Application of Numerical Methods in Structural Health Monitoring Using IoT Sensors

A model is generally not needed for the basic damage identification problem when using the electromechanical impedance-based method of structural health monitoring (SHM). However, modeling becomes necessary when more information is needed for more complex functions of the SHM system, such as estimation of remaining life. In addition, suitable models would aid in more accurately identifying and locating damage and in designing the SHM system. Since impedance-based SHM relies on high frequency excitation of the structure using piezoelectric patches, finite element modeling may not be computationally efficient. In this study, the spectral element method (SEM) is used in combination with electric circuit analysis for impedance modeling. SEM more accurately models higher frequency vibrations than finite element methods since the mass is modeled exactly and it incorporates higher order models more easily. Simulations of sensor multiplexing, high frequency response, and the inclusion of damage are presented. Experimental verification is also included.

This paper presents an overview of current research and development in the field of sensor placement methods in structural health monitoring. Due to the limitations of equipment facilities and cost, the number of sensors to be installed in a structure is relatively few. In many cases, three are always degrees of freedoms in a structure are not easily accessible, or eventually inaccessible. Moreover, actual structures are all continuous and have essentially infinite degrees of freedoms, therefore, it is impossible to instrument on all the degrees of freedoms. The methods of sensor placement are mainly dealing with accelerometers for global dynamic testing, or structural health monitoring. Three major concerns are discussed, the necessary number of sensors to be installed, where to deploy these sensors, and how to evaluate the effectiveness of these deployed sensors. Finally, a new loading dependent sensor placement method is proposed, and verified by the experimental data of a simple cantilever beam structure.

Complex, high‐rate dynamic structures, such as hypersonic air vehicles, space structures, and weapon systems, require structural health monitoring (SHM) methods that can detect and characterize damage or a change in the system’s configuration on the order of microseconds. While high‐rate SHM methods are an area of current research, there are no benchmark experiments for validating these algorithms. This paper outlines the design of an experimental test bed with user‐selectable parameters that can change rapidly during the system’s response to external forces. The test bed consists of a cantilever beam with electronically detachable added masses and roller constrains that move along the beam. Both controllable system changes can simulate system damage. Experimental results from the test bed are shown in both fixed and changing configurations. A sliding mode observer with a recursive least squares parameter estimator is demonstrated that can track the system’s states and changes in its first natural frequency.

Assessing the Evolution of Structural Health Monitoring through Smart Sensor Integration

A transformative revolution has been triggered by the inclusion of Internet of Things (IoT) sensors in healthcare’s real-time tracking capabilities. Recent advancements in miniaturization and low-power designs, wireless connectivity, and sensor fusion (integration of information from multiple sensors) continue to have a profound impact on the monitoring of health. Intelligent embeddability and superior sensing capabilities facilitate seamless patient data tracking, and strong security protects data against breaches. The role of IoT sensors in healthcare is to connect devices, such as biometric and temperature sensors or imaging and environmental sensors. IoT sensors are used in multiple applications such as fitness and vital signs tracking information, and patient care in smart healthcare facilities. Such technologies enable IoT-based medication adherence systems so that patients follow prescribed treatments, thus improving outcomes and reducing the possibility of hospital readmissions. Compared to regular monitoring devices, IoT sensors provide continuous data capture, increased accuracy, improved patient mobility and personalized healthcare solutions. Healthcare IoT adoption faces challenges in data privacy, regulation, and the complexity of technical integration of healthcare IoT in existing infrastructures. Ethical considerations must also be addressed to ensure equitable access and unbiased use of IoT technology. However, despite these obstacles, the future of IoT-based healthcare technologies remains an attractive proposition with continuous technology evolution likely to improve the quality of care, optimal resource allocation, and decision-making in real-time. This chapter explores the evolution of healthcare monitoring through IoT sensors, detailing their technological mechanisms, applications, benefits, and the potential for transforming the healthcare landscape.

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

During recent years structural health monitoring (SHM) systems have gained growing attention. Widely applied methods in SHM are methods based on the ultrasonic Lamb waves (LWs) which are also known as plate waves. LWs may be generated and sensed using different types of transducers and their selection is essential for the SHM systems performance. Among transducers used for generation and sensing of such waves, Interdigital Transducers (IDTs) are ones of the most promising types. Their main advantages are: mode selectivity, high excitation strength, wave directivity, small sizes and relatively low cost. In most cases IDTs are made of piezoceramics or piezoelectric polymers what makes them stiff and fragile. Contrary to the piezoceramic transducers, the ones based on piezocomposites (i.e.: macro-fiber composite) are flexible with the comparable efficiency of piezoelectric effects. The MFC transducers are usually optimized as actuators and they are not designed to any specific frequency. It appears, however, that sparse comb-like electrodes used in interdigital transducers create very interesting properties of mode selectivity. In the paper, after the introduction and short discussion about advantages and disadvantages of the IDTs, a novel type of the interdigital transducer, based on elastic macro-fiber composite, is presented. Next, the results of the numerical and experimental tests of the MFC based IDT designed for the A0 mode excitation in a 4mm-thick aluminium plate is presented. In the final part of the paper advantages and disadvantages of the investigated transducer are discussed.

Comparing model-based adaptive LMS filters and a model-free hysteresis loop analysis method for structural health monitoring

In composite structures, damages are often invisible from the surface and can grow to reach a critical size, potentially causing catastrophic failure of the entire structure. Thus safe operation of these structures requires careful monitoring of the initiation and growth of such defects. Ultrasonic methods using guided waves offer a reliable and cost-effective method for structural health monitoring in advanced structures. Guided waves allow for long monitoring ranges and are very sensitive to defects within their propagation path. In this work, the relevant properties of guided Lamb waves for damage detection in composite structures are investigated. An efficient numerical approach is used to determine their dispersion characteristics, and these results are compared to those from laboratory experiments. The experiments are based on a pitch-catch method, in which a pair of movable transducers is placed on one surface of the structure to induce and detect guided Lamb waves. The specific cases considered include an aluminum plate and an aluminum honeycomb sandwich panel with woven composite face sheets. In addition, a disbond of the interface between one of the face sheets and the honeycomb core of the sandwich panel is also considered, and the dispersion characteristics of the two resultant waveguides are determined. Good agreement between numerical and experimental dispersion results is found, and suggestions on the applicability of the pitch-catch system for structural health monitoring are made.

Life cycle monitoring of civil infrastructure such as bridges and buildings is critical to the long-term operational cost and safety of aging structures. The widespread use of Structural Health Monitoring (SHM) systems is limited due to unavailability of specialized data acquisition equipment, high cost of generic equipment, and absence of fully automatic decision support systems. The goals of the presented project include: first, design of a Wireless Intelligent Sensor and Actuator Network (WISAN) and creation of an inexpensive set of instrumentation for the tasks of structural health monitoring; second, development of a SHM method, which is suitable for autonomous structural health monitoring. The design of the wireless sensor network is aimed at applications of structural health monitoring, addressing the issues of achieving a low cost per sensor, higher reliability, sources of energy for the network nodes, energy-efficient distribution of the computational load, security and coexistence in the ISM radio bands. The practical applicability of the sensor network is increased through utilization of computational intelligence and support of signal generation capabilities. The automated SHM method is based on the method of modal strain energy, though other SHM methods will be supported as well. The automation tasks include automation of the modal identification through ambient vibrations, classification of the acquired mode shapes, and automatic evaluation of the structural health.

Structural Health Monitoring (SHM) seeks to evaluate the condition of a structure by instrumenting the structure with sensors and then using a combination of structural and data analysis. One unifying factor across all structures is the effect of temperature changes on the behavior of the structure. Temperature Driven - Structural Health Monitoring (TD-SHM) considers temperature as the driving force in structural behavior, and consequently the main input in SHM. Temperature as an input is related to strain and displacement as output to form three-dimensional temperature signatures for the structures. These signatures are descriptive of the thermal behavior, as well as sensitive to changes in structural condition. The two main benefits of TDSHM are the applicability to any structure that undergoes daily or seasonal temperature changes, and the possibility to measure temperature as an input for a complete input-output model. This work examines one type of three-dimensional signature: the relationship between changes in temperature with changes in strain and rotation or tilt on a beam-like concrete structure. The three-dimensional figure is fit with a surface to quantify the behavior, and changes in either the goodness of fit or in the fit itself are indicative of changes in structural behavior. These methods are validated using data from a real structure; Streicker Bridge on campus at Princeton University.

Blasts can produce, in a very short time, an overload much greater than the design load of a building. The blast explosion nearby or within structures causes catastrophic damage to the building both externally and internally. This study intends to model a Cold-Formed Steel (CFS) building using Finite Element Method (FEM) in which material properties of the model are defined according to results of performed laboratory tests. Then accelerograph record of a standard blast was applied to the Finite Element (FE) model. Furthermore, various Optimal Sensor Placement (OSP) algorithms were used and Genetic Algorithm (GA) was selected to act as the solution of the optimization formulation in selection of the best sensor placement according to the blast loading response of the system. In this research a novel numerical algorithm was proposed for OSP procedure which utilizes the exact value of the structural response under blast excitation. Results show that with a proper OSP method for Structural Health Monitoring (SHM) can detect the weak points of CFS structures in different parts efficiently.

In civil engineering, health monitoring of structure have been treated as an important subject. Health monitoring by using experimental measurements such as strain, acceleration, velocity, displacement, rotation and other parameters has been used for detecting changes, which may indicate damage or degradation. In recent, the applications of health monitoring of large structure have been facilitated by the developments in measurement, communications and computational technology gradually. Especially, large civil structures as bridges are damaged by fatigue loads or unexpected load such as earthquakes and finally reach to failure. Thus, a structural health monitoring and damage detection methods have been developed and attracted a great deal of public attention in recent decades. Actually many research efforts have been conducted to develop a health monitoring and damage detection techniques of existing civil structures. However, these methods are needed to improve to apply to a real civil structure. The purpose of this study is to develop a practical instrument to identify dynamic parameters of real civil structural systems.

Total Knee Arthroplasty (TKA) continues to be a common and important orthopedic procedure for many in the United States. Despite recent medical advancements and increasing knowledge in the orthopedic community, it has been determined that 20% of TKA patients are still dissatisfied with their knee replacements. Causes of this failure include septic loosening and wear on the bearing component of the implant. Another cause of failure that has received specific attention from the mechanical community is aseptic loosening, which has been attributed to unbalanced ligaments or misalignment of the implant components. Previous efforts have been made to detect loosening by using passive force sensors such as piezoelectric transducers or strain gauges to detect misalignment. An alternative to this is to perform active sensing or structural health monitoring to evaluate possible loosening before it becomes a critical concern to the patient. One method of structural health monitoring, called the electromechanical impedance (EMI) method, is particularly attractive as it can use a single, compact piezoelectric transducer to determine the state of the host structure. This work is intended to evaluate the ability of the EMI method in sensing loosening between the cement and bone of a TKA tibial tray. This work will utilize real tibial trays implanted into synthetic bone (Sawbone) to evaluate the feasibility of detecting loosening using the EMI method. The intention of this work is to serve as a foundation for further in-vivo and intraoperative studies.

Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation