Smart Structural Health Monitoring Research Articles

A hierarchical multistage holistic model for acoustic emission source monitoring in composites

Abstract This paper introduces a multistage smart structural health monitoring (SHM) model for carbon-fiber composites, with a focus on multiple types of acoustic emission (AE) source localization and classification. The SHM model uses time-frequency data from various AE events (such as tool drops, impact, and artificial debonding) across different zones of a composite structure. The SHM strategy demonstrates a robust smart monitoring of composites with high accuracy. Further, a hypothesis testing has been carried out that supports the superiority of a 2-stage identification process, revealing statistically significant higher accuracy and confidence intervals across all zones and AE source types. This research establishes a novel framework for solving a hierarchical multistage holistic damage source identification problem, offering robustness in identifying various damage scenarios and quantifying associated prediction uncertainties.

Smart structural health monitoring using computer vision and edge computing

Structural health monitoring (SHM) provides real-time data on the condition and performance of infrastructure, enabling timely and cost-effective maintenance interventions, and hence enhanced safety and extended service life. The computer vision-based non-contact sensor has emerged as a promising alternative to conventional contact-type sensors for structural displacement measurement and SHM. Many of the currently reported vision-based structural displacement measurement systems typically temporarily set up a video camera from a distance to the structure. The collected images or videos are usually stored locally and post-processed offline to obtain structural displacement responses, which is cumbersome and limited to short-term SHM applications. The recent development of technologies empowered by the Internet of Things (IoT) and edge computing has enabled real-time video processing and analysis at the source, minimizing latency, reducing bandwidth requirements, and enabling prompt decision-making, thereby enhancing efficiency and responsiveness compared to traditional offline video recording and processing systems. In this paper, an edge computing vision-based displacement measurement system (EdgeCVDMS) is developed. Video recording, processing, and displacement response identification are entirely performed on an edge device integrated with the vision-based displacement tracking algorithm, thereby greatly reducing the amount of data transmitted to the cloud server. The feasibility and applicability of the developed sensing system are experimentally validated on a laboratory-scaled transmission tower structure. The proposed EdgeCVDMS is cost-effective, easily deployable, and of great potential to be applied for the condition assessment of a larger population of aging civil infrastructure.

Assessing structural homogeneity and heterogeneity in offshore wind farms: A population-based structural health monitoring approach

This paper presents an in-depth analysis of population-based structural health monitoring applied to offshore wind farms, with a specific focus on a real wind farm. The study quantifies the heterogeneity within the wind turbine population utilising advanced numerical methods. The Fréchet distance is introduced as a key metric to assess the similarity in terms of structural integrity among the turbines considering factors, such as geometry, material properties, topology, and operational conditions. The results revealed a predominant homogeneity with respect across the wind turbines, despite variations in environmental exposure and operational states. Furthermore, the scope of the study is extended to explore the implications of environmental influences, such as corrosion, scour, and marine growth, which are typically unmonitored yet crucial for comprehensive structural assessment. The present paper contributes to the field by demonstrating the feasibility of knowledge transfer in homogeneous wind turbine populations and highlighting the need for detailed similarity assessments in heterogeneous settings. The findings of this study pave the way for better-targeted, smart, and efficient structural health monitoring strategies in renewable energy systems.

Enabling electrical response through piezoelectric particle integration in AA2017-T451 aluminium parts using FSP technology

In the field of structural engineering, the integration of smart materials and structural health monitoring (SHM) has given rise to self-sensing materials (SSM), leading to a paradigm shift in SHM. This paper focuses on the interplay between self-sensing capabilities and the piezoelectric properties of lead zirconate titanate (PZT) and barium titanate (BT) in aluminium components. Leveraging Friction Stir Processing (FSP), the study explores the synthesis and performance of SSMs with embedded piezoelectric particles, potentially transforming structural engineering. The paper highlights FSP as a key methodology for incorporating piezoelectric particles into structural materials, showcasing its potential in developing SSMs with enhanced functionalities. A specific focus is placed on integrating PZT and BT particles into AA2017-T451 aluminium parts using FSP, with metallographic assessments and mechanical property evaluations conducted to analyse particle distribution and concentration. This study shows how BT and PZT particles are incorporated into AA2017-T451 aluminium to create a SSM that responds to external stimuli. Under cyclic loading, the SSMs exhibit a linear load-electrical response correlation, with sensibility increasing at lower frequencies. Metallographic analysis shows homogeneous particle distribution, while PZT induces increased brittleness and brittle fractures. Yield strength remains relatively stable, but ultimate strength decreases post-FSP. Hardness variations indicate weaker bonding with PZT particles. Eddy’scurrent testing aligns with hardness profiles, and sensorial characterization reveals a non-linear frequency-sensibility relationship, showcasing the SSMs’ suitability for low-frequency applications, particularly with PZT embedment.

A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring.

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.

Development of robotic sprayable self-sensing cementitious material for smart structural health monitoring

Self-sensing cementitious materials are critical to smart structural health monitoring with the piezoresistivity as an indicator for internal stress and cracks in the structure, which have been frequently utilized as functional coatings in engineering practices. To facilitate the automatic deployment of self-sensing cementitious materials, the compatibility with robotic spraying has been systematically investigated in this study. Pre- and post-spraying evaluations have been conducted for the mixtures with carbon fibers at different dosages, including rheology, tackiness, compressive strengths, and electromechanical responses. On this basis, the addition of 0.4 vol.% carbon fiber was confirmed as the optimal dosage with both competent spraying performance and self-sensing function in the corresponding mixture. Furthermore, the porosity characteristics and fiber agglomeration of the designed mixtures were quantitatively assessed by micro computed tomography (micro-CT), which suggested their relationship to the mechanical and self-sensing properties. Apart from the structural application potential of the developed self-sensing mixture, this study also provides detailed guidance for the optimization of other value-added robotic sprayable cementitious materials.

Identification of Damage in a Wind Turbine Blade Using Mechanical Measurements and Artificial Neural Networks

Due to the stochastic nature of environmental loadings, a lot of interest is paid in the discovery of possible damages of the involved equipment in modern industry. In wind turbines’ blades, the development of a smart structural health monitoring system is essential. In this paper, a large-scale composite wind turbine blade model is designed and used for the detection of several damage scenarios. The process is mainly based on the development of monitoring techniques which exploit the capabilities of artificial neural networks. These techniques can provide the exact position of possible damages, under given external loading scenarios. Moreover, the use of such methods decreases significantly the need of external intervention and at the same time it increases the accuracy of the whole approach. The above processes are simulated using the finite element method. The goal is to develop a neural network which realizes the correlation of measurements with damage patterns. The goal is focused on the solution of inverse problems involving elastically deformable structures, based on remote mechanical measurements. The correlation between measurements and damages, which is much more complicated in comparison to image analysis, is studied by means of neural networks.

Real-time monitoring of self-sensing cementitious composite incorporating hybrid silicon carbide and graphite for enhanced structural health monitoring

This manuscript presents a comprehensive exploration of Silicon Carbide-Graphite Cement Composites (SCGCCs) by investigating their mechanical, electrical, and self-sensing properties. The study systematically varies SiC and graphite content, leading to remarkable insights. In terms of mechanical strength, specimens S50G1 and S50 stand out, exhibiting impressive enhancements of 15.6 % and 9.8 % in compressive strength, respectively, showcasing the pivotal role of particle content in bolstering structural integrity. Electrical resistivity analysis uncovers a percolation threshold at 25 % SiC and 2 % graphite, resulting in a substantial 97 % reduction in resistivity. This critical finding underscores the delicate balance required for optimal conductive networks within the composite material. The self-sensing capabilities of SCGCCs under flexural stress reveal a synergistic effect between SiC and graphite, with specimen S25G2 displaying the lowest voltage drop. The compressive stress analysis unveils a distinctive conductive path reconstruction behavior, offering valuable insights into the material's response to varying stress levels. Advanced CT scan porosity analysis further refines our understanding, indicating a significant reduction in porosity from 4.2 % to 3.1 % in specimen S25. Furthermore, specimens with lower graphite and SiC proportions exhibit efficient void compaction, resulting in a well-integrated microstructure with porosity as low as 2.5 %. These findings not only contribute to the fundamental understanding of SCGCCs but also offer practical applications in smart materials and structural health monitoring. Moreover, the inherent advantage of real-time monitoring within the context of smart mortar extends to the prognostication of an advanced warning signal for impeding the complete failure of the smart concrete matrix.

Automation of structural health monitoring (SHM) system of a bridge using BIMification approach and BIM-based finite element model development

This research focuses on the automation of an existing structural health monitoring system of a bridge using the BIMification approach. This process starts with the Finite Element Analysis (FEA) of an existing bridge for the numerical calculations of static and dynamic parameters. The validation of the FE model and existing SHM system was carried out by the field load testing (Static and dynamic) of the bridge. Further, this study tries to fill the research gap in the area of automatic FE model generation by using a novel methodology that can generate a BIM-based FE model using Visual Programming Language (VPL) scripts. This script can be exported to any FE software to develop the geometry of the FE model. Moreover, the SHM devices are deployed to the Building Information modelling (BIM) model of the bridge to generate the BIM-based sensory model (as per the existing SHM system). In this way, the BIM model is used to manage and monitor the SHM system and control its sensory elements. These sensors are then linked with the self-generated (Internet of Things) IoT platform (coded in Arduino), developing a smart SHM system of the bridge. Resultantly, the system features visualisation and remote accessibility to bridge health monitoring data.

An adaptive framework applied to structural health monitoring and damage-tolerant active control of smart structures

An adaptive framework applied to structural health monitoring and damage-tolerant active control of smart structures

Routing in LoRa for smart cities: A gossip study

Wireless communications for IoT in smart cities may be affected by Non-Line-of-Sight (NLOS) propagation conditions due to obstacles such as buildings, trees, and others. LoRaWAN technology transmits information through several kilometers in one hop, from a source node to a gateway. However, packet reception decreases when the link has NLOS conditions and there are no alternative routes for packet delivery. This paper presents a LoRa implementation of MACGSP6, a Gossip-based routing algorithm, to improve communications in a LoRaWAN network where all nodes have NLOS conditions. The paper compares the packet delivery ratio of LoRaWAN and MACGSP6. Results show that packet delivery to the gateway increased from 0% with LoRaWAN to a minimum of 53% with the routing algorithm. Additionally, most packets were delivered using the shortest path available, even when the algorithm does not explicitly compute the routes, decreasing the delay associated with this process. Thus, the proposed solution is useful for IoT applications in smart cities, such as smart metering and structural health monitoring, where one relay node can forward the information of many other nodes.

Self-sensing properties of 3D printed continuous carbon fiber-reinforced PLA/TPU honeycomb structures during cyclic compression

Polylactic acid/thermoplastic polyurethanes (PLA/TPU) filament for 3D printing was prepared by screw extruder, and continuous carbon fiber-reinforced PLA/TPU honeycomb structure was fabricated by continuous fiber 3D printer, and the self-sensing performance of the structure during cyclic compression was investigated. The results show that the strain and damage can be sensed by the change of electrical resistance in the honeycomb structure during cyclic compression at small-strain (ε = 0.04) and large-strain (ε = 0.4). Meanwhile, the temperature monitoring of the honeycomb structure can be realized by the change of electrical resistance in the honeycomb structure. This study provides the possibility for developing and applying 3D printed continuous carbon fiber composite smart structures and structural health monitoring.

Fiber Bragg Grating Smart Material and Structural Health Monitoring System Based on Digital Twin Drive

The damage self‐diagnosis function puts forward higher requirements for the research and development of intelligent structural health monitoring, and in most cases, it is necessary to monitor the load first, especially the monitoring of the impact load. The development of smart materials and structures is based on advanced sensing systems. In order to achieve this purpose, a high‐speed demodulation system based on fiber grating with double long period grating is studied, and then, a damage self‐diagnosis system based on fiber grating is constructed. The system can realize the strain distribution and impact load monitoring of the structure. After quantitative analysis of the signal, an advanced information identification method is used to realize the impact load location. This paper focuses on the data preprocessing process of bridge health monitoring. In view of the characteristics of high data complexity, large amount of data, and many noise components in the process of structural monitoring, this paper adopts basic data cleaning for the original data set, including data dimensionless and missing value processing. Based on the digital twin technology, the composition of the digital twin KNN model of bridge swivel construction monitoring and management is analyzed, and the digital twin system architecture of bridge swivel construction monitoring and management is built. The function display of the monitoring platform, including setting a variety of permission login modes, displaying BIM model, geographic information, and weather environment; monitoring data entry and addition, deletion, and modification; data chart analysis and export; and email warning, to verify the feasibility of the application of digital twin technology in bridge monitoring, and the advantages of the intelligent monitoring system are obtained. The strain error is found to be less than 15.48 μℇ in the research, which is within the range of the fiber grating. This method can effectively monitor and forecast these distributed, nonlinear, strongly coupled, multivariable, and time‐varying complex structures. By monitoring bridges, the original monitoring data of bridges can be obtained, and scientific research data and analysis services can be provided. In particular, the damage caused by shock and vibration is monitored, so that the accumulation of damage can be detected before it threatens the safety of the structure, so that the damaged structure can be repaired in time to ensure the safe operation of the structure.

Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.

Fiber Optic Sensors Embedded in Textile-Reinforced Concrete for Smart Structural Health Monitoring: A Review.

The last decade has seen rapid developments in the areas of carbon fiber technology, additive manufacturing technology, sensor engineering, i.e., wearables, and new structural reinforcement techniques. These developments, although from different areas, have collectively paved way for concrete structures with non-corrosive reinforcement and in-built sensors. Therefore, the purpose of this effort is to bridge the gap between civil engineering and sensor engineering communities through an overview on the up-to-date technological advances in both sectors, with a special focus on textile reinforced concrete embedded with fiber optic sensors. The introduction section highlights the importance of reducing the carbon footprint resulting from the building industry and how this could be effectively achieved by the use of state-of-the-art reinforcement techniques. Added to these benefits would be the implementations on infrastructure monitoring for the safe operation of structures through their entire lifespan by utilizing sensors, specifically, fiber optic sensors. The paper presents an extensive description on fiber optic sensor engineering that enables the incorporation of sensors into the reinforcement mechanism of a structure at its manufacturing stage, enabling effective monitoring and a wider range of capabilities when compared to conventional means of structural health monitoring. In future, these developments, when combined with artificial intelligence concepts, will lead to distributed sensor networks for smart monitoring applications, particularly enabling such distributed networks to be implemented/embedded at their manufacturing stage.

Multifunctional cementitious composites with integrated self-sensing and hydrophobic capacities toward smart structural health monitoring

In this study, multifunctional cementitious composites with integrated self-sensing and hydrophobicity capacities were developed and investigated using conductive graphene nanoplate (GNP) and silicone hydrophobic powder (SHP). The mechanical properties, permeability, water contact angle, microstructure and piezoresistivity were studied and compared under different contents of GNP and SHP. The highest compressive and flexural strengths with 1% SHP and 2% GNP reached 62.6 MPa and 8.9 MPa, respectively. The water absorption significantly was decreased with the content of SHP, but was minorly affected by GNP. The water contact angle firstly increased but then decreased with the dosages of GNP and SHP. SHP and GNP could reduce the microscale pores and enhance the density of microstructures. The piezoresistivity under compression firstly exhibited low gauge factor, but then gradually increased to a constant value under high-stress magnitude. Moreover, compared to the conventional cement-based sensors, this piezoresistive cementitious composites containing SHP and GNP as novel cement-based sensors are less sensitive to water content and humidity. The outcomes can provide an insight into promoting the application of multifunctional cement-based sensors toward structural health monitoring under various ambient conditions.

Accurate self‐damage detection by electrically conductive epoxy/graphene nanocomposite film

AbstractDetecting and locating accurately structure damages at an early stage is essential to minimization of catastrophic disasters, prevention of fatalities and provision of cost‐effective maintenance. We herein report a facile approach to detect structure damages and to accurately identify their locations by using an electrically conductive epoxy/graphene nanocomposite film. A percolation threshold of electrical conductivity was observed at 0.58 vol% of graphene platelets (GnPs, ~3 nm in thickness and ~15 μm in length); electrical conductivity of 3.3 S/cm was obtained at 9.00 vol% of GnPs. The epoxy/GnP composite film containing 9.00 vol% of GnPs was employed as an array of electrically conductive paths in horizontal and vertical directions to detect and locate structure's damages. Thermal stability and temperature coefficient of the composite film were studied. Relative resistance change due to temperature effect was fitted into an exponential function, which showed strong correlation with the temperature change. This implies that an algorithm can be developed to compensate drift errors in resistance measurement due to temperature variation. With the help of Internet of Things, our self‐sensing epoxy/graphene nanocomposite films have great potential for smart aerospace structural health monitoring.

Smart structural health monitoring in civil engineering: A survey

Structural health monitoring (SHM) technologies for tracking and assessing socially and historically constructed long-haul spans was being increasingly recognized, and several government officials have realized the importance of the methodology. The article reviews recent advance of the SHM technologies in order to long-span bridges. A SHM framework is implemented to deploy. The research design and quality evaluation involving modal recognition techniques, image processing approaches, and hazard detection systems were then checked and presented. This research incorporates a developing capacity tool in combination with a low- and mid-index demonstrable evaluation tool for bridge architectural condition monitoring. The strength of concrete, stiffness of the steel beam surface, density of the top surface of the hollow plate, crack propagation, cover hardness of metal bars etc.From the examples, the open issues and recent developments for a SHM system have been addressed.

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Smart structural health monitoring (SHM) for large-scale infrastructure is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1-DCNN-LSTM, featuring two algorithms—convolutional neural network (CNN) and long-short term memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1-DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic data sets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful 2-D CNN, but with a lower time and memory complexity, making it suitable for real-time SHM. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article aims to develop a practical data-driven method for automatically monitoring the operational state of structures. In order to achieve consistently and highly accurate results in performing different tasks for diverse structures, we combine underlying features in both time and frequency domains extracted from measured signal vibration data. Three popular data featuring methods are combined to achieve the diversity gain which would not be possible with each individual method. As the vibration is usually measured by long time-series signals, the most efficient deep learning architecture for time-series signal, namely long-short term memory (LSTM), is considered for this work. Besides, each structure has its own dynamic properties, i.e., eigenfrequencies, around which the most relevant information is in the frequency domain, thus convolutional neural network specifically designed for capturing local information is used in combination with LSTM, forming a hybrid deep learning architecture. The applicability and effectiveness of the proposed approach are supported by three case studies with different types of structures, showing highly accurate damage detection with reduced resource requirements. These advantages can be valuable for developing a model for live monitoring of structural health in the future life-line infrastructures.

Discerning Localized Thermal Heating from Mechanical Strain Using an Embedded Distributed Optical Fiber Sensor Network.

Prior research has demonstrated that distributed optical fiber sensors (DOFS) based on Rayleigh scattering can be embedded in carbon fiber/epoxy composite structures to rapidly detect temperature changes approaching 1000 °C, such as would be experienced during a high energy laser strike. However, composite structures often experience mechanical strains that are also detected during DOFS interrogation. Hence, the combined temperature and strain response in the composite can interfere with rapid detection and measurement of a localized thermal impulse. In this research, initial testing has demonstrated the simultaneous response of the DOFS to both temperature and strain. An embedded DOFS network was designed and used to isolate and measure a localized thermal response of a carbon fiber/epoxy composite to a low energy laser strike under cyclic bending strain. The sensor interrogation scheme uses a simple signal processing technique to enhance the thermal response, while mitigating the strain response due to bending. While our ultimate goal is rapid detection of directed energy on the surface of the composite, the technique could be generalized to structural health monitoring of temperature sensitive components or smart structures.