parameters such as natural frequencies, mode shapes, and vibration amplitudes. There are many structural applications in which dynamic loads are coupled with thermal loads. Hence, a review on those methods, which have discussed structural damage under coupled loads, is also presented. Structural health monitoring with other techniques such as elastic wave propagation, wavelet transform, modal parameter, and artificial intelligence are also discussed. The published research is critically analyzed and the role of dynamic response parameters in structural health monitoring is discussed. The conclusion highlights the research gaps and future research direction.

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.390.192 ◽

2013 ◽

Vol 390 ◽

pp. 192-197

Author(s):

Giorgio Vallone ◽

Claudio Sbarufatti ◽

Andrea Manes ◽

Marco Giglio

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Health Monitoring ◽

Structural Damage ◽

Numerical Models ◽

Damage State ◽

Operational Conditions ◽

Structural Health ◽

Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

Implementation of a Probabilistic Structural Health Monitoring Method on a Highway Bridge

Advances in Civil Engineering ◽

10.1155/2012/307515 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Adam Scianna ◽

Zhaoshuo Jiang ◽

Richard Christenson ◽

John DeWolf

Keyword(s):

Health Monitoring ◽

Structural Damage ◽

Probability Distributions ◽

Highway Bridge ◽

Monitoring Method ◽

Operational Conditions ◽

Structural Health ◽

Bridge Superstructure ◽

Girder Bridge

This paper describes the application of a probabilistic structural health monitoring (SHM) method to detect global damage in a highway bridge in Connecticut. The proposed method accounts for the variability associated with environmental and operational conditions. The bridge is a curved three-span steel dual-box girder bridge located in Hartford, Connecticut. The bridge, monitored since Fall 2001, experienced a period of settling in the Winter of 2002-2003. While this change was not associated with structural damage, it was observed in a permanent rotation of the bridge superstructure. Three damage measures are identified in this study: the value of fundamental natural frequency determined from peak picking of autospectral density functions of the bridge acceleration measurements; the magnitude of the peak acceleration measured during a truck crossing; the magnitude of the tilt measured at 10-minute intervals. These damage measures, including thermal effects, are shown to be random variables and associatedPvalues are calculated to determine if the current probability distributions are the same as the distributions of the baseline bridge data from 2001. Historical data measured during the settling of the bridge is used to verify the performance of the bridge, and the field implementation of the proposed method is described.

An overview of 38 least squares–based frameworks for structural damage tomography

10.1177/1475921719841012 ◽

2019 ◽

Vol 19 (1) ◽

pp. 215-239 ◽

Cited By ~ 7

Author(s):

Danny Smyl ◽

Sven Bossuyt ◽

Waqas Ahmad ◽

Anton Vavilov ◽

Dong Liu

Keyword(s):

Least Squares ◽

Health Monitoring ◽

Structural Damage ◽

Tomographic Imaging ◽

Inverse Methods ◽

One Dimension ◽

Distributed Data ◽

Structural Health ◽

Static Elasticity

The ability to reliably detect damage and intercept deleterious processes, such as cracking, corrosion, and plasticity are central themes in structural health monitoring. The importance of detecting such processes early on lies in the realization that delays may decrease safety, increase long-term repair/retrofit costs, and degrade the overall user experience of civil infrastructure. Since real structures exist in more than one dimension, the detection of distributed damage processes also generally requires input data from more than one dimension. Often, however, interpretation of distributed data—alone—offers insufficient information. For this reason, engineers and researchers have become interested in stationary inverse methods, for example, utilizing distributed data from stationary or quasi-stationary measurements for tomographic imaging structures. Presently, however, there are barriers in implementing stationary inverse methods at the scale of built civil structures. Of these barriers, a lack of available straightforward inverse algorithms is at the forefront. To address this, we provide 38 least-squares frameworks encompassing single-state, two-state, and joint tomographic imaging of structural damage. These regimes are then applied to two emerging structural health monitoring imaging modalities: Electrical Resistance Tomography and Quasi-Static Elasticity Imaging. The feasibility of the regimes are then demonstrated using simulated and experimental data.

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

10.2749/ghent.2021.0555 ◽

2021 ◽

Author(s):

Huaqiang Zhong ◽

Limin Sun ◽

José Turmo ◽

Ye Xia

Keyword(s):

Data Quality ◽

Health Monitoring ◽

Structural Damage ◽

Quality Evaluation ◽

Evaluation Method ◽

Operating Conditions ◽

Monitoring Data ◽

Statistical Characteristics ◽

In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.

A Multiobjective Perspective to Optimal Sensor Placement by Using a Decomposition-Based Evolutionary Algorithm in Structural Health Monitoring

Applied Sciences ◽

10.3390/app10217710 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7710

Author(s):

Tsung-Yueh Lin ◽

Jin Tao ◽

Hsin-Haou Huang

Keyword(s):

Evolutionary Algorithm ◽

Health Monitoring ◽

Sensor Placement ◽

Frame Structure ◽

Mode Shapes ◽

Beam Model ◽

Optimal Sensor Placement ◽

Modal Assurance Criterion ◽

The objective of optimal sensor placement in a dynamic system is to obtain a sensor layout that provides as much information as possible for structural health monitoring (SHM). Whereas most studies use only one modal assurance criterion for SHM, this work considers two additional metrics, signal redundancy and noise ratio, combining into three optimization objectives: Linear independence of mode shapes, dynamic information redundancy, and vibration response signal strength. A modified multiobjective evolutionary algorithm was combined with particle swarm optimization to explore the optimal solution sets. In the final determination, a multiobjective decision-making (MODM) strategy based on distance measurement was used to optimize the aforementioned objectives. We applied it to a reduced finite-element beam model of a reference building and compared it with other selection methods. The results indicated that MODM suitably balanced the objective functions and outperformed the compared methods. We further constructed a three-story frame structure for experimentally validating the effectiveness of the proposed algorithm. The results indicated that complete structural modal information can be effectively obtained by applying the MODM approach to identify sensor locations.

IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management ◽

Structural Health Monitoring for cultural heritage constructions: a resilience perspective

10.2749/guimaraes.2019.1552 ◽

2019 ◽

Author(s):

Maria Pina Limongelli ◽

Zehra Irem Turksezer ◽

Pier Francesco Giordano

Keyword(s):

Cultural Heritage ◽

Health Monitoring ◽

Social Value ◽

Specific Reference ◽

Structural Health ◽

Heritage Constructions ◽

The Impact ◽

Support Decision Making ◽

Over Time

Disturbances or disruptive events may induce reductions of functionality of the built environment. For Cultural Heritage (CH) structures, functionalities may range from technical, to economic ones linked to touristic activities, up to intangible functionalities related to the cultural and social value of these constructions. Resilience can be defined as the capability of a system overcome a disturbance with the minimum total loss of functionality over time. Structural Health Monitoring (SHM) may enhance resilience by providing information that can support decision making, aiming to reduce the impact of the disturbances. In this paper, the benefits of SHM systems as means for improving resilience of CH structures are addressed and discussed with specific reference to the three different decision situations; before, during and after events of disturbances. Examples of real applications of SHM for CH structures and its effect on the resilience of the system conclude the paper.

Research on the Progress of Interdigital Transducer (IDT) for Structural Damage Monitoring

Journal of Sensors ◽

10.1155/2021/6630658 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Wang Ziping ◽

Xiong Xiqiang ◽

Qian Lei ◽

Wang Jiatao ◽

Fei Yue ◽

...

Keyword(s):

Health Monitoring ◽

Structural Damage ◽

Large Scale ◽

Safety Information ◽

Health And Safety ◽

Guided Wave ◽

Structural Safety ◽

Structural Health ◽

Damage Monitoring

In the application of Structural Health Monitoring (SHM) methods and related technologies, the transducer used for electroacoustic conversion has gradually become a key component of SHM systems because of its unique function of transmitting structural safety information. By comparing and analyzing the health and safety of large-scale structures, the related theories and methods of Structural Health Monitoring (SHM) based on ultrasonic guided waves are studied. The key technologies and research status of the interdigital guided wave transducer arrays which used for structural damage detection are introduced. The application fields of interdigital transducers are summarized. The key technical and scientific problems solved by IDT for Structural Damage Monitoring (SHM) are presented. Finally, the development of IDT technology and this research project are summarised.

Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education ◽

Structural Health Monitoring With Piezoelectric Active Sensors

10.1115/2000-gt-0051 ◽

2000 ◽

Cited By ~ 1

Author(s):

Howard A. Winston ◽

Fanping Sun ◽

Balkrishna S. Annigeri

Keyword(s):

Health Monitoring ◽

Structural Damage ◽

Electromechanical Coupling ◽

Low Cost ◽

Piezoelectric Element ◽

Impedance Spectrum ◽

Active Sensors ◽

Electromechanical Impedance ◽

A technology for non-intrusive real-time structural health monitoring using piezoelectric active sensors is presented. The approach is based on monitoring variations of the coupled electromechanical impedance of piezoelectric patches bonded to metallic structures in high-frequency bands. In each of these applications, a single piezoelectric element is used as both an actuator and a sensor. The resulting electromechanical coupling makes the frequency-dependent electric impedance spectrum of the PZT sensor a good mapping of the underlying structure’s acoustic signature. Moreover, incipient structural damage can be indicated by deviations of this signature from its original baseline pattern. Unique features of this technology include its high sensitivity to structural damage, non-intrusiveness to the host structure, and low cost of implementation. These features have potential for enabling on-board damage monitoring of critical or inaccessible aerospace structures and components, such as aircraft wing joints, and both internal and external jet engine components. Several exploratory applications will be discussed.