A bio-inspired asynchronous skin system for crack detection 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.

Fundamental shear horizontal (SH0) waves in plate-like structures are of great importance in structural health monitoring (SHM) applications due to its unique nondispersive nature. The generation and the reception of the SH0 waves using piezoelectrics, however, is always a challenge. In this study, a dynamic model on the shear horizontal (SH) wave generation is proposed based on the continuum mechanics theory, in which the full dynamic coupling between a PZT actuator and a plate is considered. The inevitable unideal bonding between the actuator and the plate is taken into account through a spring model. The solutions, which can simultaneously satisfy the governing dynamic equations and the boundary conditions, are obtained in a closed form using trigonometric series and modal superposition method. The convergence of the series is demonstrated and the model is numerically validated through comparisons with FEM results using a PZT-5H wafer bonded on the surface of a thin aluminum plate. The proposed model is general, suitable for both ideal and unideal bonding, and expected to provide a useful tool for analyzing and simulating SH0 wave generations in SHM applications.

Polymer optical fiber (POF) sensors have the unique ability to measure high strain distributed along the fiber up to 40% using the optical time-domain reflectometry (OTDR) technique. Both, standard PMMA fibers and perfluorinated (PF) graded-index (GI) POF are introduced and evaluated in potential use and applicability. Further, distributed length change measurement based on cross-correlation analysis of the characteristic fiber signature of PF POF is introduced. We conclude the advances in distributed POF strain sensors technology with respect to application in structural health monitoring. Special focus is on the sensor integration into technical textiles for health monitoring of geotechnical structures and masonry structures. Measurement results of sensor-equipped textiles in different model tests are presented, displacement of soil and cracks in retrofitted masonry structures are detected and evaluated.

Structural health monitoring plays an increasingly significant role in detecting damages for large and complex structures to ensure their serviceability and sustainability. Optimal sensor placement is critical in the structural health monitoring system as the sensor configuration directly impacts the quality of collected data used for structural health diagnosis. Therefore, this study presents a comprehensive review of computational methodologies for optimal sensor placement in structural health monitoring. The problem formulation of optimal sensor placement is first introduced, including commonly used evaluation criteria for sensor configurations. Then, various existing optimization methodologies for sensor placement are summarized and introduced in detail, especially for the evolutionary algorithms and their improved variants. Finally, the suitability of computational methods for specific structural health monitoring applications is also discussed. The main goal of this study is to deliver a comprehensive reference of computational methodologies for optimal sensor placement in structural health monitoring studies and applications. This article is concluded by highlighting the most widely utilized evaluation criteria and optimization methodologies for sensor configuration determination.

Structural health monitoring has arisen as an important tool for managing and maintaining civil infrastructure. A critical problem for all structural health monitoring systems is data loss or data corruption due to sensor failure or other malfunctions, which bring into question in subsequent structural health monitoring data analysis and decision-making. Probability density functions play a very important role in many applications for structural health monitoring. This article focuses on data loss compensation for probability density function estimation in structural health monitoring using imputation methods. Different from common data, continuous probability density functions belong to functional data; the conventional distribution-to-distribution regression technique has significant potential in missing probability density function imputation; however, extrapolation and directly borrowing shape information from the covariate probability density function are the main challenges. Inspired by the warping transformation of distributions in the field of functional data analysis, a new distribution regression approach for imputing missing correlated probability density functions is proposed in this article. The warping transformation for distributions is a mapping operation used to transform one probability density function to another by deforming the original probability density function with a warping function. The shape mapping between probability density functions can be characterized well by warping functions. Given a covariate probability density function, the warping function is first estimated by a kernel regression model; then, the estimated warping function is used to transform the covariate probability density function and obtain an imputation for the missing probability density function. To address issues with poor performance when the covariate probability density function is contaminated, a hybrid approach is proposed that fuses the imputations obtained by the warping transformation approach with the conventional distribution-to-distribution regression approach. Experiments based on field monitoring data are conducted to evaluate the performance of the proposed approach. The corresponding results indicate that the proposed approach can outperform the conventional method, especially in extrapolation. The proposed approach shows good potential to provide more reliable estimation of distributions of missing structural health monitoring data.

As a typical non-contact sensing and monitoring capability, optical measurements have been gaining tremendous attention recently in structural dynamics identification and health monitoring. This non-contact methodology usually supplies full-field measurement, and is able to out-play traditional transducers such as accelerometers, laser vibrometers and strain gauges. As a new computer vision based approach, 2-D Phase-Based Motion Estimation (PME) is extended in this paper to 3-D applications for structural health monitoring (SHM) purposes. Compared to the 2-D PME, which only captures the in-plane motions on the selected field of view of the camera, 3-D PME removes these restrictions and estimates the out-of-plane motion partially if the required conditions are met for the proposed algorithm. The modal information such as natural frequencies of the test wind turbine blade are extracted and compared with the results obtained via 2-D PME, as well as other conventional measurement approaches. With all the modes decoupled on the testing turbine blade, a contrived damage is added to the structure, and the 3-D PME proposed in this paper successfully extracts the modal frequencies for both conditions, baseline and damaged, and therefore detects damages in a non-contact and full-scale fashion.

The load-carrying capability of marine steel structures often suffers degradation and at times structural failure during their service life due to the corrosive sea environment and the extreme wave loads which they experience. Structural integrity is a high priority during asset design and operation to preserve human safety, environment protection, and to maintain operations. Structural Health Monitoring (SHM), a process of deriving the health status and predicting structural damage via sensor-based measurements, data trending and analysis, has been commonly implemented in marine and offshore assets for decades. There are mature commercial SHM products on the market and available international and industrial regulations, guidance and classification rules (such as IMO MSC/Circ. 646, ABS Guide for Hull Condition Monitoring Systems, etc.). With the emergence of the Structural Digital Twin (SDT) concept in the marine industry in the recent years, the application of structural sensors on marine structures and the integration between sensor-based SHM techniques and structural digital twin approaches have attracted more attention. As a digital representation of the physical asset, the structural digital twin typically involves multiple-scale, multiple-physics, and data driven models and simulations. Leveraging the high fidelity full-scale SHM data, the structural digital twin is capable to provide more accurate assessments, predictions, and insights on structural integrity conditions and support the decision-making process regarding asset operation, structural inspection, repairs, and condition-based asset integrity management. This paper reviews the mature and commonly employed SHM sensor techniques (such as electric resistance strain gauge, fiber Bragg grating (FBG) strain gauge, accelerometer, pressure transducer, etc.) and discusses their implementation on marine assets and integration with structural digital twin. The paper elaborates to provide some general guidance on the selection of the structural sensors and their installation, as well as the proper sensor specification, in the context of a structural digital twin implementation. The paper also discusses the required engineering mindsets and skillsets for developing and deploying sensor plans, and for implementing a sensor-based structural digital twin framework. The paper concludes that a successful SHM plan and structural digital twin implementation rely on sensor planning, data acquisition, data processing, analytic models and a proper integration of the data and data analytics. The overall planning and implementation should be governed by the suitability for the business purpose and implementation expectation.

The large number of long-span bridges constructed in China motivates the applications of structural health monitoring (SHM) technology. Many bridges have been equipped with sophisticated SHM systems in the mainland of China and in Hong Kong of China. Recently, SHM technology has been extended to field test systems. In this view, SHM can serve as a tool to develop the methods of life-cycle performance design, evaluation, maintenance and management of bridges; to develop new structural analysis methods through validation and feedback from SHM results; and to understand the behavior of bridges under natural and man-made disasters, rapidly assess the damage and loss of structures over large regions after disasters, e.g., earthquake, typhoon, flood, etc. It is hoped that combining analytical methods, numerical simulation, small-scale tests and accelerated durability tests with SHM could become the main engine driving the development of bridge engineering. This paper demonstrates the above viewpoint.

Probability of detection (POD) calculations in structural health monitoring (SHM) applications are complicated by the dependency of measurements obtained on the same structure, among other factors. This article presents a repeated measures method to extend POD signal-response modeling to correctly describe a population of repeated measurements while estimating the variance due to dependence in the observations. In particular, equations are presented which develop an autoregressive correlation structure to model continuous observations that are correlated in time. Software implementation of these models is discussed and methodology to simulate correlated datasets is presented. The combination of these tools enables a method of POD estimation in SHM applications through the appropriate mathematical extensions of the statistical modeling.

The design, development, and testing of a low-cost phase optical time-domain reflectometry (Phase-OTDR) system, intended for use in structural health monitoring (SHM) applications, are presented. Phase-OTDR is a technology that is growing and evolving at an impressive rate. Systems based on this principle are becoming very sensitive and elaborate and can perform very accurate condition monitoring, but at the same time, they are critically alignment-dependent and prohibitively costly to be considered as viable options in real field applications. Certain Phase-OTDR systems have been applied in real field studies, but these examples are mostly a proof-of-concept. The system presented here is the result of a compromise between performance and cost, using commercial components, specifically combined and tuned for SHM applications. The design and implementation of all the electronic and optoelectronic steps are presented, and the operation of the system is demonstrated, achieving a spatial resolution of ~6 m over 5 km. This work provides useful engineering guidelines for the low-cost implementation of Phase-OTDR systems. It is anticipated that the affordable development of such interrogation systems will promote their use in a wide range of SHM applications with moderate monitoring requirements and will assist the penetration of Phase-OTDR technology in the industry.

The active sensing methods using piezoelectric materials have been extensively investigated for the efficient use in structural health monitoring (SHM) applications. Relying on high frequency structural excitations, the methods showed the extreme sensitivity to minor defects in a structure. Recently, a sensor self-diagnostic procedure that performs in situ monitoring of the operational status of piezoelectric (PZT) active sensors and actuators in SHM applications has been proposed. In this investigation, previously developed impedance models were revisited in order to investigate the effects of sensor and/or bonding defects on the admittance measurement. New parameters for sensor quality assessment of a PZT and coupling degradation effects between a PZT and bonding layer were incorporated into the traditional electromechanical impedance model for better estimation of the electromechanical impedance signatures and sensor diagnostics. The feasibility of the modified impedance model for sensor self-diagnosis using the admittance measurements was demonstrated by a series of parametric studies using a simple example of PZT-driven single degree of freedom spring-mass-damper system. This paper summarizes the description of the proposed modified electromechanical impedance model, parametric studies for impedance-based sensor diagnostics, and several issues that can be used as a guideline for future investigation.

Quasi-phase-matched nonlinear Lamb waves in composite laminates for material degradation monitoring

Wireless sensor networks are being increasingly accepted as an effective tool for structural health monitoring. The ability to deploy a wireless array of sensors efficiently and effectively is a key factor in structural health monitoring. Sensor installation and management can be difficult in practice for a variety of reasons: a hostile environment, high labour costs and bandwidth limitations. We present and evaluate a proof-of-concept application of virtual visual sensors to the well-known engineering problem of the cantilever beam, as a convenient physical sensor substitute for certain problems and environments. We demonstrate the effectiveness of virtual visual sensors as a means to achieve non-destructive evaluation. Major benefits of virtual visual sensors are its non-invasive nature, ease of installation and cost-effectiveness. The novelty of virtual visual sensors lies in the combination of marker extraction with visual tracking realised by modern computer vision algorithms. We demonstrate that by deploying a collection of virtual visual sensors on an oscillating structure, its modal shapes and frequencies can be readily extracted from a sequence of video images. Subsequently, we perform damage detection and localisation by means of a wavelet-based analysis. The contributions of this article are as follows: (1) use of a sub-pixel accuracy marker extraction algorithm to construct virtual sensors in the spatial domain, (2) embedding dynamic marker linking within a tracking-by-correspondence paradigm that offers benefits in computational efficiency and registration accuracy over traditional tracking-by-searching systems and (3) validation of virtual visual sensors in the context of a structural health monitoring application.

In order to promote the engineering application of carbon fiber reinforced concrete (CFRC) in the field of structural health monitoring, this study systematically explores the optimization of CFRC conductivity and monitoring applications. Through experiments, the key influencing factors of the conductivity of CFRC were identified, and based on these factors, three optimization methods were proposed: optimizing the carbon fiber content and distribution through ultrasonic stirring and magnetic field induction techniques, improving interface bonding through oxidation/coupling agent treatment, and constructing a synergistic conductive network by adding carbon nanotubes and graphene, all of which achieved a reduction in CFRC resistivity and an improvement in conductivity. In applied research, the CFRC force electric coupling effect is established as the monitoring core principle (external loads cause microstructural changes, which in turn lead to quantitative correlations between resistance and stress, strain, and damage), and a structural health monitoring system is constructed that includes CFRC sensors, data acquisition and transmission, and data analysis and processing (statistical analysis, machine learning algorithms) to achieve real-time perception, accurate judgment, and early warning of structural status. This research results provide theoretical and technical support for the large-scale application of CFRC in engineering structural health monitoring.

Distributed Sensor Networks (DSN) has emerged as a sensing paradigms in the structural egineering field due to the application of Structural Health Monitoring (SHM) for large-scale structures which results in accurately diagnosing the health of structures and enhancing the re liability and robustness of monitoring systems. The multisensor network greatly enhances the feasibility of applying SHM and also provides awareness of structural damage. In this work, we develop data-driven method for the diagnosis of damage in mechanical structures using an array of distributed sensors. The proposed approach relies on comparing intrinsic geometry of data sets corresponding to the undamage and damage state of the system. This approach assumes no knowledge of underlying models of the different data sources. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion map. The low dimensional embedding of the data set corresponding to different damage level is done using singular value decomposition of the diffusion map to identify the intrinsic geometry. We construct appropriate metric in diffusion space to compare the low-dimensional data set corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. Towards this goal we developed a detailed finite element-based model of CX-100 blade in ANSYS using shell elements. The damage in the blade is modeled by degrading the material property which in turn results in change of stiffness. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with relatively small signal to noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm can not only account for data from different sensors but also different types of sensor in the form of sensor fusion hereby making it attractive to exploit the distributed nature of sensor array. The distributed nature of sensor array is further exploited to determine the location of