An updating kernel density estimation method for guided wave-based quantitative damage diagnosis under vibration conditions.

Structural health monitoring technology has gradually developed from the research in laboratory to engineering validations and applications. However, the problem of reliable damage evaluation under time-varying conditions is a main obstacle for applying structural health monitoring to real aircraft structures. Among the existing structural health monitoring methods, the guided wave–based structural health monitoring method is popular but the time-varying problem needs to be addressed. Several methods have been proposed to deal with this problem but limitations remain. In this article, an adaptive guided wave-Gaussian mixture model–based damage monitoring method is proposed. It can be used online without any structural mechanical model and a priori knowledge of damage under time-varying conditions. With this method, a baseline guided wave-Gaussian mixture model is constructed first based on the guided wave features obtained under time-varying conditions when the structure is in healthy state. When a new guided wave feature is obtained during an online damage monitoring process, the guided wave-Gaussian mixture model is updated by an adaptive updating mechanism including dynamic learning and Gaussian components split-merge. The mixture probability structure of the guided wave-Gaussian mixture model and the number of Gaussian components can be optimized adaptively. Finally, a probability damage index is proposed to measure the degree of variation between the baseline guided wave-Gaussian mixture model and the online guided wave-Gaussian mixture model to reveal the damage-induced weak cumulative variation trend of the guided wave-Gaussian mixture model so as to increase the damage evaluation reliability. The method is validated in a full-scale aircraft fatigue test, and the results indicate that the reliable crack growth monitoring of the right landing gear spar and the left wing panel under the fatigue load condition is achieved.

Structural health monitoring (SHM) is essential for ensuring the durability and safety of steel bridges, which are considered as critical part of modern infrastructure. Ensuring the integrity of bridges is vital for maintaining their functionality and safety. Considerable research has been conducted on predicting damage location in multi-span steel bridges, while relatively limited attention has been given to the identification of damage in single-span steel bridges, and there exists a notable gap in the literature regarding the quantification of damage based on severity. This study presents a novel approach combining Modal Strain Energy-based damage indices with extensively trained data-driven Artificial Neural Networks (ANNs) to achieve highly accurate damage location and quantify this damage in terms of severity in single-span steel bridges. The research integrates a finite element (FE) model as the case study of the Alamosa Canyon Bridge in Sierra County, New Mexico. The validated FE model was used to compute damage indices based on the first two bending mode shapes. The damage index (DI) of each mode for different damage scenarios simulated on the FE model is then combined using the absolute value method to pinpoint damage locations. ANNs are employed to predict severity based on these damage indices, leveraging a comprehensive dataset generated through cubic spline interpolations and FE simulations. The DI was used as an input parameter for the ANNs to estimate damage. This methodology highlights the reduced computational effort using a streamlined method for structural health monitoring that maintains high accuracy in severity prediction and damage detection. This study enhances the infrastructure safety and maintenance strategies by offering a practical framework for steel bridges.

Bolt early looseness monitoring using modified vibro-acoustic modulation by time-reversal

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.

In order to solve the problem of abnormal structural health monitoring data of large-span bridges, the construction of civil engineering structural health monitoring platform based on intelligent algorithm is proposed. This paper utilizes the Internet of Things RFID technology to deploy health monitoring sensor nodes and process the bridge structural health monitoring data, so as to realize the large-span bridge structural health monitoring. The experimental results show that the structural health monitoring method designed in this paper for large-span bridges is effective, and the accuracy rate between the measured data values and the real values is 90%, which is highly reliable and has certain application value. Conclusion: The designed structural health monitoring method for large-span bridges has a high degree of agreement between the main girder vertical deformation variables and the actual main girder vertical deformation variables at different monitoring points, which proves that the adopted structural health monitoring method for large-span bridges has a good accuracy and has certain application value.

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.

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.

Accurate detection of time and location of damage in its early stages has motivated the researchers to develop several damage diagnosis methods in recent decades. With current improvements in monitoring hardware, many of the previously developed Structural Health Monitoring (SHM) methods encounter the BIG DATA problem in processing the data collected through dense sensor networks. Therefore, it is vital to improve the efficiency and scalability of today’s SHM procedures parallel to data acquisition enhancements. Toward this end, this paper presents a data-driven damage detection methodology based on compressed sensing techniques. The objective of the paper is accurate damage localization in a structural component instrumented with a dense sensor network, by processing data only from a subset of sensors. In this method, first a set of sensors from the network are randomly sampled. Measurements from these sampled sensors are processed to extract damage sensitive features. These features undergo statistical change point analysis to establish a new boundary for a local search of damage location. As the local search proceeds, probability of the damage location is estimated through a Bayesian procedure with a bivariate Gaussian likelihood model. The decision boundary and the posterior probability of the damage location are updated as new sensors are added to processing subset and more information about location of damage becomes available. This procedure is continued until enough evidence is collected to infer about damage location. Performance of this method is evaluated using a finite element model of a cracked gusset plate connection. Pre- and post-damage strain distributions in the plate are used for damage diagnosis. doi: 10.12783/SHM2015/279

This paper presents an experimental study on the detection and localization of damage in a steel plate using vibration-based methods. Dynamic impulse tests were conducted on both undamaged and damaged plate to determine changes in modal parameters, such as eigenfrequencies and mode shapes. Several damage detection methods, including the Modal Assurance Criterion (MAC), Coordinate Modal Assurance Criterion (COMAC), Mode Shape Curvature (MSC), Mode Shape Curvature Square (MSCS), and Damage Index (DI), were applied. The results showed that while MAC and COMAC effectively detected damage, they did not provide information about the location. MSC and MSCS indices demonstrated improved damage localization, with the MSCS being particularly effective. The DI index provided the clearest and most accurate representation of the damage location. These findings highlight the potential of these methods for non-destructive testing and structural health monitoring.

Damage localization for composite structure using guided wave signals with Gramian angular field image coding and convolutional neural networks

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.

Earthquake-prone regions like the circum-Pacific belt necessitate proactive measures to mitigate seismic risks. This study explores the synergy between Seismic Vulnerability Assessment (SVA) and Structural Health Monitoring (SHM) for enhanced infrastructure resilience. The researchers integrate machine learning algorithms with diverse sensors, including seismometers and accelerometers, to analyze vibration data for non-destructive testing. This multifaceted approach, encompassing ground-penetrating radar for subsurface analysis and acoustic emission/ultrasonic testing for internal assessment, refines SHM precision for pre-emptive vulnerability identification in various materials and configurations. By analyzing existing SVA studies focused on SHM, the authors ad-dress current gaps in monitoring practices, aiming to create more robust, scalable, and adaptable solutions for diverse structures and environments. The research dissects various SHM methods, analyzes their parameters and prototypes, and examines challenges in extant structure assessments. The authors propose com-prehensive strategies and solutions to bridge research gaps, leveraging non-destructive methods like the rebound hammer test and ferro scanning alongside computer-aided structural design software and strategic retrofitting. The authors aim to advance SHM for effective and interdisciplinary seismic risk mitigation strategies through collaborative efforts across academia, industry, and government sectors.

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.

In this study, the post-embedded piezoceramic transducers are used to perform structural health monitoring (SHM) and detect crack damage on the reinforced concrete (RC) column. The piezoceramic transducers are utilized as actuators and sensors, respectively. To investigate the use of the piezoceramic transducer in detecting crack damage in RC columns, SHM tests are performed on three full-size RC column specimens with various failure modes under cyclic loading. The sensors are installed at two depth ranges (40–50 and 70–80 mm) beneath the surface of the column to investigate the effect of the sensor depth on the damage index. In this study, the energy and amplitude-based damage indexes are developed. Both approaches follow the same trend under various drift ratios of the specimen. However, the value of energy-based damage index is much higher than the amplitude index for all peak drift ratio of the specimen. From different depth range of piezoceramic transducers, this study recommends a depth of 80 mm as optimal for post-embedded piezoceramic transducers, since it receive more stable signals in various frequencies. In the proposed health monitoring approach using piezoceramic transducers, the equation to predict the maximum residual crack width and damage index smart aggregates are developed. The normalized damage index equation which in this study is referred as ‘crack damage index’ is introduced and used to calculate the limiting value for each damage level. The suggested value of the crack damage index for each damage level is also provided. The energy, strength and stiffness reduction factors for each damage level, which correlate with the crack damage index, are also presented. These results demonstrate that the proposed post-embedded piezoceramic transducers has the potential to be used as a tool for SHM on an RC column.