Real Structural Health Monitoring Research Articles

Analytical Bayesian Copula‐Based Uncertainty Quantification (A‐BASIC‐UQ) Using Data with Missing Values in Structural Health Monitoring

The presence of missing values is common in real‐world datasets, so modeling and uncertainty quantification (UQ) of incomplete datasets have gained increasing attention in various research areas, including structural health monitoring (SHM). However, modeling and UQ utilizing incomplete datasets are nontrivial tasks. On the other hand, prediction based on a set of incomplete measured input variables is also an important task, but most existing methods, which are discriminative models, do not possess this capability. Aiming to tackle these two challenges, we propose the two‐stage analytical Bayesian copula‐based uncertainty quantification (A‐BASIC‐UQ) using incomplete SHM data. In the modeling stage, the copula‐based multivariate joint probability density function (PDF) is modeled directly according to an incomplete dataset without imputation or disposal of any data points. For the univariate marginal PDF, using the measured (nonmissing) values of the corresponding random variable (RV), Bayesian model class selection is conducted to select the most suitable model class. For the Gaussian copula PDF, using the bivariate complete data points of entry‐by‐entry pairwise data, the optimal parameter vector is obtained from the estimation of the Pearson correlation coefficient. In the prediction stage, the analytical expressions of the predictive PDF, the predicted value and the credible region of the output variables are derived according to a set of incomplete measured input variables. The analytical expression of the predictive PDF is obtained based on the analytical operations on the auxiliary RVs and that of the predicted value and the credible region are obtained based on the analysis of multivariate Gaussian distribution. Therefore, the proposed method does not require numerical integration nor Monte Carlo simulation and does not suffer from computational burden even when there are many variables (say 4 or above). Examples using simulated data and real SHM data are presented to illustrate the capability of the proposed A‐BASIC‐UQ.

An IoT Toolchain Architecture for Planning, Running and Managing a Complete Condition Monitoring Scenario

Condition Monitoring (CM) is an extremely critical application of the Internet of Things (IoT) within Industry 4.0 and Smart City scenarios, especially following the recent energy crisis. CM aims to monitor the status of a physical appliance over time and in real time in order to react promptly when anomalies are detected, as well as perform predictive maintenance tasks. Current deployments suffer from both interoperability and management issues within their engineering process at all phases– from their design to their deployment, to their management–, often requiring human intervention. Furthermore, the fragmentation of the IoT landscape and the heterogeneity of IoT solutions hinder a seamless onboarding process of legacy devices and systems. In this paper, we tackle these problems by first proposing an architecture for CM based on both abstraction layers and toolchains, i.e., automated pipelines of engineering tools aimed at supporting the engineering process. In particular, we introduce four different toolchains, each of them dedicated to a well-defined task (e.g., energy monitoring). This orthogonal separation of concerns aims to simplify both the understanding of a complex ecosystem and the accomplishment of independent tasks. We then illustrate our implementation of a complete CM system that follows said architecture as a real Structural Health Monitoring (SHM) pilot of the Arrowhead Tools project, by describing in detail every single tool that we developed. We finally show how our pilot achieves the main objectives of the project: the reduction of engineering costs, the integration of legacy systems, and the interoperability with IoT frameworks.

IoT Edge Caching: Taxonomy, Use Cases and Perspectives

Low latency is an essential constraint for many emerging Internet of Things (IoT) applications. Cloud-based solutions can impose excessive network and processing latency that might hinder the system's operation. On the other hand, offloading tasks to the edge layer - close to where data is consumed - might be unfeasible in many situations due to the low computational resources of edge devices or because the system utilizes third-party applications. One potential solution is caching data on the edge of the network, thus decreasing the latency of subsequent requests, saving bandwidth, and avoiding redundant computation. At the same time, caching IoT data on the edge has its challenges and features that deviate from the standard methods of network caching, such as the content dynamicity and the power constraints. This paper performs a comprehensive review of the state-of-art in IoT Edge Caching and proposes a novel taxonomy focused on five orthogonal features of edge caching: placement, distance, strategy, metrics, and design. Moreover, we illustrate the characteristics identified in five prevalent use cases for the IoT. Finally, we conduct an in-depth analysis and performance evaluation of edge caching deployed in a real Structural Health Monitoring (SHM) scenario. Results show that different caching strategies decrease the system latency by more than 95 percent.

Traffic Load Estimation from Structural Health Monitoring sensors using supervised learning

Traffic Load Estimation (TLE) is increasingly adopted in public road infrastructures to regulate the access and limit heavy vehicles circulation. Standard approaches to TLE are based either on installing dedicated sensors such as intelligent cameras or infrared sensors or using existing smartphone sensors. However, both approaches have severe limitations, as often dedicated sensors are power-hungry and expensive to install and maintain, whereas smartphone-based approaches critically rely massively on users collaboration. More recently, researchers have started investigating TLE approaches using networks of accelerometers that are often already installed on critical road elements such as viaducts and bridges for Structural Health Monitoring (SHM) purposes. Specifically, in previous solutions, the detection and counting of vehicles was based on unsupervised anomaly detection and did not use any labeled data. While this simplifies the system’s setup, it also makes full validation impossible. In this work, we investigate the TLE problem using a supervised learning approach for SHM-sensor-based TLE for the first time. In particular, we use a relatively short recording session from a smart camera to label acceleration data with the corresponding number (and type) of passing vehicles. Labeled data are then fed to a Machine Learning (ML) model trained as a regressor to estimate the vehicle count corresponding to each input sample. We perform an extensive comparison among different types of ML models, both classic and deep. Our experiments find that the highest accuracy is achieved by a Support Vector Regressor (SVR) combined with simple feature extraction, which can reach a Mean Absolute Error (MAE) of 0.47 light vehicles and 0.21 heavy vehicles. This corresponds to a 9.8× and 8.1× error reduction compared to previous unsupervised solutions, respectively. Lastly, we show that our approach lends itself to an energy-efficient implementation on a real SHM gateway.

Optical Fiber Sensors for Ultrasonic Structural Health Monitoring: A Review.

Guided waves (GW) and acoustic emission (AE) -based structural health monitoring (SHM) have widespread applications in structures, as the monitoring of an entire structure is possible with a limited number of sensors. Optical fiber-based sensors offer several advantages, such as their low weight, small size, ability to be embedded, and immunity to electro-magnetic interference. Therefore, they have long been regarded as an ideal sensing solution for SHM. In this review, the different optical fiber technologies used for ultrasonic sensing are discussed in detail. Special attention has been given to the new developments in the use of FBG sensors for ultrasonic measurements, as they are the most promising and widely used of the sensors. The paper highlights the physics of the wave coupling to the optical fiber and explains the different phenomena such as directional sensitivity and directional coupling of the wave. Applications of the different sensors in real SHM applications have also been discussed. Finally, the review identifies the encouraging trends and future areas where the field is expected to develop.

Noise Reduction for Improvement of Ultrasonic Monitoring Using Coda Wave Interferometry on a Real Bridge

Reinforced concrete bridges are iconic parts of modern infrastructure. They are designed for a minimum service life of 100 years. However, environmental factors and/or inappropriate use might cause overload and accelerate the deterioration of bridges. In extreme cases, bridges could collapse when necessary maintenance lacks. Thus, the permanent monitoring for structure health assessment has been proposed, which is the aim of structural health monitoring (SHM). Studies in laboratories have shown that ultrasonic (US) coda wave interferometry (CWI) using diffuse waves has high sensitivity and reliability to detect subtle changes in concrete structures. The creation of micro-cracks might be recognized at an early stage. Moreover, large-volume structures can be monitored with a relatively small number of US transducers. However, it is still a challenge to implement the CWI method in real SHM practical applications in an outdoor environment because of the complex external factors, such as various noise sources that interfere with the recorded signals. In this paper, monitoring data from a 36-m long bridge girder in Gliwice, Poland, instrumented with embedded US transducers, thermistors, and vibrating wire strain gauges, is presented. Noise estimation and reduction methods are discussed, and the influence of traffic, as well as temperature variation, are studied. As a result, the relative velocity variation of US waves following the temperature change with a very high precision of 10^{-4} % is shown, and a good bridge health condition is inferred. The influence of lightweight real traffic is negligible. The study verified the feasibility of the implementation of the CWI method on real bridge structures.

Structural damage detection with distributed long-gauge FBG sensors under multi-point excitations

Distributed strain sensing techniques using long-gauge fiber Bragg grating sensors have been widely applied in health monitoring of civil engineering structures. Developing an effective damage identification method based on the acquired macro-strain is a crucial issue for real applications. In this paper, based on the previous studies on modal macro-strain vector (MMSV) for damage identification under single-point excitation, the damage detection approach under multi-point excitations is further studied. An effective damage index is further proposed as an indicator of the structural damage and the frequency domain decomposition (FDD) technique is introduced to accurately extract the MMSV. The sensitivity and effectiveness of the proposed method for damage detection of a cantilever beam are verified in detail with both numerical simulations and experiments. Results show that both the single and multi damages with different damage extents can be effectively detected under either single point excitations or the wind load excitations. It shows that the proposed approach can do the work well and could be applied for the structural health monitoring of real structures.

Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

Structural parameters are the most important factors reflecting structural performance and conditions. As a result, their identification becomes the most essential aspect of the structural assessment and damage identification for the structural health monitoring. In this article, a structural parameter identification method based on Monte Carlo method and likelihood estimate is proposed. With which, parameters such as stiffness and damping are identified and studied. Identification effects subjected to three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise are studied and compared. Considering the existence of damage, damage identification is also realized by the identification of the structural parameters. Both simulations and experiments are conducted to verify the proposed method. Results show that structural parameters, as well as the damages, can be well identified. Moreover, the proposed method is much robust to the noises. The proposed method may be prospective for the application of real structural health monitoring.

A regime-switching cointegration approach for removing environmental and operational variations in structural health monitoring

Abstract Cointegration is now extensively used to model the long term common trends among economic variables in the field of econometrics. Recently, cointegration has been successfully implemented in the context of structural health monitoring (SHM), where it has been used to remove the confounding influences of environmental and operational variations (EOVs) that can often mask the signature of structural damage. However, restrained by its linear nature, the conventional cointegration approach has limited power in modelling systems where measurands are nonlinearly related; this occurs, for example, in the benchmark study of the Z24 Bridge, where nonlinear relationships between natural frequencies were induced during a period of very cold temperatures. To allow the removal of EOVs from SHM data with nonlinear relationships like this, this paper extends the well-established cointegration method to a nonlinear context, which is to allow a breakpoint in the cointegrating vector. In a novel approach, the augmented Dickey-Fuller (ADF) statistic is used to find which position is most appropriate for inserting a breakpoint, the Johansen procedure is then utilised for the estimation of cointegrating vectors. The proposed approach is examined with a simulated case and real SHM data from the Z24 Bridge, demonstrating that the EOVs can be neatly eliminated.

Estimation on structural responses using multi scale measurements

Different measurands from the different types of sensors can obtain different information regarding the structural behavior in a real structural health monitoring system. To enrich information and estimate the structural responses based on much more known information, the estimation on structural responses using multi scale measurements from multi-type sensors is proposed in this paper. Pattern identification is constructed with the pattern library given by strain measurements and deformation measurements. Considering the uncertainty of the measurements as well as to enhance the robustness of the proposed algorithm, more than one best pattern is selected to synthesize the finally estimated stress responses. To validate the capacity of the proposed acquisition method using multi scale measurements, finite element model analysis is conducted to estimate the structural stress response in Shenzhen Bay Stadium as an example. The performance of the pattern identifications, constructed by two kinds of pattern libraries captured by sole strain measurement, and multi scale measurements which are constructed by both kinds of strain measurements and deformation measurements, respectively, are compared in this paper to observe measurements constructed from strain measurements and deformation measurements outperformed others. Errors analysis for a series of parametric studies in which noise at different levels has also included in the measurements are further carried out, and robustness of the proposed information acquisition scheme under noisy measurement is demonstrated.

Approaches to nonlinear cointegration with a view towards applications in SHM

One of the major problems confronting the application of Structural Health Monitoring (SHM) to real structures is that of divorcing the effect of environmental changes from those imposed by damage. A recent development in this area is the import of the technique of cointegration from the field of econometrics. While cointegration is a mature technology within economics, its development has been largely concerned with linear time-series analysis and this places a severe constraint on its application - particularly in the new context of SHM where damage can often make a given structure nonlinear. The objective of the current paper is to introduce two possible approaches to nonlinear cointegration: the first is an optimisation-based method; the second is a variation of the established Johansen procedure based on the use of an augmented basis. Finally, the ideas of nonlinear cointegration will be explored through application to real SHM data from the benchmark project on the Z24 Highway Bridge.

Simultaneous multipoint acoustic emission sensing using fibre acoustic wave grating sensors with identical spectrum

This paper introduces the development of a simultaneous multipoint acoustic emission (AE) sensing system using a narrowband tuneable laser with high power and fibre acoustic wave grating sensors (FAWGSs). The demodulation technique is the same as that used in existing methods where the narrowband laser peak is tuned to one mid-reflection point in the main lobe of a fibre Bragg grating (FBG) spectrum. However, the sensor head is changed to an FAWGS for which a FBG is installed in a strain-free configuration so that it can detect AE waves in a structure not directly but in the form of a fibre-guided acoustic wave. Therefore since the structural strain cannot make the Bragg wavelength change, multiple FBGs with identical spectrum can be connected with multiple optical paths realized by equal light intensity dividers. The possible temperature difference between the multiple FAWGSs is passively resolved by using short FBGs which provide a wider operating temperature region. Consequently, we can resolve the problem that the FBG spectrum is easily deviated from the lasing wavelength because of the strain. In addition, the simultaneous multipoint sensing capability based on a single laser improves the cost?performance ratio of the optical system as well as reducing the structural inspection time, and enabling in?situ health monitoring of real structures exposed to large and dynamic strains. The feasibility of the system is demonstrated in typical applications of in?situ structural health monitoring based on AE techniques.

Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler

A new Bayesian model updating approach is presented for linear structural models. It is based on the Gibbs sampler, a stochastic simulation method that decomposes the uncertain model parameters into three groups, so that the direct sampling from any one group is possible when conditional on the other groups and the incomplete modal data. This means that even if the number of uncertain parameters is large, the effective dimension for the Gibbs sampler is always three and so high-dimensional parameter spaces that are fatal to most sampling techniques are handled by the method, making it more practical for health monitoring of real structures. The approach also inherits the advantages of Bayesian techniques: it not only updates the optimal estimate of the structural parameters but also updates the associated uncertainties. The approach is illustrated by applying it to two examples of structural health monitoring problems, in which the goal is to detect and quantify any damage using incomplete modal data obtained from small-amplitude vibrations measured before and after a severe loading event, such as an earthquake or explosion.

A Wavelet Packet Based Sifting Process and its Application for Structural Health Monitoring

This article presents an innovative wavelet packet based sifting process to decompose a signal into its components with different frequency contents by examining the energy content in the wavelet packet components of a signal and imposing certain decomposition criteria. A new method is illustrated for simulation data of a linear three-degree-of-freedom spring-mass-damper system and the results are compared with those obtained using the empirical mode decomposition (EMD) method. Both methods provide good approximations, as compared with the exact solution for modal responses from a conventional modal analysis and both show relatively greater errors at the beginning and ending parts of the signal due to the well-known end effects. A comparison study is also provided to illustrate differences between two sifting processes of the proposed approach and the EMD, by using a harmonic signal with a sweeping frequency and the impulse response of a linear single-degree-of-freedom system with viscous damping. Incorporated with the classical Hilbert transform, the proposed sifting process may be effectively used for structural health monitoring, including both detecting abrupt loss of structural stiffness and monitoring development of progressive stiffness degradation, as demonstrated by two case studies. Results from a preliminary study for experimental data from a shaking table test of a full-size two-story wooden building structure are also presented to show great promise of the proposed method in practical applications of health monitoring of real structures.

複合材料積層梁の層間はく離同定手法開発におけるはく離のモデル化の影響

Damage identification methods using vibration data have attracted attention as health monitoring systems of composite laminated structures. In many papers referred to damage identification methods using vibration data, damage is modeled in stiffness degradation regardless of damage modes. In health monitoring of real structures, identifying size and location of slight delamination, which accompany matrix cracks, is important. However, the relationship between the change of natural frequency caused by stiffness degradation and those caused by actual delamination is unknown. Then, unconfirmed applicability of damage identification method with stiffness degradation model to identifying delamination would be introduced. In the present study, the relationship between stiffness degradation and delamination is investigated in terms of factors that control the natural frequency changes of the structures by using FEM. Applicability of damage identification method to delamination identification is also examined.