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Due to the capability of multiplexing thousands of sensors on a single optical cable, ultra-weak fiber Bragg grating (UWFBG) vibration sensing technology has been utilized in monitoring the vibration response of large-scale infrastructures, particularly urban railway tracks, and the volume of the collected monitoring data can be huge with the great number of sensors. Even though the train-induced vibration responses of urban railway tracks constitute the most informative and crucial component, they comprised less than 7% of the total operational period. This is mainly attributed to the temporal sparsity of commuting trains. Consequently, the majority of the stored data consisted of low-informative environmental noise and interference excitation data, leading to an inefficient structural health monitoring (SHM) system. To address this issue, this paper introduced an adaptive monitoring strategy for railway track structures, which is capable of identifying train-load patterns by leveraging deep learning techniques. Inspired by image semantic segmentation, a U-net model with one-dimensional convolution layers (U-net-1D) was developed for the pointwise classification of vibration monitoring data. The proposed model was trained and validated using a dataset obtained from an actual urban railway track in China. Results indicated that the proposed method outperforms the traditional dual-threshold method, achieving an Intersection over Union (IoU) of 94.27% on the segmentation task of the test dataset. [ABSTRACT FROM AUTHOR]

The implementation of Structural Health Monitoring (SHM) offers the prospect for sustainable and safe service‐life extension of existing bridges, a large portion of which is approaching the end of their nominal life. Many SHM frameworks for civil infrastructure address timely damage detection and identification. However, the scarcity of case studies on real damaged bridges hinders the generalized application of SHM in practice. In this contribution, monitoring data from a four‐day campaign on the Ponte‐Moesa bridge, a three‐span concrete box‐girder bridge, is presented as a benchmark for data‐driven damage diagnosis schemes. The monitoring data, covering accelerations from ambient and forced vibrations, contains the reference state after concluding the service life along with several gradually increasing damage states, including drilling holes and cutting reinforcement rebars and prestressed cables. The potential of damage‐sensitive features to identify damage is presented and the uncertainties, resulting from the environmental and operational conditions and sensor malfunctioning, pertaining to robust damage detection are discussed. Drawing from real bridge monitoring data, a range of prospects and open challenges of vibration‐based SHM for bridges are reviewed. [ABSTRACT FROM AUTHOR]

In this paper, an IoT sensor node, based on smart Bluetooth low energy (BLE), for the health monitoring of artworks and large wooden structures is presented. The measurements from sensors on board the node are collected in real-time and sent to a remote gateway. The sensor node allows for the monitoring of environmental parameters, in particular, temperature and humidity, with accurate and robust integrated sensors. The developed node also embeds an accelerometer, which also allows other mechanical quantities (such as tilt) to be derived. This feature can be exploited to perform structural monitoring, exploiting the processing of data history to detect permanent displacements or deformations. The node is triggered by acceleration transients; therefore, it can also generate alarms related to shocks. This feature is crucial, for instance, in the case of transportation. The developed device is low-cost and has very good performance in terms of power consumption and compactness. A reliability assessment showed excellent durability, and experimental tests proved very satisfactory robustness against working condition variations. The presented results confirm that the developed device allows for the realization of pervasive monitoring systems, in the context of the IoT paradigm, with sensor nodes devoted to the monitoring of each artwork present in a museum or in a church. [ABSTRACT FROM AUTHOR]

Deep learning techniques have been increasingly popular for detecting structural novelties in recent years. The deep learning notion originates from the theory of neural networks, and it comprises several machine learning approaches that were primarily created to solve high-dimensional and nonlinear problems due to their great data mapping capabilities. Although the basic ideas of such algorithms were established in the 1960s, their use in damage detection situations is still relatively new. In so doing, the current study assesses the Sparse Auto-Encoder (SAE) deep learning method when applied to the characterization of structural anomalies. The fundamental concept is to employ the SAE to extract significant features from monitored signals and the well-known Support Vector Machine (SVM) to classify those features within the framework of a Structural Health Monitoring (SHM) program. The proposed method is evaluated using vibration data from a numerical beam model and a highway viaduct in Brazil. The results demonstrate that the SAE can extract relevant properties from dynamic data, making it valuable for SHM applications. [ABSTRACT FROM AUTHOR]

For modern engineering infrastructures, the vibration information is critical in identifying the dynamic characteristics and assessing the structural health. Over the past few decades, Global Navigation Satellite System (GNSS) has played an increasingly important role in the monitoring of surface dynamic movement. The GNSS-based precise point positioning (PPP) has received much attention due to its technical feasibility and economic efficiency. More efforts are expected to be made in the research of high-rate PPP and the integration of multiple GNSSs for structural health monitoring. This study evaluates the performance of GPS/BDS kinematic PPP with 20-Hz observations in capturing vibrations generated from a linear shaker. A comparative analysis of the solutions obtained from GPS/BDS high-rate PPP, real-time kinematic (RTK) positioning, total station and accelerometer is performed in both frequency and displacement domains. The experimental results show that the displacement time series captured by GPS/BDS PPP and RTK can achieve an accuracy of 5.7–8.4 and 4.5–6.6 mm with respect to those from total station, respectively. The PPP-derived vibration frequency is consistent with that from RTK, total station and accelerometer, with a deviation less than 0.008 Hz among each other. For comparison, the GPS-only and BDS-only PPP results are also provided, and it is indicated that the dual-system integration can enhance the reliability and accuracy of monitoring results. For completeness, another experiment is conducted to investigate the performance difference between ambiguity-float and ambiguity-fixed PPP for vibration monitoring. [ABSTRACT FROM AUTHOR]

Objectives: Time and frequencies are important for investigating dynamic responses of offshore oil platforms. Time -frequency feature extraction is a key issue in structural health monitoring of large civil engineering structures. Current popular methods for signal analysis and processing cannot extract the characteristics of dynamic responses accurately and completely. Therefore, a new approach combining variational mode decomposition (VMD) and Hilbert-Huang transform (HHT) is proposed to extract multi-dimensional vibration characteristics of time, frequency and energy. Methods: Firstly, VMD is used to extract the vibration frequency component of events and the noise component could thus be removed. Then, HHT is applied to extract multi-dimensional vibration characteristics of time, frequency and energy using the cleaned accelerometer data. Finally, the frequency domain integration approach is introduced to compute the vibration displacements based on cleaned data processed by VMD. A series of simulation tests and ship impact test on offshore oil platform are performed using accelerometers in the MEMS IMU. Results: Experimental results show that the extraction method based on VMD-HHT can obtain the multi-dimensional time-frequency-energy characteristics accurately. The frequency domain integration approach can produce highly accurate and reliable vibration displacements using accelerometer data. Conclusions: VMD HHT feature extraction model can not only extract the vibration frequency more completely and accurately, but also clearly see the time of vibration events, the corresponding frequency range and vibration intensity. VMD HHT aided frequency domain integration approach can effectively remove the low-frequency noise and determine cut-off frequency quickly. [ABSTRACT FROM AUTHOR]

• Beam displacement reconstruction using Fiber Bragg Grating (FBG) accelerometers is studied. • Distributed strain is determined via matching displacement obtained by Finite Element Method. • An FBG accelerometer with an equal-strength beam structure is designed and developed. • Feasibility of the proposed method is experimentally verified on a 12 m steel pipe. Fiber Bragg Grating (FBG) has been widely used in vibration monitoring due to its advantages of high sensitivity, resistance to electromagnetic interference and ease of networking. This paper proposes a method of distributed strain monitoring for vibrating structures where the surface is unsuitable for attaching FBG. This method utilizes measured multi-point acceleration to calculate dynamic displacement, and subsequently reconstructs distributed strain based on displacement matching and a displacement–strain function. Finite element simulation results demonstrate that the displacement matching scheme has higher strain reconstruction accuracy and broader adaptability. An FBG accelerometer with an equal-strength beam is designed, fabricated, and tested, exhibiting a resonant frequency of 56 Hz and an average sensitivity of 70 pm·g−1 in the flat frequency band. The proposed method is experimentally validated using a 12 m steel pipeline. It's demonstrated that the displacement reconstruction error is less than 8 %, and the strain reconstruction error is less than 11 %. [ABSTRACT FROM AUTHOR]

Monitoring the structural health of wind turbine blades is essential to increase energy capture and operational safety of turbines, and therewith enhance competitiveness of wind energy. With the current trends of designing blades ever longer, detailed knowledge of the vibrational characteristics at any point along the blade is desirable. In our approach, we monitor vibrations during operation of the turbine by wirelessly measuring accelerations on the outside of the blades. We propose an algorithm to extract so-called vibration-based fingerprints from those measurements, i.e., dominant vibrations such as eigenfrequencies and narrow-band noise. These fingerprints can then be used for subsequent analysis and visualisation, e.g., for comparing fingerprints across several sensor positions and for identifying vibrations as global or local properties. In this study, data were collected by sensors on two test turbines and fingerprints were successfully extracted for vibrations with both low and high operational variability. An analysis of sensors on the same blade indicates that fingerprints deviate for positions at large radial distance or at different blade sides and, hence, an evaluation with larger datasets of sensors at different positions is promising. In addition, the results show that distributed measurements on the blades are needed to gain a detailed understanding of blade vibrations and thereby reduce loads, increase energy harvesting and improve future blade design. In doing so, our method provides a tool for analysing vibrations with relation to environmental and operational variability in a comprehensive manner. [ABSTRACT FROM AUTHOR]

Outlier ensembles are shown to provide a robust method for damage detection and dimension reduction via a wholly unsupervised framework. Most interestingly, when utilised for feature extraction, the proposed heuristic defines features that enable near-equivalent classification performance (95.85%) when compared to the features found (in previous work) through supervised techniques (97.39%) — specifically, a genetic algorithm. This is significant for practical applications of structural health monitoring, where labelled data are rarely available during data mining. Ensemble analysis is applied to practical examples of problematic engineering data; two case studies are presented in this work. Case study I illustrates how outlier ensembles can be used to expose outliers hidden within a dataset. Case study II demonstrates how ensembles can be utilised as a tool for robust outlier analysis and feature extraction in a noisy, high-dimensional feature-space. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring using raw dynamic measurements is the subject of several studies aimed at identifying structural modifications or, more specifically, focused on damage assessment. Traditional damage detection methods associate structural modal deviations to damage. Nevertheless, the process used to determine modal characteristics can influence the results of such methods, which could lead to additional uncertainties. Thus, techniques combining machine learning and statistical analysis applied directly to raw measurements are being discussed in recent researches. The purpose of this paper is to investigate statistical indicators, little explored in damage identification methods, to characterize acceleration measurements directly in the time domain. Hence, the present work compares two machine learning algorithms to identify structural changes using statistics obtained from raw dynamic data. The algorithms are based on Artificial Neural Networks and Support Vector Machines. They are initially evaluated through numerical simulations using a simply supported beam model. Then, they are assessed through experimental tests performed on a laboratory beam structure and an actual railway bridge, in France. For all cases, different damage scenarios were considered. The obtained results encourage the development of computational tools using statistical indicators of acceleration measurements for structural alteration assessment. [ABSTRACT FROM AUTHOR]

Vibration-based condition monitoring relies on capturing spatio-temporal characteristics (STCs) such as resonant frequencies and operational deflection shapes to assess the health of a system. Optical techniques have gained popularity in this field, but noise in the optical data can affect the accuracy of extracted STCs. To address this issue, ad-hoc denoising methods like total variation denoising (TVD) and deep learning-based algorithms have been used. However, these methods are often specific to particular applications. This study proposes a robust time-inferred autoencoder (TIA) framework to preserve a system's STCs while denoising its optically collected response. The TIA model is trained using videos of an undamaged vibrating structure to learn its underlying STCs. It is then used to reconstruct the dynamic response of damaged configurations of the same structure. The performance of TIA in reconstructing the dynamic response and denoising is compared to CNN-based autoencoders and TVD. Laboratory tests were conducted, and the results showed that TIA achieved an accuracy of approximately 94% in extracting the STCs, outperforming CNN-based autoencoders by around 40%. At the same time, TIA demonstrated comparable denoising accuracy as TVD. However, TIA offers more flexibility and automated processes over TVD, resulting in a case-independent method. Once the TIA model is trained, it does not require manually selecting or updating the regularizer term if the input dataset changes. Further development of the TIA framework could enhance its capabilities and enable its broader application as a robust tool for condition monitoring, contributing to improved system health assessment. [ABSTRACT FROM AUTHOR]

Information on the condition and reliability of an offshore jacket structure provided by a vibration-based structural health monitoring system can guide decisions on inspection and maintenance. When selecting the sensor setup, the designer of the monitoring system must assess its overall benefit compared to its costs before installation. The potential benefit of continuously monitoring the dynamic response of a jacket structure can be formally quantified through a value of information analysis from Bayesian decision theory. In this contribution, we present a framework for optimizing the placement of vibration sensors on offshore jacket structures by maximizing the value of information of the monitoring system. To solve the resulting discrete optimization problem, we adapt a genetic algorithm. The framework is demonstrated in a numerical example considering a redundant jacket-type steel frame. The numerical study shows that monitoring the vibration response of the frame is beneficial. Good sensor setups consist of relatively few sensors located towards the upper part of the frame. The adapted genetic algorithm performs similarly well as established sequential sensor placement algorithms and holds substantial promise for application to real jacket structures. • Optimal vibration sensor placement for offshore jackets is considered. • The optimization is based on the value of structural health monitoring. • Bayesian model updating is applied to update the system fatigue model. • A genetic algorithm is adapted to solve the optimization problem. • A numerical study shows the potential of the method for practical applications. [ABSTRACT FROM AUTHOR]

Abstract A critical issue for structural health monitoring (SHM) strategies based on pattern recognition models is a lack of diagnostic labels to explain the measured data. In an engineering context, these descriptive labels are costly to obtain, and as a result, conventional supervised learning is not feasible. Active learning tools look to solve this issue by selecting a limited number of the most informative observations to query for labels. This work presents the application of cluster-adaptive active learning to measured data from aircraft experiments. These tests successfully illustrate the advantages of utilising active learning tools for SHM, and they present the first application/adaptation of active learning methods to engineering data — a MATLAB package is available via GitHub: https://github.com/labull/cluster_based_active_learning. [ABSTRACT FROM AUTHOR]

Highlights • Application of a novel digital video processing method to structural monitoring. • Viable and effective method by fast setup data acquisition and low-cost equipment. • Detection of weak points in structures through magnified motion anomalies. • Capability of providing approximate dynamic identification of low-frequency modes. Abstract A new methodology for digital image processing, namely the Motion Magnification (MM), allows to magnify small displacements of large structures. MM acts like a microscope for motion in video sequences, but affecting only some groups of pixels. The processed videos unveil motions hardly visible with the naked eye and allow a more effective frequency domain analysis. We applied the MM method to several historic structures, including a 1:10-scale mockup of Hagia Irene in Constantinople tested on shaking table, the so-called Temple of Minerva Medica in Rome and the Ponte delle Torri of Spoleto. MM algorithms parameters were calibrated by comparison with reference consolidated modal identification methods applied to conventional velocimeters data. Encouraging results were obtained in terms of vibration monitoring and modal analysis for dynamic identification of the studied structures, offering a low-cost, viable support to the standard vibration sensing equipment, such as contact velocimeters, laser vibrometers and others. [ABSTRACT FROM AUTHOR]

Summary: Large‐scale, long‐term structural health monitoring systems have become more feasible in recent years as the required data acquisition and analysis systems are more affordable to deploy. These long‐term systems must process and store vast amounts of data without wasting computational power and storage capacity with redundant or poor quality data. While not a primary system for damage detection, large‐scale, long‐term vibration monitoring systems aim to leverage changes in the dynamic signature of a structure to assess global structural changes. Although the ability to continually collect vibration data at high rates exists, it is not always feasible to store all these data long term. As more long‐term monitoring systems are deployed, efficient methods need to be developed to quickly and efficiently analyze large quantities of vibration data so that only the most pertinent information is archived. Previous researchers have used scheduled approaches, eg, taking data every hour, or triggered sensing systems. A monitoring system on the I‐35W St. Anthony Falls Bridge, which crosses the Mississippi River in Minneapolis, Minnesota, has been collecting vibration and temperature data since the structure's opening in 2008. This provides a uniquely large data set to establish the characteristics of a good signal for output‐only system identification to consistently and efficiently capture natural frequencies and mode shapes. To this end, a system identification routine using a novel signal selection approach and modal sorting routine that leverages NExT‐ERA/DC is proposed to analyze this large data set. The resulting information allows long‐term and temperature‐based trends to be identified. [ABSTRACT FROM AUTHOR]

Structural changes are usually associated to damage occurrence, which can be caused by design flaws, constructive problems, unexpected loading, natural events or even natural aging. The structural degrading process affects the dynamic behavior, leading to modifications in modal characteristics. In general, natural frequencies are sensitive indicators of structural integrity and tend to become slightly smaller in the presence of damage. Despite this, it is very difficult to state the relationship between decreasing values of natural frequencies and structural damage, since the dynamic properties are also influenced by uncertainty on experimental data and temperature variation. In order to contribute to improving the quality of natural frequency-based methods used for damage identification, this paper presents a simple and efficient strategy to detect structural changes in a set of experimental tests from a real structure using a computational intelligence method. For a full time monitored structure, the evolution of natural frequencies and temperature are used as input data for a Support Vector Machine (SVM) algorithm. The technique consists on detecting structural changes and when they occur based on the structural dynamic behavior. The results obtained on a historic tower show the capacity of the proposed methodology for damage identification and structural health monitoring. [ABSTRACT FROM AUTHOR]

Although vision-based methods for displacement and vibration monitoring have been used in civil engineering for more than a decade, most of these techniques require physical targets attached to the structures. This requirement makes computer vision-based monitoring for real-life structures cumbersome due to need to access certain critical locations. In this study, a non-target computer vision-based method for displacement and vibration measurement is proposed by exploring a new type of virtual markers instead of physical targets. The key points of measurement positions obtained using a robust computer vision technique named scale-invariant feature transform show a potential ability to take the place of classical targets. To calculate the converting ratio between pixel-based displacement and engineering unit (millimetre), a practical camera calibration method is developed to convert pixel-based displacements to engineering unit since a calibration standard (a target) is not available. Methods and approaches to handle challenges such as low contrast, changing illumination and outliers in matching key points are also presented. The proposed method is verified and demonstrated on the UCF four-span bridge model and on a real-life structure, with excellent results for both static and dynamic behaviour of the two structures. Finally, the method requires a simple, less complicated and more cost-effective hardware compared to conventional displacement and vibration monitoring measuring technologies. [ABSTRACT FROM PUBLISHER]

Challenges concerning the automation of modal identification and tracking procedures in permanent monitoring systems for Structural Health Monitoring purposes are discussed. In this context, an automated procedure based on parametric identification methods that involve the interpretation of stabilization diagrams is proposed. The methodology comprehends two key points: (i) automatic analysis of stabilization diagrams, performed through a first check of reasonable damping ratio, a subsequent modal complexity check and a final clustering of structural modes; (ii) automated tracking of the evolution in time of the identified modal properties. The proposed modal clustering and tracking steps exploit the introduction of self-adaptable dynamic thresholds, that do not require any a priori manual tuning for the different recorded data set. Finally, the proposed approach was successfully validated using real data collected on a historic iron arch bridge. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a vibration based method using changes in frequencies to detect delamination damage in composite beams. The basis of the present detection method is to first determine how changes in natural frequencies are related to the location and severity of delamination damage and then use this information to solve the inverse problem of predicting the delamination characteristics from measured frequency changes. To study the forward problem, a theoretical model of composite beams with delaminations is built to obtain the natural frequencies as a function of delamination sizes and locations. The inverse detection of delamination is realized using a graphical method, which makes use of frequency changes in multiple modes to assess the damage characteristics. The efficiency and accuracy of the present method are validated using experimental results reported in literature. [ABSTRACT FROM AUTHOR]

With the increasing use of health usage monitoring systems on helicopters, a lot of research has been undertaken for diagnosis of transmission components. However, most of these works are performed in laboratory environments and there are hardly any published works on in-service application. In this study, we present an experience in diagnosis of a helicopter gearbox bearing using actual service data gathered from AH64D helicopters belonging to the Republic of Singapore Air Force. A number of helicopters have been found with grease leak and radial play in the tail rotor gearbox output shaft during field maintenance. Subsequent tear-down inspections of the tail rotor gearboxes revealed that they had similar defects of bearing race spalling and widespread pitting of the rolling elements. Spectral analysis was carried out on the accelerometer data from these helicopters and correlated with the tear-down inspection findings. The fault patterns exhibited correspond well to progressing stages of bearing wear and are consistent across defective gearboxes from different helicopters. It is demonstrated that simple spectral analysis can be effective in tracking progressive stages of bearing damage using both low-frequency and high-frequency bandwidths. The observed fault patterns are extracted as features for diagnosis and used to determine the bearings’ estimated time to failure for maintenance planning. [ABSTRACT FROM AUTHOR]

Abstract: Structural health monitoring aims to detect damages in man-made engineering structures by monitoring changes in their vibration response. Unsupervised learning algorithms can be used to obtain a model of the undamaged condition and detect which new samples of the structure are not in agreement with it. However, in real structures with a sensor network configuration, the number of candidate features usually becomes large. Therefore, complexity increases and it is necessary to perform feature selection and/or dimensionality reduction to achieve good detection accuracy. In this paper, we propose to exploit the three-way structure of data and apply a true multi-way data analysis algorithm: Parallel Factor Analysis. A simple model is obtained and used to train novelty detectors. The methods are tested both with real and simulated structural data to assess that the three-way analysis can be successfully used in structural health monitoring. Furthermore, the usefulness of the approach for feature selection is also analyzed. [Copyright &y& Elsevier]

Abstract: Continuous monitoring of structural vibrations is becoming increasingly common as sensors and data acquisition systems become more affordable, and as system and damage identification methods develop. In vibration-based structural health monitoring, the dynamic modal parameters of a structure are usually used as damage-sensitive features. The modal parameters are often sensitive to changing environmental conditions such as temperature, humidity, or excitation amplitude. Environmental conditions can have as large an effect on the modal parameters as significant structural damage, so these effects should be accounted for before applying damage identification methods. This paper presents results from a continuous monitoring system installed on the Dowling Hall Footbridge on the campus of Tufts University. Significant variability in the identified natural frequencies is observed; these changes in natural frequency are strongly correlated with temperature. Several nonlinear models are proposed to represent the relationship between the identified natural frequencies and measured temperatures. The final model is then validated using independent sets of measured data. Finally, confidence intervals are estimated for the identified natural frequencies as a function of temperature. The ratio of observed outliers to the expected rate of outliers based on the confidence level can be used as a damage detection index. [Copyright &y& Elsevier]

Abstract: The monitoring of progressive wear in gears using various non-destructive technologies as well as the use of advanced signal processing techniques upon the acquired recordings to the direction of more effective diagnostic schemes, is the scope of the present work. For this reason multi-hour tests were performed in healthy gears in a single-stage lab scale gearbox until they were seriously damaged. Three on-line monitoring techniques are implemented in the tests. Vibration and acoustic emission recordings in combination with data coming from oil debris monitoring (ODM) of the lubricating oil are utilized in order to assess the condition of the gears. A plethora of parameters/features were extracted from the acquired waveforms via conventional (in time and frequency domain) and non-conventional (wavelet-based) signal processing techniques. Data fusion was accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix. Principal component analysis (PCA) was utilized to reduce the dimensionality of the data matrix whereas independent component analysis (ICA) was further applied to identify the independent components among the data and correlate them to different damage modes of the gearbox. Finally heuristic rules based on characteristic values of the resulted independent components were set, realizing thus a health monitoring scheme for gearboxes. The integration of vibration, AE and ODM data increases the diagnostic capacity and reliability of the condition monitoring scheme concluding to very interesting results. The present work summarizes the joint efforts of two research groups towards a more reliable condition monitoring of rotating machinery and gearboxes specifically. [Copyright &y& Elsevier]

Over recent years, there has been much interest in the use of low-cost wireless transceivers for communication of sensor data to alleviate the expense of widely distributed cable-based sensors in structural monitoring systems. However, while the number of unique wireless sensor platforms has continued to expand rapidly, the lack of success in replicating the number of deployed sensors and sampling rates utilized in previous cable-based systems has led to disillusionment over their use for this application. This article presents a wireless sensing system designed for concurrent measurement of both static and dynamic structural response through strain transducers, accelerometers, and temperature sensors. The network protocol developed supports real-time, high-rate data acquisition from large wireless sensor arrays with essentially no data loss. The current network software enables high-rate acquisition of up to 40 channels across 20 wireless units on a single peer-to-peer network with system expansion enabled through additional networks operating simultaneously on adjacent communication channels. Elements of the system design have been specifically tailored towards addressing condition assessment of highway bridges through strain-based load ratings as well as vibration-based dynamic analysis. However, the flexible system architecture enables the system to serve essentially as an off-the-shelf solution for a wide array of wireless sensing tasks. The wireless sensing units and network performance have been validated through laboratory tests as well as dense large-scale field deployments on an in-service highway bridge. [ABSTRACT FROM AUTHOR]

Researchers and engineers at the University of Connecticut and the Connecticut Department of Transportation have been using non-destructive field monitoring to evaluate a variety of bridges in the State. This has been done to answer questions on the performance of existing bridges, refine techniques needed to evaluate different bridge components, and develop approaches that can be used to provide a continuous picture of a bridge's structural integrity. This paper reports on some of the lessons learned in this continuing research. The field monitoring studies have supplemented the regularly scheduled visual inspection program. Without field data, it would be necessary to use simplified, conservative assumptions to define the actual behavior. The field monitoring efforts have resulted in savings to the state by eliminating or reducing the scope of planned renovations and replacements. This paper shows the need and benefits in using non-destructive evaluation to determine structural health. [ABSTRACT FROM PUBLISHER]

Vibration sensors greatly benefit medical and healthcare monitoring, environmental monitoring, and structural health monitoring. However, most of them are shadowed by relatively low-frequency vibration response, the narrow operating frequency range, and operational complexity, which hinders their use in wide practical applications. Here, we report a self-powered broadband vibration sensor with a layer-powder-layer structure based on a triboelectric nanogenerator. The internal polytetrafluoroethylene (PTFE) and silver (Ag) micro powder can vibrate under the external vibration stimuli, offering distinct advantages for high-frequency vibration sensing. The high-frequency triboelectric vibration sensor exhibits a significantly broad frequency response range of 3–133 kHz. The highest response frequency is approximately 1–3 orders of magnitude higher than most previously reported triboelectric vibration sensors. Additionally, the HVS shows directional independence, a good frequency resolution of 0.01 kHz, and small hysteresis. With these capabilities, the HVS was demonstrated in burst vibration detection, rail track fracture detection, automobile engine monitoring, and geological exploration applications. The facile and effective vibration monitoring system based on the HVS can provide a platform for various vibration monitoring applications. The self-powered high-frequency vibration sensor is a promising candidate for next-generation vibration sensors. Vibration sensors are highly desirable in various fields, such as structural health monitoring and environmental monitoring. In this study, a self-powered high-frequency vibration sensor (HVS) is developed. The optimal HVS features a broad vibration frequency response range of 3–133 kHz, omnidirectional response, and good frequency resolution ability. The HVS was demonstrated in rail track fracture detection, automobile engine monitoring, and geological exploration applications. The HVS is a promising alternative to commercial piezoelectric vibration sensors for wide applications. [Display omitted] • A sensor with high-frequency vibration response-ability and a broad operating frequency range is developed. • The sensor shows an omnidirectional response, a good frequency resolution of 0.01 kHz, and small hysteresis. • The sensor is demonstrated in rail track fracture detection, automobile engine monitoring, and geological exploration. [ABSTRACT FROM AUTHOR]