Structural Monitoring System Research Articles

20 years of monitoring of a steel jacket offshore platform: variability of environmental and modal parameters

The fixed steel offshore platform VEGA-A has been operating in the Sicily channel since 1988. The platform, one of the largest in the Mediterranean Sea, is equipped with a monitoring system that collects environmental (by means of a current meter, a depth gauge, and a system for detecting wind speed and direction) and structural (by means of nine accelerometers) data. After a description of the recording system, this paper provides a statistical interpretation of the structural response of the platform over the last 20 years. The structural monitoring system is aimed at performing dynamic identification (i.e., evaluating main frequencies and mode shapes), and part of the acquired data is directly processed on the platform in order to constantly check their representativeness in relation to the structural control of the platform. The possible variation of main frequencies and mode shapes over time may be due to both changes in environmental loads/conditions and/or structural damage; in this respect, a data-driven approach capable of recognizing anomalies in the dynamic behavior was adopted here to assess the performances of the platform over the 20 years monitoring period.

Technical aspect of the operation of accelerometers as composition of a system for monitoring engineering structures of a cable-stayed bridge over the Petrovsky canal in the construction of the Western Speed Diameter highway in Saint Petersburg

Aim. This study analyzes the current monitoring system for the engineering structures of a cable-stayed bridge across the Petrovsky Canal, part of the Western High-Speed Diameter highway in St. Petersburg, focusing on emergency situations where accelerometers on pylons record values that exceed limits. Methods and Materials. The study utilizes statistical data from existing monitoring system databases. The tasks, set within the framework of the study, are accomplished by applying theoretical scientific methods including analytical techniques, mathematical statistics, and induction. Results. The analysis presents an overview of the current system used for monitoring engineering structures and highlights issues related to emergency situations in which accelerometer readings exceed threshold values. This study proposes a method for assessing the reliability of the monitoring system by classifying the data obtained from the accelerometer into two groups. The research identifies the need for further investigations to understand the root causes of these issues. Conclusion. Тhe results can assist in the design and installation of monitoring systems of the engineering structures for cable-stayed bridges, as well as for modernization and optimization of existing monitoring systems to improve the quality of structural technical condition assessments.

The Effects of Seismic Behavior on High Ground Stress Soft Rock Tunnel: A Review

The purpose of this review is to critically assess seismic activity's effects on soft rock tunnels under high ground stress scenarios. The paper seeks to identify key novelties and research gaps in the existing literature, offering new insights into analytical techniques, excavation methods, support systems, and monitoring technologies. A comprehensive review of recent studies was conducted, focusing on seismic behavior, analytical techniques, and mitigation strategies for soft rock tunnels. Case studies were selected based on their relevance to high ground stress conditions and their contribution to understanding seismic resilience. Significant findings include the identification of specific geological conditions that exacerbate seismic risks and the comparative effectiveness of various analytical techniques and support systems. Novel insights into the interaction of structural reinforcements and monitoring systems are also discussed. The review highlights new analytical techniques and advanced monitoring systems that improve predictive accuracy and early detection of seismic risks. It also proposes a refined approach to integrating mitigation strategies for enhanced tunnel resilience. Doi: 10.28991/CEJ-2024-010-09-020 Full Text: PDF

Robustness of Structural Health Monitoring Systems for Offshore Energy Structures based on Damage Characteristics

Offshore energy structures such as fixed/floating wind turbines, tidal turbines and wave energy converters are exposed to loads coming from different sources. Recent advances in SHM offer many unique opportunities to assess the structural integrity of offshore energy infrastructure. The process of SHM generally involves the use of a system of sensors mounted on a structure; the processing of signals received from the sensors to attain damage sensitive features; and the detection of damage in the structure. Over the past decades, most of the research in SHM has been focused on improving and developing new sensor technology, signal processing, and damage detection algorithms. However, there are very few studies assessing the performance of SHM systems with respect to damage detection, localization, and quantification. Moreover, most of the performance analysis models for SHM systems rely only on 'probability of detection (POD)', that is an assessment index used in NDT to evaluate the probability of detecting a damage as a function of its size. For SHM systems, the replicability of POD is influenced by the environment, measurement noise, and the deterioration of sensors and instruments. Therefore, the distribution of POD is often determined by performing an extensive number of test sets with existing damage of varying magnitude. This paper aims to reduce the volume of experimental data required, as well as the uncertainties associated with POD prediction by using a model-assisted probability of detection (MAPOD) approach. We develop a Bayesian network (BN) method to evaluate the performance of SHM systems in terms of damage detection, localization, and assessment. In addition to POD, the probability of accurate localization (POAL) and probability of accurate assessment (POAA) are also incorporated into the analysis. The BN and limit state functions are applied to a fixed offshore energy structure equipped with a vibration based SHM system.

Assessment of the dynamic parameters of a 673-meter cable-stayed bridge with wireless three-component MEMS-based sensors

Dynamic structural monitoring of bridges under ambient vibrations is a method to assess dynamic parameters of structures with low intrusiveness, e.g. with Operational Modal Analysis (OMA) data processing. Yet, several barriers hinder its adoption for real world applications, such as a lack of adequate sensors or industrial sensor networks and more generally, a lack of integrated solutions. In this paper, we present a data collection campaign using a novel wireless sensing system for dynamic structural monitoring of civil engineering structures. The core enabler of this SHM product named S-lynks is the use of a novel ultra-low noise seismic MEMS accelerometer, QuietSeisTM, which makes possible to envision a true industrial wireless sensor network for OMA in civil engineering structures. We developed a bespoke wireless sensing system composed of Wireless Sensor Nodes (WSN) and Gateways (WSG) and combining Low Power Wide Area Network (LPWAN) and Wireless Local Area Network (WLAN) technologies to manage the WSN wake up, synchronization and data transmission to WSG. This product has been used on a 675-meter cable-stayed bridge to assess the dynamic parameters from ambient vibrations measurements. We successfully triggered 120 measure points on the deck and inside a tower in less than 10 hours. More than 20 modes are identified between 0 and 5 Hz, with well-defined mode shapes. Extra measurements on a few cables to identify the tension allow explaining the asymmetry observed in some mode shapes.

Continuous mapping of deformation over a ship structure model based on pointwise measurements

The development of structural monitoring systems on-board vehicles aims to achieve a full-picture of the vehicle response under operating conditions. The use of different data sources is a key point for many SHM approaches which demand for extensive information and data fusion techniques to address the specific inverse problem in a robust way. Therefore, a procedure to transform pointwise measurements into a continuous representation of the monitored structure is presented in this paper. To achieve the desired monitoring goals, a numerical approach which combines experimental recorded data and modelled responses from a ship structural model is developed. Craig-Bampton truncation technique is applied on the FE vehicle model to reduce elastic dofs and obtain a computationally cost-effective model. The main advantage of Craig-Bampton modes is the capability to keep unchanged the information at the sensor nodes. A state observer is then built to evaluate the elastic deflection field and the loads over the structure in the points where no measurement is available. A natural second-order observer is exploited as it is more suited for the present application than the Kalman filter. To enhance the observer performances, an optimization process is carried out based on a learning phase. Following a previous paper about theoretical aspects of the virtual sensing approach on 1D structures. the developed techniques are here applied to the monitoring of the structural deflections and the ambient excitation relative to an elastically scaled model of a fast catamaran, tested in the towing-tank. Accelerations and strain-gage data are used to reconstruct the elastic deformations of the physical model metallic backbone scaling the reference ship behavior. Hydrodynamic load data relative to the demihull portions and the wetdeck are obtained as well.

Enhanced prediction of reflected spectrum for FBG sensors with metallic coating embedded in CFRP composites: Unveiling the impact of process-induced residual stress and coating thickness

The occurrence of peak-split or distortion in the reflected light of fiber Bragg grating (FBG) sensors with metallic coatings embedded in composites is inevitable during the curing process, regardless of protection layers. In this study, we present a comprehensive methodology to numerically predict the reflected spectrum of metallic-coated FBG sensors, considering the process-induced residual stress in carbon fiber/epoxy composites. The finite element analysis was utilized to simulate the residual stress, which primarily arises from mechanical, thermal, and chemical cure mechanisms of the composites, including the thermosetting resin. Subsequently, the reflected spectra were calculated using the coupled mode theory. Contrary to common expectations, our findings indicate that the coating thickness has minimal influence on the reflected spectrum, while the residual stress and embedding position significantly impact it. By employing this proposed methodology, the number of experimental trials can be reduced, enabling the development of robust structural and state monitoring systems for composites using metallic-coated FBG sensors.

A Supervised Machine Learning Monitoring System for Vehicle-Railway Bridge Collision

Vehicle collisions on bridges is an important issue for the transportation infrastructure management. This study explores the significance of bridge monitoring and the benefits of employing machine learning (ML) techniques to detect and classify vehicle-deck collisions on railway bridges. The ultimate goal is to transition from traditional bridge monitoring methods to a real-time monitoring system based on a machine learning approach, aiming to improve efficiency and accuracy in detecting bridge issues. Multiple supervised ML algorithms are evaluated to identify the most accurate model for collision detection and signal categorization. The selected ML model employs a distributed approach, enhancing its adaptability and integration into a comprehensive monitoring system for diverse bridge structures. The dataset comprises frequency, velocity, and displacement measurements collected over a one-year monitoring period from three distinct railway bridges. Additionally, a controlled experiment was conducted to identify signal patterns associated with collisions of different energy levels. The collected data underwent rigorous processing, including data cleaning, synchronization, pattern identification, and statistical analysis, to extract relevant features. The proposed model achieved an accuracy of 100% in detecting vehicle-deck collisions on railway bridges and demonstrated high accuracy in classifying other types of signals. The model provides bridge managers with a valuable digital decision support tool that aids in evaluating bridge conditions, minimizing maintenance costs, and ensuring train user safety. Furthermore, the developed approach aids in reducing disk storage and saving energy in embedded systems, enhancing its practicality and sustainability in real-world applications.

Monitoring framework development for a network of multiple laboratory structures

The stadium structures have unique structural features increasing the significance of structural monitoring systems specifically designed for them. Aside from vibrations serviceability concerns and human-induced excitations, the development and propagation of structural damage under all possible atmospheric and seismic conditions need to be closely monitored for structural resiliency and integrity of the stadia. As such, Structural Health Monitoring (SHM) methods combined with effective data evaluation methodologies need to be deployed to monitor the structural performance of stadiums. Even though stadia monitoring has been performed at multiple locations in the world, a web based and real-time SHM network of stadia is not known to authors. As a preliminary study for the network implementation of stadia monitoring with acceleration measurements, the presented work focuses on the fundamental steps to accomplish this goal, with a collaborative research effort between Qatar University, the University of Central Florida, and University of Alberta. The authors performed analytical investigations and experimental testing on stadium-type structures built in laboratory environments for the development of the SHM framework. Specialized signal processing algorithms, sensing suites and approaches considering multi-scale monitoring were used on collected acceleration measurements. The novelty of the work presented in this manuscript are the following items which exist simultaneously in the developed SHM framework. The developed framework is a web-based monitoring application where structural damage is detected in real-time. The proposed methodology operates directly on raw acceleration signals and runs at a network level. With that, the damage detection, damage localization, and damage quantification tasks are performed simultaneously, while the feature extraction and classification stages are combined in one learning body.

An ultrafast and robust structural damage identification framework enabled by an optimized extreme learning machine

Artificial intelligence (AI) has recently been implemented in structural health monitoring (SHM) systems for damage detection and identification. However, existing AI methods often involve a high number of parameters, resulting in the laborious workload during model training and implementation. In this study, we develop a lightweight damage identification framework embedded with an optimized extreme learning machine (ELM) using a significantly reduced number of parameters from frequency features of the structural vibrational signals. Coupled with the ensemble empirical mode decomposition (EEMD), the frequency features were abstracted from the structural vibrational signals in the data pre-processing step. Then, a metaheuristic algorithm (chaos game optimization, CGO) was chosen to optimize the model weight of the ELM to guarantee damage identification accuracy. Once the model is trained, the structural damages can be precisely identified despite the noise-contaminated data. We validated the efficiency and accuracy of the proposed framework with a public database and compare its performance with several common metaheuristic algorithms. With the proven capability, the proposed framework shows a promising future as a handy and reliable AI-assisted digital tool for robust development in next-generation structural monitoring systems.

A new fast four-point rainflow cycle counting algorithm by employing a new loop iteration mode for fatigue life estimation

Purpose In order to improve the computation efficiency of the four-point rainflow algorithm, a new fast four-point rainflow cycle counting algorithm (FFRA) using a novel loop iteration mode is proposed.Design/methodology/approach In this new algorithm, the loop iteration mode is simplified by reducing the number of iterations, tests and deletions. The high efficiency of the new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems.Findings The extensive simulation results show that the extracted cycles by the new FFRA are the same as those by the four-point rainflow cycle counting algorithm (FRA) and the three-point rainflow cycle counting algorithm (TRA). Especially, the simulation results indicate that the computation efficiency of the FFRA has improved an average of 12.4 times compared to the FRA and an average of 8.9 times compared to the TRA. Moreover, the equivalence of cycle extraction results between the FFRA and the FRA is proved mathematically by utilizing some fundamental properties of the rainflow algorithm. Theoretical proof of the efficiency improvement of the FFRA in comparison to the FRA is also given.Originality/value This merit makes the FFRA preferable in online monitoring systems of structures where fatigue life estimation needs to be accomplished online based on massive measured data. It is noticeable that the high efficiency of the FFRA attributed to the simple loop iteration, which provides beneficial guidance to improve the efficiency of existing algorithms.

Reducing false damage detections in guided ultrasonic wave monitoring systems using a denoising autoencoder

ABSTRACT Guided ultrasonic wave (GUW) monitoring systems for pipeline structures are gaining much attention in critical sectors such as the petrochemical, nuclear and energy sectors. However, the effects of environmental and operational conditions (EOCs), especially temperature, may generate substantial false damage detections. The temperature effect may interfere with different coherent noise sources and generate unwanted peaks that are falsely identified as damage. In this paper, a denoising autoencoder (DAE) is proposed to reduce the frequency of false damage detections in GUW monitoring systems. A DAE decodes high dimensional data into low-dimensional features and reconstructs the original data from these low-dimensional features. By providing signals at a reference temperature with the fewest false damage detections, this structure forces the DAE to learn the essential features hidden within complex data. A database of GUW signals is formed based on the experimental measurements using a six-metre-long stainless steel Schedule 20 pipe. Variations in temperature and damage severity are applied to develop the database to mimic a simple step change in damage growth under EOCs. The outcomes obtained from this study show that the proposed methodology can reduce false damage detections during GUW monitoring and is valuable for pipeline safety evaluations.

Structural monitoring data repair based on a long short-term memory neural network.

As construction technology and project management develop, structural monitoring systems become increasingly important for ensuring large-span spatial structure safety during construction and operation. However, most of the sensors and monitoring equipment in monitoring systems are poorly serviced, resulting in frequent abnormal monitoring data, which directly leads to challenges in data analysis and structural safety assessment. In this paper, a structural response recovery method based on a long short-term memory (LSTM) neural network is proposed by studying the autocorrelation of data and the spatial correlations among data at multiple measurement points. The effectiveness and robustness of the proposed method are verified using the monitored stress data for a grid structure jacking construction process, and the influence of different data loss rates on the recovery accuracy is analysed. The recovery models are compared using a support vector machine and a Multi-Layer Perception (MLP) neural network. The proposed method can effectively restore missing data; notably, the MSE index is 0.6, and the MAPE is below 15%. The data restoration method based on the LSTM neural network is more accurate than the traditional method. Finally, the repair applicability of various types of monitored data is verified using the monitoring data from Hall F of Qingdao Jiao-dong International Airport under typhoon conditions.

Fiber Bragg Gratings Sensor Strain-Optic Behavior with Different Polymeric Coatings Subjected to Transverse Strain.

This research work is based on a previous study by the authors that characterized the behavior of FBG sensors with a polyimide coating in a structural monitoring system. Sensors applied to structural health monitoring are affected by the presence of simultaneous multidirectional strains. The previous study observed the influence of the transverse strain (εy) while keeping the longitudinal strain constant (εx), where the x direction is the direction of the optical fiber. The present study develops an experimental methodology consisting of a biaxial test plan on cruciform specimens with three embedded FBG sensors coated with polyimide, acrylate, and ORMOCER®. Applying the Strain-Optic Theory as a reference, a comparison of the experimental values obtained with the different coatings was studied. This experimental work made it possible to study the influence of the transverse strain (εy) on the longitudinal measurements of each FBGS and the influence of the coating material. Finally, the calibration procedure was defined as well as K (strain sensitivity factor) for each sensor.

Structural health monitoring of an onshore steel wind turbine

The study presents the development of a structural monitoring system installed in a 45-m-high steel wind tower located in Italy. The installed monitoring system was composed by 16 strain gauges placed in the tower wall, in a pattern of four Wheatstone bridges at 45°, together with thermal couples, at 21 m from the ground (half-height of the tower). Moreover, several accelerometers were placed along the tower height (with one of them located next to the strain gauges). The wind velocity and directions were obtained directly from the turbine own monitoring system. Such a monitoring system was designed because, due to the decrement of the total height from the original design, the tower suffers from resonance problems. In fact, the investigated tower was originally designed with 65 m of height but then, to comply with local regulations, the height was decreased to the actual size. Therefore, to allow safe operation and avoid excessive fatigue due to the increased displacements, the velocity of the rotor has been manually limited causing an important reduction in the energy production. The results of the study show the importance of monitoring the resonance issue. The differences between the damage indexes obtained with two different working conditions are discussed: tower working with limited operational capacity and tower working at its maximum capacity (in resonance).

An intelligent framework of upgraded CapsNets with massive transmissibility data for identifying damage in bridges

Structural monitoring systems installed on bridges are capable of capturing large-scale dynamic responses online and in real-time. The response data of the bridge under different loading conditions is used for condition assessment of the bridge. There is a chasm between monitoring data and damage assessment due to the difficulty in revealing the relationship between the massive monitoring data and damage. this study proposes an intelligent framework for identifying (localizing and quantifying) damage in a bridge from massive acceleration responses. The framework features (i) physical big-data quantity and (ii) an intelligent identification model. The former specifies a mechanical quantity termed damage pattern spectrum of power-spectrum-density transmissibility (PSDT), the PSDT-damage pattern spectrum for short, constructed to protrude damage information from massive acceleration responses; the latter specifies an upgraded Capsules Networks (CapsNets), Up-CapsNets for short, which intelligently locate and quantify damage via deep learning with inputted PSDT-damage pattern spectrum. The proposed intelligent framework is verified through a numerically truss bridge model and an experimental suspension bridge model. The results demonstrate that this intelligent framework can identify locations and severity of damage with greater accuracy, stronger immunity to noise, and higher generalization than existing convolutional neural networks (CNNs) and CapsNets. Such a peculiarity can be attributed to two points: (i) the sophisticated function of the Up-CapsNets in recognizing damage patterns over conventional deep learning networks; and (ii) the unique qualification of PSDT-damage pattern spectrum in refining damage information over raw acceleration responses. The proposed intelligent framework provides a viable approach for monitoring the condition of a bridge by combining deep-learning deduction and big-data dynamic responses.

RETRACTED: Subway structure health monitoring system based on internet of things

The present study advances the metro structure monitoring system, which has been hindered by technological constraints limiting the comprehensive and precise collection of vital data from shield tunnels. We introduce a metro tunnel health advisory system that leverages a compressed sensory reconstruction algorithm and wavelet transform to integrate and alert the 3D model of metro tunnels. Additionally, we propose a novel wavelet packet spectrum multi-threshold noise reduction method, based on the categorization of the wavelet packet spectrum and a threshold criterion for signal noise reduction. Our findings reveal that the threshold UL enables precise determination of the structure's damage state. Moreover, the BIM-based IoT metro health monitoring system has been effectively deployed in Wuhan Metro Line 3's monitoring project, proving the technique's capability to accurately trace the full progression of structural damage. This approach offers fresh insights for designing primary content, selecting transmission schemes, analyzing early warnings, and visualizing the health monitoring system of operational tunnels.

Thunderstorm impact on the built environment: A full-scale measurement and post-event damage survey case study

This paper investigates the impact of an intense thunderstorm on the urban area of Sânnicolau Mare, Romania, based on the meteorological data collected by full-scale field measurement and the damage data of low-rise buildings obtained by post-event survey. The meteorological data throughout the entire thunderstorm process were collected from two sources, namely, a wind and structural monitoring system of a 50 m-tall lattice tower and a state-operated meteorological station, both located at the outskirts of the city. The damage data were collected by post-event in-situ survey with crowdsourcing techniques, providing information of both wind- and hail-induced damage to over 200 low-rise buildings. Based on the meteorological and damage data, the damage mechanisms were revealed by conducting forensic engineering analysis for two representative buildings which respectively sustained severe wind- and hail-induced damage. Moreover, the widely used estimation scales for wind intensities, such as the Fujita scale, were validated by anemometric records and damage observations. Finally, the feasibility of utilizing the hail-induced damage to building facades as a new indicator for the estimation of wind speed and direction was discussed. This study aims to further understand the impact of thunderstorms on low-rise buildings, so as to deliver essential information for their storm-resistant designs.

Real-Time evaluation of bridge girder deflection under heavy vehicles

Modern Anomaly Detection techniques enable the detection of structure damage from data recorded by structural monitoring systems. One of the most significant quantities to be monitored is beam deflection, which, however, is difficult to measure directly. This research focuses on evaluating bridge girder deflection using laser sensors. The validation of the system is conducted on the lateral span of a prestressed precast concrete deck which is characterized by high stiffness and behaves displacements below the millimeter in operational conditions. Monitoring displacements in such a complex scenario is a challenging objective. The study demonstrates the effectiveness of measuring deflection with vertical sensors and, when necessary, with sub-vertical sensors in cases of obstacles. The results indicate a good correlation between the readings of the vertical and diagonal sensors, despite increased ambient noise in the diagonal measurements.

Recent Progress in Self-Powered Sensors for Structural and Human Monitoring Systems Using Thermoelectric Energy Harvesters

With the evolution of Internet of Things (IoT), it has become essential to build systems that enable real-time monitoring of structures, the environment, and the human body. Among them, various studies have identified self-powering of real-time monitoring systems as one of the most important technologies . Thermoelectric generators are reported to be one of the most suitable power sources for real-time monitoring systems because they have the advantage of converting thermal energy into electrical energy through temperature gradients without using additional power sources. In this review, we will discuss structural/environmental and human health monitoring systems utilizing thermoelectric generators (TEGs). First, we will introduce the type of thermoelectric generators. Then, we will provide an overview of the thermoelectric generators utilized in structural/environmental monitoring and biomonitoring systems. The latest applications of thermoelectric generator-based systems will also be introduced to showcase their potential in industrial applications.