Structural Health Monitoring Information Research Articles

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification.

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork.

A Digital Twin Model of High-Pile Docks’ Structures based on Neo4j Knowledge Graph

High-pile docks plays a vital role in modern civil engineering field. The operational efficiency and structural safety of these docks are paramount. However, structural damage to high-pile docks can lead to economic and human losses. To effectively manage structural health monitoring information for high-pile docks, this research utilizes knowledge graph technology to construct a visual knowledge repository for high-pile docks, named as the High-Pile Dock Knowledge Graph (DKG-Neo4j). For the construction of DKG-Neo4j, a plethora of literature, news, and standards related to health monitoring in the field of high-pile docks were gathered. Then, the collected information was organized hierarchically and categorized into tables, with CSV files exported using Python. Finally, this aggregated information was integrated into the Neo4j graph database to establish a comprehensive knowledge graph. The proposed High-Pile Dock Knowledge Graph (DKG-Neo4j) not only reflects the current state of the docks but also captures the connections between issues and maintenance strategies, empowering experts to engage in structural health monitoring and risk analysis. The DKG-Neo4j model boasts structural flexibility and scalability, contributing to smart cities and providing new approaches for the sustainable development of high-pile docks and the digital management of civil engineering.

Optimizing utilization strategy of real-time SHM information for structural integrity management based on preposterior decision analysis

In the context of structural integrity management (SIM), effective utilization of real-time structural health monitoring (SHM) information is crucial for minimizing the expected value of life-cycle costs. To enhance the benefits of real-time SHM information for SIM, this paper introduces a framework aimed at optimizing the utilization of real-time SHM information from a life-cycle perspective, grounded in Bayesian posterior decision analysis. In addressing this issue, the paper utilizes a robust probabilistic model that is intimately connected with real-time SHM information. This model plays a crucial role in quantifying how the availability of real-time SHM data impacts the benefits of SIM. Following this quantification in the proposed analytical framework, the paper conducts a comprehensive comparative analysis of various utilization strategies, highlighting their respective efficiencies and effectiveness in the context of SIM. Through a case study centered on a weld exposed to fatigue loading, the paper not only showcases the proposed framework but also demonstrates its efficacy in identifying the optimal strategy for real-time SHM information utilization.

Visualization of structural health monitoring information using Internet-of-Things integrated with building information modeling

Structural Health Monitoring (SHM) has become a paramount necessity in civil engineering for improving the operational performance of aging infrastructure. Recent monitoring techniques have utilized emerging technologies in Industry 4.0, such as the Internet of Things, Big Data analytics, cloud computing, and cybersecurity, to automate SHM methodologies. However, they have found challenges in linking these technologies and developing an autonomous, well-established digital framework for applications of SHM. Visualizing processed SHM data in a real-time digital interface generates multiple obstacles, such as witnessing delays in data transfer and resorting to offline tools for manual data processing. This paper, therefore, explores the integration of Building Information Modeling (BIM) and the Internet of Things (IoT) through an Arduino micro-processing unit for tracking and visualizing the data from the time and frequency domains. Strategies for enabling data monitoring and processing are developed while continuously acquiring structural responses. The query of data is established in a web-based database instead of storing the data in offline resources that await manual intervention. The proposed real-time SHM methodology is validated experimentally using two practical applications: a dynamically moving vehicle over a simply-supported bridge prototype and a randomly excited three-story model with real-time visualization of both time- and frequency-domain information under undamaged and damaged conditions. The proposed research develops an early-phase Digital Twin (DT) to present static and real-time dynamic data in a rich-fed BIM database.

Monocular vision based 3D vibration displacement measurement for civil engineering structures

Vibration displacement of civil structures are crucial information for structural health monitoring (SHM). However, the challenges and costs associated with traditional physical sensors make displacement measurement difficult. In recent years computer vision (CV) techniques have been employed for measuring vibration displacement in civil structures. There has been a growing interest in CV-based three-dimensional (3D) displacement measurement, as it provides comprehensive information for structural health assessment. Most existing methods use multi-view geometry, requiring multiple cameras for depth measurement. This paper proposes a new system for measuring the 3D vibration displacement utilising a single camera. Instead of using multi-view geometry, deep neural networks are utilised to learn the depth of scenes from monocular images. Compared with the multi-view methods, the proposed 3D measurement system with monocular vision is more cost-effective and much more convenient to set up and use in practice, avoiding the complicated calibration and object matching between multiple cameras. Experimental tests are conducted in the laboratory to investigate the feasibility of the proposed system. Physical displacement sensors are equipped with the testing structure to provide the ground truth data. The results demonstrate that the proposed monocular 3D displacement system is able to produce reasonable 3D full-field displacement measurement, which makes monocular image based CV system a promising approach to achieve 3D displacement measurement, with its obvious advantages in cost and convenience compared to the traditional sensor-based or multi view CV-based methods.

The Benefit of Informed Risk-Based Management of Civil Infrastructures

One of the most interesting applications of Structural Health Monitoring (SHM) is the possibility of providing real-time information on the conditions of civil infrastructures during and following disastrous events, thus supporting decision-makers in prompt emergency operations. The Bayesian decision theory provides a rigorous framework to quantify the benefit of SHM through the Value of Information (VoI) accounting for different sources of uncertainties. This decision theory is based on utility considerations, or, in other words, it is based on risk. Instead, decision-making in emergency management is often based on engineering judgment and heuristic approaches. The goal of this paper is to investigate the impact of different decision scenarios on the VoI. To this aim, a general framework to quantify the benefit of SHM information in emergency management is applied to different decision scenarios concerning bridges under scour and seismic hazards. Results indicate that the considered decision scenario might tremendously affect the results of a VoI analysis. Specifically, the benefit of SHM information could be underestimated when considering non-realistic scenarios, e.g., those based on risk-based decision-making, which are not adopted in practice. Besides, SHM information is particularly valuable when it prevents the selection of suboptimal emergency management actions.

Quantifying the value of structural health monitoring information with measurement bias impacts in the framework of dynamic Bayesian Network

Structural Health Monitoring (SHM) information contributes substantially to Structural Integrity Management (SIM), which can be achieved through reducing epistemic uncertainty and/or enriching decision-making alternatives. However, measurement uncertainties in terms of random error and measurement bias, and the degradation of monitoring performance typically exist. These factors negatively affect the contributions of an SHM system in the context of SIM. To efficiently quantify the Value of Information (VoI) of the SHM system, and to investigate the effects of the influencing factors on the VoIs, this work performs VoI analyses within the computational framework of Dynamic Bayesian Network (DBN). In this framework, Risk-Based Inspection (RBI) planning is used as the prior decision scenario, and two maintenance strategies considering SHM information are proposed as the pre-posterior decision scenario. To demonstrate the significance of taking into account measurement bias and monitoring performance deterioration, two scenarios, considering and ignoring these two influencing factors, are taken into consideration. Finally, the main purpose is demonstrated with a case study associated with optimizing the inspection and maintenance strategy for welded joints subjected to fatigue loading.

Investigation of structural responses and dynamic characteristics of a supertall building during Typhoon Kompasu

In this paper, comprehensive field measurements were conducted on a 600-m-high skyscraper in Shenzhen during Typhoon Kompasu based on structural health monitoring (SHM) system and a remotely non-contact interferometric radar system called IBIS-FS. The SHM system recorded wind field and structural responses, including accelerations at 10 building heights by accelerometers and horizontal displacements atop the building by global positioning system (GPS). Meanwhile, the IBIS-FS simultaneously measured the horizontal displacements of the building at multiple heights. This is the first time that the interferometric radar system, i.e., IBIS-FS, is applied to the displacement monitoring of a supertall building under typhoon conditions. The effectiveness of the IBIS-FS is verified through comparing the building responses measured by the IBIS-FS with those measured by the SHM system. Then, based on the field-measured responses, the mean, background, and resonant responses of the monitored supertall building are extracted and analyzed; and the amplitude dependencies and time-varying characteristics of the structural modal parameters (i.e., natural frequencies and damping ratios) are presented and discussed. The results show that the IBIS-FS can provide convenient and accurate displacement measurements of the skyscraper under severe weather conditions. This paper aims to provide useful information for SHM and wind-resistant design of supertall buildings in tropical cyclone-prone regions.

Fatigue reliability analysis considering corrosion effects and integrating SHM information

Fatigue cracking is a common problem facing steel bridges. S-N curves have been adopted in design specifications to offer guidance on fatigue design of these bridges. Structural health monitoring (SHM) techniques can provide reliable information on stress states at fatigue-prone details using strain sensors, thereby circumventing complex theoretical analyses. When corrosion is present, simply relying on SHM information obtained through the monitoring period may not be sufficient to reliably estimate the damage incurred by corrosion-enhanced fatigue as corrosion is a long-term process and the time-variant effect of corrosion on fatigue needs to be determined. In this paper, time-variant analysis is used to estimate the reliability of fatigue-prone details at the coped region of a transverse diaphragm in a tied-arch bridge. Stress histograms obtained based on SHM information associated with this case study are utilized to establish the distribution of the original effective stress. Finite element analysis is conducted for (a) determining the spatial adjustment factor (SAF) to adjust the stress range obtained at the location of strain sensors, and (b) assessing the effect of corrosion on the effective stress range. The reliability analysis results show that for the fatigue details investigated in this case study, corrosion has a significant effect on the fatigue reliability and, therefore, should be considered in the life-cycle management of fatigue-prone details.

The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

This paper proposes an approach to select a maintenance strategy from a predefined set of choices considering the decision maker’s behavioral risk profile. It is assumed that the damage state is characterized by a continuous state parameter probabilistically inferred from observable sensor data. This work applies an engineering application of consequence-based decision-making incorporating the acceptable risk intensity of the decision-maker, i.e., the decision-maker’s (individual or an organization) valuation of the outcome of a decision, using a risk profile model. The utility of a decision-maker is subjective, and this paper considers the fact that different decision-makers mentally assign a different importance factor (the utility) to the seriousness or urgency to take necessary actions with the increasing intensity of structural damage. The approach herein incorporates a layer of human psychology on selecting appropriate maintenance strategies that not only depend on the posterior distribution of unmeasurable damage state but also consider the behavioral risk profile of the decision-maker. The collective decision-making of an organization consisting of many individuals is also investigated. The approach is exemplified in a case study involving life cycle monitoring of a miter gate, part of a lock system enabling navigation of inland waterways.

Vision‐based displacement measurement using a camera mounted on a structure with stationary background targets outside the structure

While structural displacements are essential information for structural health monitoring, they are not being widely used in practice due to the inconvenience. Recently, vision-based displacement measurement methods have been introduced, which are more convenient and cost effective. However, vision-based methods have generally not been used primarily for the continuous monitoring of structures, due to spatial constraints on obtaining an appropriate location to secure the field of view. A vision device shows not only the changes of objects in the scene but also the relative changes of view according to changes in the position to which it is mounted. Accordingly, this study proposes a methodology for measuring the structural displacement of a location where a camera is mounted, based on a camera motion-induced relative view change. The method is organized into three steps. First, camera calibration is conducted with background targets to derive the camera parameters and coordinates of the target feature points. Second, by tracking the relative changes in the feature points according to the camera motion, the changed 2D-image coordinates of the points are derived. Third, the displacement is calculated through the relationship between the changed 2D-image coordinates and fixed 3D-world coordinates of the target feature points using the camera parameters. The changes in view according to the camera motion are analyzed with simulation tests, and the applicability of the proposed method is verified through experimental tests. The results show that the proposed method can be used to rationally measure structural displacements.

A Two-Stage Approach for Damage Diagnosis of Structures Based on a Fully Distributed Strain Mode under Multigain Feedback Control

The application of distributed fiber sensing technology in civil engineering has been developed to obtain more accurate and reliable information for structural health monitoring (SHM). With this sensing technique, high-density strain data are provided to benefit the stability and robustness in a closed-loop damage detection method which has not yet been investigated. To address this concern, a two-stage approach for structural damage detection combining a modal strain energy-based index (MSEBI) method with a hybrid artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. In this study, the fully distributed strain measurement is taken advantage of, and a strain-based, closed-loop system with multiple gains aggregated for damage sensitivity enhancement is established, by which high-precision damage location and quantification can be realized through the proposed two-stage method. For the first step, the closed-loop strain mode shapes are used to construct the MSEBI for damage localization. For the second step, we adopt the PSO algorithm to train the parameters (weights and biases) of the neural network in order to reduce the difference between the real and expected outputs and then use the trained network for quantifying the damage extent. Furthermore, validation is completed by contemplating a two-span, bridge-like structure.

VoI analysis of temporally continuous SHM information in the context of adaptive risk-based inspection planning

Decision-making on integrity management strategy of engineering structures can be regarded as a time dependent problem, where optimal decisions change over the course of the operational service life depending on the information collecting in the past. Moreover, continuously collected SHM information is also closely related to time, in which the incompleteness of information and temporal effect on the reduction of epistemic uncertainties affecting the life-cycle costs are strongly affected by the timing of decisions. To consider the effects of incompleteness on the optimal decision making and the corresponding value of information (VoI), the present contribution focuses on the analysis of time-dependent and incomplete Structural Health Monitoring (SHM) information value within a framework of adaptive risk-based inspection (RBI) planning. To this end, results of past inspections and maintenance actions together with temporal continuous SHM information are jointly considered in the proposed framework. To account for the time-dependency and incomplete properties of SHM information, a thorough information modelling method is given, along with a simplified approach to the quantification of the VoI. The analysis approach is demonstrated through an example considering integrity management of a structural detail subject to fatigue degradation. The example results demonstrate the potential benefits of adaptive RBI planning and the value of fatigue stress information in the context of structural integrity management. Finally, the present work concludes with a discussion of limitations associated with the proposed schemes and points at the prospects for further investigations.

Joint Estimation of Multi-Scale Structural Responses and Unknown Loadings Based on Modal Kalman Filter Without Using Collocated Acceleration Observations

It has been proved that the usage of multi-type observations including global and local information for structural health monitoring (SHM) outperforms that of solo-type measurement. Kalman filter (KF) is a simple but powerful tool for the online state estimation. However, external force is required for the classic KF. Moreover, direct implementation of KF in time domain may be time consuming especially for large structures with many degrees-of-freedoms (DOFs) involved. From these points of view, the idea of modal Kalman filter (MKF) is introduced. An MKF-based approach is then proposed for joint estimation of multi-scale responses and unknown loadings with data fusion of multi-type observations. By using a projection matrix and the selected several modes, a modified observation equation in modal domain is derived. Limited multi-type measurements are fused together for online estimating multi-scale responses of structure at its critical locations. The unknown excitation is simultaneously identified by least squares estimation (LSE). The drift problem of the real time estimation of structural responses and unknown loads is avoided. The effectiveness of the proposed approach is numerically demonstrated via several examples. Results show that the proposed approach is capable of satisfactorily estimating the unmeasured multi-scale responses and identifying the unknown loadings.

Quantifying the value of SHM information for bridges under flood-induced scour

Bridge scour is a leading cause of failure for bridges over waterways and is notoriously difficult to detect with accuracy. Dynamic Structural Health Monitoring (SHM) of bridges for scour has gained traction in recent years as monitoring systems have improved and the reliability of measurements increased. Due to the large number of bridges on typical networks and the limited financial resources within asset management agencies, decision-makers must prioritize certain structures when it comes to management in the event of flooding. The decision to install a dynamic SHM system on a bridge must be balanced by the financial benefit of doing so, as limited resources often need to be carefully rationed. In this paper, a methodology is proposed to evaluate such benefit based on the Value of Information (VoI) from Bayesian decision analysis. A case study is presented whereby a dynamic SHM system is considered to be installed on a typical bridge with the aim to support emergency management operations during flooding. The proposed methodology allows computation of the financial benefit of installing a dynamic SHM system over a certain reference period, thus accounting for multiple flood events and scenarios. The various elements of the procedure are discussed in detail.

Performance Evaluation of a Long-Span Cable-Stayed Bridge Using Non-Destructive Field Loading Tests

As an important part of the transportation network, the reliability of bridge structures is of great significance to people’s personal safety, as well as to the national economy. In order to evaluate the performance of complex bridge structures, their mechanical behavior and fundamental characteristics need to be studied. Structural health monitoring (SHM) has been introduced into bridge engineering, and the structural response assessment, load effects monitoring, and reliability evaluation have been developed based on the collected SHM information. In this study, a performance evaluation method for complex bridge structures based on non-destructive field loading tests is proposed. The cable-stayed bridge in Guangxi with the largest span (Pingnan Xiangsizhou Bridge) was selected as the research object, and loading on the main girder was transferred to the piers and tower through the stay cables, whose structural responses are critical in the process of bridge operation. Therefore, the field loading tests—including deflection and strain testing of the main girder, as well as cable force tests—were also conducted for Pingnan Xiangsizhou Bridge by using non-destructive measurement techniques (multifunctional static strain test system, radar interferometric deformation measurement technology, etc.). Based on the numerically simulated results of a finite element model for Pingnan Xiangsizhou Bridge, reasonable field loading test conditions and loading arrangement were determined. Non-destructive field loading test results showed that the quality of the bridge’s construction is up to standard, due to a good agreement between the calculated and measured frequencies of the bridge. In addition, the calibration coefficients of displacement and strain were less than 1, indicating that Pingnan Xiangsizhou Bridge has satisfactory stiffness and strength.

Dissimilarity-vector spaces based on Dynamic Time Warpings of spectral/time-frequency information for structural health monitoring

This paper presents a powerful data representation, so far unexplored for data-based Structural Health Monitoring, relying on the dissimilarity pattern recognition paradigm and the proximity-learning, providing highly discriminant dissimilarity-based vector spaces, —also called generalized dissimilarity kernels—, where any classifier can be trained for damage classification issues. Conventionally, these damage detection tasks involve a domain-dependent and, quite often, non-trivial preprocessing step by which is computed a feature set from each observation. A crucial consequence is that valuable structural information could be lost in this feature extraction step, leading to models with poor performance. In particular, in this paper we introduce a novel type of dissimilarity-based vector spaces for structural health diagnosis, building up them on a direct pairwise comparison between spectral/time–frequency structural information via the Dynamic Time Warping distance, without a previous feature extraction step, for learning one-class classifiers and using only undamaged data during training. The very sound results, using two data sets widely referenced in the scientific literature, clearly show its potential to complement the state-of-art of pattern recognition algorithms that are used on data-based Structural Health Monitoring.

Aircraft Maintenance: Structural Health Monitoring Influence on Costs and Practices

AbstractThe structural health monitoring (SHM) represents an evolution of the traditional way to design and maintain mechanical structures. SHM systems allow to continuously (or discretely) monitor the structural integrity of an asset, not waiting for the planned check. This result implies an important reduction of inspection time and guarantees an improvement of service life cost of the structural components, but how and how much this cost can be influenced is still an open issue. This research addresses the identification of previous studies that dealt with this topic. A systematic literature review (SLR) is carried out with the aim of investigating the current SHM state‐of‐the art in the aircraft industry in order to point out the main advantages deriving from the application of these technologies and the existing gaps and limitations. Three database (Scopus, Web of science and Google Scholar) are used for selecting peer‐reviewed papers that address with evidence the changes in maintenance practices resulting from the use of SHM. The analysis proves the differences between the traditional programmed maintenance and the innovative predictive approach based on SHM information in terms of interval inspection policy and costs. Moreover, the technical limits that do not allow to totally exploit the potential strength of SHM are outlined.

Investigation on the data augmentation using machine learning algorithms in structural health monitoring information

Structural health monitoring system plays a vital role in smart management of civil engineering. A lot of efforts have been motivated to improve data quality through mean, median values, or simple interpolation methods, which are low-precision and not fully reflected field conditions due to the neglect of strong spatio-temporal correlations borne by monitoring datasets and the thoughtless for various forms of abnormal conditions. Along this line, this article proposed an integrated framework for data augmentation in structural health monitoring system using machine learning algorithms. As a case study, the monitoring data obtained from structural health monitoring system in the Nanjing Yangtze River Tunnel are selected to make experience. First, the original data are reconstructed based on an improved non-negative matrix factorization model to detect abnormal conditions occurred in different cases. Subsequently, multiple supervised learning methods are introduced to process the abnormal conditions detected by non-negative matrix factorization. The effectiveness of multiple supervised learning methods at different missing ratios is discussed to improve its university. The experimental results indicate that non-negative matrix factorization can recognize different abnormal situations simultaneously. The supervised learning algorithms expressed good effects to impute datasets under different missing rates. Therefore, the presented framework is applied to this case for data augmentation, which is crucial for further analysis and provides an important reference for similar projects.

The value of structural health monitoring in seismic emergency management of bridges

The management of civil infrastructures in the aftermath of a seismic event is a concern for decision makers, which have to choose quickly among alternative actions with limited knowledge on the actual structural conditions. The availability of real time Structural Health Monitoring (SHM) data on the asset might be particularly useful. However, SHM data are not collected for free, and the cost of the SHM system should be compared with its associated benefit. A powerful tool to estimate such benefit is the Value of Information (VoI) from Bayesian decision theory. This paper provides a methodology to compute the VoI of SHM for seismic emergency management of roadway bridges. This methodology can be used by decision makers before the installation of a seismic SHM system to quantify the cost benefit of doing so and thus optimize the allocation of economic resources. Results show that the VoI is high when the expected costs of the decision alternatives (such as ‘keep the bridge open’ or ‘close the bridge’) evaluated without the SHM information are comparable. In this condition, which is highly dependent on the seismic hazard and on emergency management costs at stake, the SHM information provides the maximum support to decision making.