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Showing papers on "Structural health monitoring published in 2021"


Journal ArticleDOI
TL;DR: This paper aims to fulfill the gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.

440 citations


Journal ArticleDOI
TL;DR: In this paper, the feasibility of using optical fiber sensing technology for marine application is discussed and a review of optical fiber sensors employed for marine environment and marine structural health monitoring are summarized for the understanding of their basic sensing principles.
Abstract: Optical fiber sensors have attracted considerable attention for marine environment and marine structural health monitoring, owing to advantages including resistance to electromagnetic interference, durability under extreme temperature and pressures, light weight, high transmission rate, small size and flexibility. In this paper, the optical fiber sensors employed for marine environment and marine structural health monitoring are summarized for the understanding of their basic sensing principles, and their various sensing applications such as physical parameters, chemical parameters and structural health monitoring. This review paper shows the feasibility of using optical fiber sensing technology for marine application and, due to the aforementioned advantages, it is possible to envisage a widespread use in this research field in the next few years.

184 citations


Journal ArticleDOI
TL;DR: A detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance and a brief conclusion on potential future research directions of CNN in structural condition assessment is presented.

148 citations


Journal ArticleDOI
TL;DR: The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHm has been provided, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.
Abstract: Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.

143 citations


Journal ArticleDOI
05 Mar 2021-Sensors
TL;DR: In this paper, the authors present a comprehensive collection of recently published research articles on Structural Health Monitoring (SHM) campaigns performed by means of Distributed Optical Fiber Sensors (DOFS).
Abstract: The present work is a comprehensive collection of recently published research articles on Structural Health Monitoring (SHM) campaigns performed by means of Distributed Optical Fiber Sensors (DOFS). The latter are cutting-edge strain, temperature and vibration monitoring tools with a large potential pool, namely their minimal intrusiveness, accuracy, ease of deployment and more. Its most state-of-the-art feature, though, is the ability to perform measurements with very small spatial resolutions (as small as 0.63 mm). This review article intends to introduce, inform and advise the readers on various DOFS deployment methodologies for the assessment of the residual ability of a structure to continue serving its intended purpose. By collecting in a single place these recent efforts, advancements and findings, the authors intend to contribute to the goal of collective growth towards an efficient SHM. The current work is structured in a manner that allows for the single consultation of any specific DOFS application field, i.e., laboratory experimentation, the built environment (bridges, buildings, roads, etc.), geotechnical constructions, tunnels, pipelines and wind turbines. Beforehand, a brief section was constructed around the recent progress on the study of the strain transfer mechanisms occurring in the multi-layered sensing system inherent to any DOFS deployment (different kinds of fiber claddings, coatings and bonding adhesives). Finally, a section is also dedicated to ideas and concepts for those novel DOFS applications which may very well represent the future of SHM.

98 citations


Journal ArticleDOI
TL;DR: In this article, the most promising type of sensors, laboratory made and commercially available, for structural health monitoring of aerospace composites are discussed, including sensors, wiring and cabling, data acquisition devices and software, data storage equipment, power equipment and algorithms for signal processing.

88 citations


Journal ArticleDOI
TL;DR: This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.
Abstract: Structural health monitoring (SHM) is an important aspect of the assessment of various structures and infrastructure, which involves inspection, monitoring, and maintenance to support economics, quality of life and sustainability in civil engineering. Currently, research has been conducted in order to develop non-destructive techniques for SHM to extend the lifespan of monitored structures. This paper will review and summarize the recent advancements in non-destructive testing techniques, namely, sweep frequency approach, ground penetrating radar, infrared technique, fiber optics sensors, camera-based methods, laser scanner techniques, acoustic emission and ultrasonic techniques. Although some of the techniques are widely and successfully utilized in civil engineering, there are still challenges that researchers are addressing. One of the common challenges within the techniques is interpretation, analysis and automation of obtained data, which requires highly skilled and specialized experts. Therefore, researchers are investigating and applying artificial intelligence, namely machine learning algorithms to address the challenges. In addition, researchers have combined multiple techniques in order to improve accuracy and acquire additional parameters to enhance the measurement processes. This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.

79 citations


Journal ArticleDOI
TL;DR: In this paper, the static and dynamic theory of cables is summarized and the traditional and innovative monitoring methods of cable force are analyzed, especially the recent emerging intelligent methods are provided for the future development of cable-stayed bridges.

77 citations


Journal ArticleDOI
TL;DR: In this article, the use of distributed optical fiber sensors (DOFS) based on Optical Frequency Domain Reflectometry of Rayleigh backscattering for Structural Health Monitoring purposes in civil engineering structures is investigated.
Abstract: This paper investigates the use of distributed optical fiber sensors (DOFS) based on Optical Frequency Domain Reflectometry of Rayleigh backscattering for Structural Health Monitoring purposes in civil engineering structures. More specifically, the results of a series of laboratory experiments aimed at assessing the suitability and accuracy of DOFS for crack monitoring in reinforced concrete members subjected to external loading are reported. The experiments consisted on three-point bending tests of concrete beams, where a polyamide-coated optical fiber sensor was bonded directly onto the surface of an unaltered reinforcement bar and protected by a layer of silicone. The strain measurements obtained by the DOFS system exhibited an accuracy equivalent to that provided by traditional electrical foil gauges. Moreover, the analysis of the high spatial resolution strain profiles provided by the DOFS enabled the effective detection of crack formation. Furthermore, the comparison of the reinforcement strain profiles with measurements from a digital image correlation system revealed that determining the location of cracks and tracking the evolution of the crack width over time were both feasible, with most errors being below +/- 3 cm and +/- 20 mu m, for the crack location and crack width, respectively.

72 citations


Journal ArticleDOI
TL;DR: A wide-ranging review of static and dynamic studies published on SHM and NDT of slender masonry structures summarizing and discussing the different experimental techniques used is presented in this article.

66 citations


Journal ArticleDOI
TL;DR: A framework is proposed to model a population of nominally-identical systems, such that (complete) datasets are only available from a subset of members.

Journal ArticleDOI
TL;DR: A mathematical underpinning for when domain adaptation is possible in a structural dynamics context is provided, with reference to topology within a graphical representation of structures.

Journal ArticleDOI
TL;DR: An effective approach for damage detection by using a recently developed novel swarm intelligence algorithm, i.e. the marine predator algorithm (MPA), and the superior and stable performance of MPAFNN proves its effectiveness.

Journal ArticleDOI
TL;DR: A design and manufacturing method of a stretchable and large-scale guided wave sensor network that can be applied to both active and passive guided wave–based structural health monitoring of composite structures, including damage imaging and impact imaging is proposed.
Abstract: Aircraft smart skin technology requires the integration of large-scale, lightweight, and integrative sensor networks with aircraft structural skin, but it is difficult to directly manufacture such ...

Journal ArticleDOI
TL;DR: The real-time monitoring and response of buildings is discussed, which includes both static and dynamic analyses along with numerical simulation studies such as finite element analysis (FEA), and recommendations for the future research and development of SHM are made.
Abstract: This study investigated operational and structural health monitoring (SHM) as well as damage evaluations for building structures. The study involved damage detection and the assessment of buildings by placing sensors and by assuming weak areas, and considered situations of assessment and self-monitoring. From this perspective, advanced sensor technology and data acquisition techniques can systematically monitor a building in real time. Furthermore, the structure’s response and behavior were observed and recorded to predict the damage to the building. In this paper, we discuss the real-time monitoring and response of buildings, which includes both static and dynamic analyses along with numerical simulation studies such as finite element analysis (FEA), and recommendations for the future research and development of SHM are made.

Journal ArticleDOI
TL;DR: It is believed that the proposed technologies can provide an economical and relatively non-invasive tool for real-time structural monitoring and that the availability of large amounts of data from actual measurements can give effective information on the structural behaviour of historic constructions.
Abstract: The recent developments of micro-electro-mechanical systems and wireless sensor networks allow today the use of low-cost and small-size sensors for continuous monitoring of civil structures. Both t...

Journal ArticleDOI
TL;DR: Carbon nanomaterial-coated piezoresistive fiber sensors are finding applications in many industries for in-situ process and structural health monitoring of composites as mentioned in this paper.
Abstract: Carbon nanomaterial-coated piezoresistive fiber sensors are finding applications in many industries for in-situ process and structural health monitoring of composites. These nanomaterials are embedded within the composite in two different ways; either by incorporating them in the matrix, or by depositing them on fibrous reinforcements. This review highlights the utility of carbon nanomaterials as deposition materials for fiber reinforcements and turning them into sensors for process monitoring during manufacturing and structural health monitoring during in-service life. A number of different strategies to coat carbon nanomaterials on fiber reinforcements are also discussed. A review of various monitored parameters during composites manufacturing such as reinforcement compaction response, flow-front tracking, and resin gelation and cure, as well as damage detection of finished composites using nanomaterial-coated in-situ sensors is also presented. Finally, current and future challenges are discussed where new types of 2D materials and their hybrids for next generation smart sensors are highlighted.

Journal ArticleDOI
TL;DR: This work proposes a low-cost wireless sensor node specifically designed to support modal analysis over extended periods of time with long-range connectivity at low power consumption and uses very cost-effective MEMS accelerometers and exploits the Narrowband IoT protocol to establish long-distance connection with 4G infrastructure networks.
Abstract: Monitoring of civil infrastructures is critically needed to track aging, damages and ultimately to prevent severe failures which can endanger many lives. The ability to monitor in a continuous and fine-grained fashion the integrity of a wide variety of buildings, referred to as structural health monitoring, with low-cost, long-term and continuous measurements is essential from both an economic and a life-safety standpoint. To address these needs, we propose a low-cost wireless sensor node specifically designed to support modal analysis over extended periods of time with long-range connectivity at low power consumption. Our design uses very cost-effective MEMS accelerometers and exploits the Narrowband IoT protocol (NB-IoT) to establish long-distance connection with 4G infrastructure networks. Long-range wireless connectivity, cabling-free installation and multi-year lifetime are a unique combination of features, not available, to the best of our knowledge, in any commercial or research device. We discuss in detail the hardware architecture and power management of the node. Experimental tests demonstrate a lifetime of more than ten years with a 17000 mAh battery or completely energy-neutral operation with a small solar panel (60 mm $\times$ 120 mm). Further, we validate measurement accuracy and confirm the feasibility of modal analysis with the MEMS sensors: compared with a high-precision instrument based on a piezoelectric transducer, our sensor node achieves a maximum difference of 0.08% at a small fraction of the cost and power consumption.

Journal ArticleDOI
TL;DR: A DT framework based on cloud computing and deep learning (DL) for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance.
Abstract: Digital Twin technology has recently gathered pace in the engineering communities as it allows for the convergence of the real structure and its digital counterpart throughout their entire life-cycle. With the rapid development of supporting technologies, including machine learning, 5G/6G, cloud computing, and Internet of Things, Digital Twin has been moving progressively from concept to practice. In this paper, a Digital Twin framework based on cloud computing and deep learning for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance. The framework consists of structural components, device measurements, and digital models formed by combining different sub-models including mathematical, finite element, and machine learning ones. The data interaction among physical structure, digital model, and human interventions are enhanced by using cloud computing infrastructure and a user-friendly web application. The feasibility of the proposed framework is demonstrated via case studies of damage detection of model bridge and real bridge structures using deep learning algorithms, with high accuracy of 92%.

Journal ArticleDOI
TL;DR: A novel tunnel-lining crack recognition system is established and an improved segmenting method combining adaptive partitioning, edge detection and threshold method to improve the recognition accuracy is proposed.

Journal ArticleDOI
05 Mar 2021-Sensors
TL;DR: In this article, a shortlist of three well-established signal decomposition algorithms has been selected for an in-depth analysis, namely, empirical mode decomposition (EMD), Hilbert Vibration Decomposition (HVD), and the Variational Mode Decompposition (VMD), and compared with a numerical case study and a well-known experimental benchmark.
Abstract: Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks. However, the investigation of real-life vibration measurements is quite compelling. For a better understanding of its dynamic behaviour, a multi-degree-of-freedom system should be efficiently decomposed into its independent components. However, the target structure may be affected by (damage-related or not) nonlinearities, which appear as noise-like distortions in its vibrational response. This response can be nonstationary as well and thus requires a time-frequency analysis. Adaptive mode decomposition methods are the most apt strategy under these circumstances. Here, a shortlist of three well-established algorithms has been selected for an in-depth analysis. These signal decomposition approaches-namely, the Empirical Mode Decomposition (EMD), the Hilbert Vibration Decomposition (HVD), and the Variational Mode Decomposition (VMD)-are deemed to be the most representative ones because of their extensive use and favourable reception from the research community. The main aspects and properties of these data-adaptive methods, as well as their advantages, limitations, and drawbacks, are discussed and compared. Then, the potentialities of the three algorithms are assessed firstly on a numerical case study and then on a well-known experimental benchmark, including nonlinear cases and nonstationary signals.

Journal ArticleDOI
TL;DR: A Segment based Conditional Generative Adversarial Network (SegGAN), which is a powerful deep learning model for solving pixel-to-pixel tasks, is proposed to conduct structural dynamic response reconstruction and produces outstanding reconstruction results in both time and frequency domains.

Journal ArticleDOI
TL;DR: In this article, an application of embedded fiber Bragg grating (FBG) sensors arrays for evaluation of complex composite marine structures is presented, which includes spectra analyses for thin laminate sample (skin), a composite sandwich panel and fast patrol boat hull (sandwich structure).

Journal ArticleDOI
22 Oct 2021-Symmetry
TL;DR: In this article, a review of vibration-based SHM methods in terms of the vibrational parameters used is presented, and technical codes on vibration based SHM system have also been reviewed, since they are more important in engineering applications.
Abstract: Structural damages occur in modern structures during operations due to environmental and human factors. The damages accumulating with time may lead to a significant decrease in structure performance or even destruction; natural symmetry is broken, resulting in an unexpected life and economic loss. Therefore, it is necessary to monitor the structural response to detect the damage in an early stage, evaluate the health condition of structures, and ensure the operation safety of structures. In fact, the structure and the evaluation can be considered as a special symmetry. Among several SHM methods, vibration-based SHM techniques have been widely adopted recently. Hence, this paper reviews the vibration-based SHM methods in terms of the vibrational parameters used. In addition, the technical codes on vibration based SHM system have also been reviewed, since they are more important in engineering applications. Several related ISO standards and national codes have been developed and implemented, while more specific technical codes are still required to provide more detailed guidelines in practice to maintain structure safety and natural symmetry.

Journal ArticleDOI
TL;DR: The results indicate that the proposed Bayesian dynamic linear model-based approach for detecting anomalies of the structural health monitoring data exhibits good accuracy and high computational efficiency and also allows for reconstructing the strain measurements to replace anomalies.
Abstract: Enormous data are continuously collected by the structural health monitoring system of civil infrastructures. The structural health monitoring data inevitably involve anomalies caused by sensors, t...

Journal ArticleDOI
TL;DR: The results demonstrate the advantages of the proposed methodology by improving damage detectability in all the different damage scenarios despite the influence of EOVs in both the simulated and real data.

Journal ArticleDOI
TL;DR: In this article, the inverse finite element method (iFEM) has been used for shape sensing and for damage identification in a Structural Health Monitoring framework without any a-priori knowledge of the material properties or the loading condition.

Journal ArticleDOI
TL;DR: The experimental results from the damage indices based on the extracted features demonstrate the robustness, superiority, and more sensitivity of the complete ensemble empirical mode decomposition with adaptive noise technique method in addressing the damage location, classifying the severity, and detecting the damage compared to empirical Mode decomposition and ensemble empiricalMode decomposition techniques.
Abstract: Signal processing is one of the essential components in vibration-based approaches and damage detection for structural health monitoring. Since signals in the real world are often nonlinear and non...

Journal ArticleDOI
TL;DR: This paper proposes a combination of Particle Swarm Optimization and Support Vector Machine (PSO-SVM) for damage identifications inspired by the effective searching capability of PSO, which can eliminate the redundant input parameters and robust SVM technique to classify damage locations effectively.
Abstract: Structural health monitoring (SHM) and Non-destructive Damage Identification (NDI) using responses of structures under dynamic excitation have an imperative role in the engineering application to make the structures safe. Interpretations of structural responses known as inverse problems are emerging topics with a large body of works in the literature. They have been widely solved with Machine Learning (ML) techniques such as Artificial Neural Network (ANN), Deep Neural Network (DNN), Adaptive Network-based Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). Nonetheless, these approaches can precisely predict the inverse problems of civil structures (e.g., truss or frame systems) with low damage levels, which have to wait until the structures reach certain damage or deteriorate level. The issue is related to the fact that most of the real structures have very low damage levels during their routine maintenances and usually be neglected due to limitations of the current techniques. This paper proposes a combination of Particle Swarm Optimization and Support Vector Machine (PSO-SVM) for damage identifications. The proposed approach is inspired by the effective searching capability of PSO, which can eliminate the redundant input parameters and robust SVM technique to classify damage locations effectively. In other words, natural frequencies and mode shapes extracted from the numerical examples of truss and frame structures are used as input parameters in which the redundant parameters might lead to reduction of the accuracy in the predicting models. The proposed PSO-SVM shows superior accuracy prediction in both damage locations and damage levels compared to the other ML models. It also substantially outperforms other ML models through validated cases of low damage levels.

Journal ArticleDOI
TL;DR: This paper presents a surrogate-based model updating approach for online assessment of historic buildings and its application to a medieval masonry tower, the Sciri Tower in Perugia (Italy), and demonstrates the suitability of the proposed methodology for tracking the temperature-dependent intrinsic properties of the tower.
Abstract: Structural Health Monitoring (SHM) based on Automated Operational Modal Analysis (A-OMA) has gained increasing importance in the conservation of heritage structures over recent decades. In this con...