Foreword

Advances in sensor deployment and computational modeling have allowed significant strides to be made recently in the field of Structural Health Monitoring (SHM). One widely used SHM technique is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability and unknown values for model parameters. Not accounting for uncertainty in the analysis can lead to false-positives or false-negatives in the assessment of the structural condition. To manage the aforementioned uncertainty, we propose a robust-SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes in time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM.

Vibration based baseline updating method to localize crack formation and propagation in reinforced concrete members

As newer and more reliable ways of construction were developed, civilization began to spread out further and retain functional infrastructure for longer periods of time.[...]

Strain is an important parameter reflecting structural state, which is particularly important in the field of structural health monitoring (SHM). It can be used to evaluate the mechanical properties, failure behavior, crack development and residual stress of structural members and materials. It is particularly important in the field of structural health monitoring (SHM). In this paper, a new type of large gauge strain sensor is proposed. The proposed sensor is based on micro vision and uses a camera to capture small displacement. The packaging structure of strain sensor with sensing gauge length of about 50 cm is designed. In order to realize the monitoring in the field environment, the microscopic images in the experiment are obtained by webcam, which has broad application prospects. In the field of view of the camera, the maximum distance that the probe in the sensor can move is certain, but when the sensing gauge distance increases, the accuracy of strain measurement will be improved. The strain data obtained by the sensor is compared with the data obtained by FBG sensors to verify the measurement accuracy. It is found that the measurement accuracy is suitable for SHM of infrastructure.

This research applies a bibliometric analysis to the structural health monitoring (SHM) research field over a 15-year period, from 2003 to 2017. The goal of this research is to provide a broader analysis of the field as a whole through a data-driven approach with a bibliometric analysis of SHM literature. The aim of this work is to understand relationships between research themes and communities within the SHM field, and how this landscape has evolved over time. By employing graph theory and social network analysis methods, this study uses a data-driven analysis using a co-word and co-venue analysis based on publication data from over 21,000 conference and journal papers in SHM. Strategic diagrams and network analysis were used to map and visualize the academic landscape of SHM. Additionally, a co-venue analysis was performed to determine the conference and journal research communities within the field at each time segment. The analysis was conducted for three 5-year time periods providing insight on how the communities within the SHM research field have evolved.

PurposeOne of the paramount challenges in the field of structural health monitoring (SHM) is the precise assessment of structural damage. Recently, many methods have been proposed to diagnose damage in structures using time series data. Nevertheless, these techniques are restricted in their ability to capture the bidirectional temporal and spatial relationships among the data. To remedy these shortcomings, this study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN.Design/methodology/approachThis study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN. The novelty of BiLSTM-1DCNN comes in its effective fusion of 1DCNN’s local feature extraction and BiLSTM’s capacity to capture long-term information from both directions (forward and backward).FindingsValidation using a dataset from the Nam O steel truss bridge demonstrates that the proposed method surpasses conventional models, including 1DCNN (87.9%), LSTM (72.5%), BiLSTM (76.1%) and LSTM-1DCNN (91.1%), achieving an accuracy rate of 96.4%. This approach provides a significant advancement in SHM, paving the way for more accurate and reliable damage detection.Research limitations/implicationsThe study’s limitations include potential overfitting due to the model’s complexity and the high computational resources required. The BiLSTM-1DCNN model’s performance is also influenced by the quality and quantity of the training data as well as the specific characteristics of the dataset. Additionally, while the model performs well in detecting damage in the Nam O steel truss bridge, its generalizability to other types of structures and environmental conditions remains untested.Practical implicationsThe BiLSTM-1DCNN model offers significant practical benefits for structural health monitoring by enhancing damage detection accuracy. Its ability to effectively capture both local features and long-term temporal dependencies makes it a valuable tool for real-time monitoring and early detection of structural issues. This can lead to more timely maintenance actions, potentially reducing repair costs and improving safety. The model’s high accuracy in detecting damage can also support infrastructure management decisions, contributing to better resource allocation and enhanced overall safety of critical structures.Social implicationsThe BiLSTM-1DCNN model’s advanced damage detection capabilities have positive social implications, such as improving the safety and reliability of public infrastructure like bridges. Enhanced structural health monitoring can reduce the risk of catastrophic failures, protecting lives and reducing accidents. By providing more accurate and timely assessments, the model can also lead to more efficient use of maintenance and repairs, ensuring that resources are allocated effectively.Originality/valueThe originality of the BiLSTM-1DCNN model lies in its innovative integration of one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks. This unique combination leverages 1DCNN’s strength in local feature extraction and BiLSTM’s capacity to capture temporal dependencies in both directions, addressing the limitations of existing methods. The model’s high accuracy in detecting structural damage, as demonstrated by its performance on the Nam O steel truss bridge dataset, highlights its value in advancing structural health monitoring technologies and offers a significant improvement over traditional approaches.

The application of MEMS and nanotechnology (MNT) to the field of structural health monitoring (SHM) is a fairly recent development. The recent change in this focus for MNT has been driven by the need to expand the applications for much of the technologies that were developed in the late 1990s. In addition, many companies desire to expand beyond their target high volume market segments of automotive, wireless communications, and computer peripherals, since these market segments were not as lucrative as first predicted. Most of the aerospace structural health monitoring developmental activity has been sponsored by agencies of the U.S. Government, which serves to pace the examination of these newer technologies to some degree. With that said, efforts are underway by companies such as Acellent Technologies and Blue Road Research to explore various MNT structural health monitoring approaches. The MNT under test include embedded piezoelectric sensors, MEMS accelerometers, time domain region sensors, and topical and embedded single and multi-axis fiber optic Bragg grating sensors. The promise of MNT for the SHM market segment is very enticing. The many wireless communication developments and miniaturization developments of the past five years is very attractive to the SHM community, especially those that are able to reduce the cost and complexity of integration. The main challenge for the community is one of selective integration. That is, certain pieces may be appropriate for SHM systems and certain pieces may not be. The better companies will chose wisely and put forth an approach that can be seamlessly integrated into the larger structure. For over a decade, Blue Road Research has been developing technologies aimed at structural health monitoring of both composite and non-composite parts, through the use of single and multiaxis fiber optic Bragg grating sensors. These sensors are 80 to 120 microns in diameter making them smaller than the diameter of a human hair. Multiaxis fiber optic sensors are able to measure pressure, temperature, axial and transverse strain, chemical properties, corrosion, as well as transverse strain gradients. This technology is easily embedded in between the various layers of the composite structure, during manufacture, without compromising the structural integrity, in order to verify manufacturing parameters during the cure cycle and well as monitor the on-going condition of the composite structure throughout its life time. This paper reviews some of the technical work that has been accomplished during the past two years; specifically the embedding of fiber optic sensors into various composite structures in order to be able to conduct in situ non-destructive evaluation of the curing process and the service life of the component. The fiber optic technology has been developed to the point that it is at a TRL of 6.

With the continuous advancement of infrastructure development, ensuring the health and safety of existing structures has become increasingly critical. Structural Health Monitoring (SHM) plays a vital role in assessing the integrity and longevity of buildings, bridges, and other infrastructures. However, traditional monitoring methods often face challenges related to efficiency, accuracy, and cost-effectiveness. The rapid development of Artificial Intelligence (AI) has opened new opportunities for SHM, offering intelligent, automated, and highly precise monitoring solutions. By integrating AI with SHM, it is possible to enhance detection accuracy, improve predictive maintenance, and significantly reduce operational costs. This paper aims to provide a comprehensive summary of AI technologies that have been successfully applied in the field of SHM. By reviewing current advancements, we seek to contribute to the ongoing development of intelligent monitoring systems and offer new perspectives on the future integration of AI in SHM.

Health monitoring has, over the last 2-3 decades, become a topic of significant interest within the structural engineering research community, but also in the broader areas of civil and mechanical engineering. Whereas the merits of Structural Health Monitoring (SHM) are generally appreciated in qualitative terms there has, as of yet, not been reported much fundamental research on the quantification of the benefit of SHM. The present paper addresses the quantification of SHM with basis in the Bayesian pre-posterior decision analysis. The starting points are historical and recent developments in the fields of SHM and the quantification of value of information as well as the identification of typical situations in structural engineering in which SHM has the potential to provide value beyond its costs. Subsequently, the theoretical framework which allows for the quantification of the value of information collected through SHM systems is developed and elaborated. It is shown how the value of information can be quantified to support the assessment and optimization of decisions on whether and how to implement SHM. To illustrate the use of the developed theoretical framework for evaluating the benefit of SHM an example is provided. The example addresses the life-cycle benefit maximization for offshore jacket structures subject to fatigue crack growth utilizing monitoring of near field fatigue stresses as a means of optimizing risk based inspection and maintenance strategies.

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.

The concept of wireless sensor nodes and sensor networks has been widely investigated within the field of structural health monitoring (SHM). However, the ability to power sensors, onboard processing, and telemetry components is a significant challenge in many SHM applications. Several energy harvesting techniques, based on piezoelectric and thermoelectric materials, have been proposed and studied to solve such problems. This article summarizes recent advances and research issues in energy harvesting relevant to SHM applications. We also present a feasibility study for using radio frequency (RF) signals to wirelessly deliver energy to power sensing systems. The goal of this study is to provide a method for supplying power to a sensor node periodically so that the SHM system does not rely on an onboard power source.

Structural health monitoring (SHM) includes both static and highly dynamic engineering systems. With the advent of real-time sensing, edge-computing, and high-bandwidth computer memory, there is an ability to enable high-rate SHM (HR-SHM). The paper defines the technical area of high-rate structural health monitoring and prognostics and presents the HR-SHM technical grand challenges including multi-timescales of the problem, adequate sensor network and response, real-time assessment, and decision-making with quantified uncertainty and risk. Key issues to address in such challenges include the time duration of the event, timescales of the physics, multiple sources of uncertainty, as well as limited spatiotemporal constraints for hardware execution. The paper defines the high-rate timescale as 1 ms on the integrated paradigm including data acquisition, assessment execution, and decision-making. The spatial issues include the resolution of the area monitored, the communication distance, and the number of edge sensors. The temporal issue includes the sensor type (e.g., THz) as well as multiple sources of uncertainty. These constraints must be coupled to allow for high-rate implementation that is robust, adaptable, and beneficial to the missions of interest. To address the grand challenge, we propose physics-informed real-time fusion (PIRF) of high-speed dynamic data. Technologies such as machine learning and edge-computing can be further harnessed to enable structural and functional prognostics for high-rate dynamic systems. Quantification of uncertainty, both aleatory and epistemic, is necessary for real-time state estimation to be connected with the confidences to integrate risks into the decision-making.

Autoregressive coefficients based Hotelling’s T2 control chart for structural health monitoring

The fundamental shear horizontal (SH0) wave in plates is of great importance in the field of nondestructive testing (NDT) and structural health monitoring (SHM) since it is the unique non-dispersive guided wave mode. For practical applications, a phased array system based on omnidirectional SH0 wave transducers is most useful as it can cover a wide range of a plate. However, so far very few omnidirectional SH wave transducers have been developed. In this work, we proposed an omnidirectional SH wave piezoelectric transducer (OSH-PT) based on a thickness poled piezoelectric ring. The ring is equally divided into twelve sectors and the electric field is circumferentially applied, resulting in a new thickness-shear (d15) mode. Finite element analysis shows that the proposed OSH-PT can excite single-mode SH0 wave and receive the SH0 wave only. Experiments were then conducted to examine the performance of the proposed OSH-PT. Results indicated that it can generate and receive single-mode SH0 wave in a wide frequency range with nearly uniform sensitivities along all directions. Considering its quite simple configuration, compact size and low cost, the proposed OSH-PT is expected to greatly promote the applications of SH waves in the field of NDT and SHM.

Vibration-based damage detection approaches have received considerable attention in the field of structural health monitoring. Modal parameters are often adopted to define damage-sensitive features, since their strong physical meaning can help interpreting the structural condition. On the other hand, they are also sensitive to changes to environmental and operational conditions. This aspect is critical for an automatic damage detection, because changes in modal parameters due to varying external factors (e.g. temperature) can be greater than those caused by damage.In this context, this paper proposes an application to real data coming from long-term structural health monitoring of axially-loaded beam-like structures under realistic environment. These very common structural elements are usually subject to an axial load that changes under environmental and operating conditions. Since the axial load is not directly known in most real applications, assessing damage using modal-based damage features is a complicated task.In this paper, a data driven approach that does not require a knowledge of the axial load is proposed to filter out the environmental effects on modal-based damage features. The strategy has been successfully tested on data acquired in an uncontrolled environment, and resulted in being a promising solution for real structural health monitoring applications.KeywordsLong-term monitoringDamage detectionEnvironmental and operational variationsVibration-based featureStatistical pattern recognitionBeam-like structureTie-rod