SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge

The computational and wireless communication capabilities of smart sensors densely distributed over structures can provide rich information for structural monitoring. While smart sensor technology has seen substantial advances during recent years, interdisciplinary efforts to address issues in sensors, networks, and application specific algorithms are needed to realize their potential. This paper first discusses each of these issues, and then reports on research that combines the results to develop a structural health monitoring (SHM) system suitable for implementation on a network of smart sensors. Experimental verification is provided using Intel's Imote2 smart sensors installed on a threedimensional truss structure. The Imote2 is employed herein because it has the high computational and wireless communication performance required for advanced SHM applications. This SHM system is then investigated from sensing, network, and SHM algorithm perspectives.

Near real-time tracking of dynamic properties for standalone structural health monitoring systems

Structural Health Monitoring (SHM) systems are widely regarded as capable of significantly reducing inspection costs of safety-critical structures in industries such as aerospace, nuclear, and oil and gas, among others. Successful SHM systems can be considered those which combine good sensitivity to defects, preferably with the capability of localization and identification, with a low sensor density. Techniques based on sparse arrays of sensors which generate and receive guided waves are among the most promising candidates. Guided waves propagate over large distances and certain modes have the ability to transmit through a variety of structural features leading to a relatively small number of distributed sensors being able to cover the structure. In complex structures, which contain high densities of structural elements, the timetraces obtained are often too complex to be directly interpreted due to the large number of overlapping reflections. In this case, the Baseline Subtraction technique becomes attractive. In this method a current signal from the structure is subtracted from a signal which has been acquired during the initial stages of operation of the structure. This eliminates the need for interpretation of the complex raw time signal and any defects will be clearly seen provided the amplitude of the residual signal obtained after subtraction of the baseline signal is sufficiently low when the structure is undamaged. However, it is well known that environmental effects such as stress, ambient temperature variations and liquid loading affect the velocity of guided waves; this modifies the time-traces and leads to high levels of residual signal if a single baseline, taken under different conditions, is used. Of these effects, temperature variations are the most commonly encountered and are critical since they affect not only the wave propagation but also the response of transducers. The present work aims to demonstrate the potential of guided wave health monitoring of large area complex structures. It starts with a general literature review on inspection and monitoring of large area structures, in which the advantages and disadvantages of this technique compared to other well-established SHM techniques 2 are presented. The design and behaviour of two different temperature-stable transducers generating high A0 or S0 mode purity in the sub-200kHz frequency region are described. The efficiency of different signal processing techniques aimed at reducing or eliminating the influence of temperature on wave propagation is evaluated and a temperature compensation signal processing strategy is proposed. Finally, a large metallic structure is used to demonstrate a sparse-array SHM system based on this signal processing strategy, and imaging algorithms are used to combine the information from a large number of sensor combinations, ultimately leading to the localization of defects artificially introduced in the structure.

The management and maintenance of cable-stayed bridges represents a major investment of human and financial capital. One possible approach to reducing the cost while simultaneously improving the process is by utilizing structural health monitoring (SHM) systems to enable diagnostic load tests to be regularly and efficiently conducted. The Indian River Inlet Bridge (IRIB), a 533-meter long cable stayed bridge, was opened for traffic in 2012. From the very early stages of the design process, the Center for Innovative Bridge Engineering (CIBrE) at the University of Delaware (UD) worked with the Delaware Department of Transportation (DelDOT) and their design-build team of Skanska and AECOM to plan and install a comprehensive structural health monitoring (SHM) system. The SHM system is a fiber-optic based design with more than 120 sensors of varying type distributed throughout the bridge. The system, which not only collects data continuously during normal operation, has also been utilized during regularly scheduled controlled diagnostic load tests being used to monitor ongoing bridge performance. This paper will present results from a series of six diagnostic load tests which have been performed over the first six years of the bridge’s service life (just prior to the bridge’s opening, and then again at six months, one year, two years, four years, and six years). The results of these six load tests have enabled the bridge’s baseline performance to be established. They have also provided an opportunity to develop a process for comparing future biennial tests to the evolving database of results, thereby enhancing DelDOT’s ability to operate and maintain the bridge.

In Japan, structural health monitoring (SHM) for building structures has received increased attention since the 2011 Tohoku earthquake, particularly regarding business continuity and functional recovery. Most applications are market-driven, and building owners deploy an SHM system at their own expense so that they can promptly assess the condition of their buildings as one of the tags denoted “Green,”“Yellow,” or “Red.” In 2015, the authors’ group began installing such an SHM system, and as of December 2023, 553 privately owned buildings have been equipped with this SHM system. This opinion paper presents the authors’ experiences to date on monitoring, utility of data, and interaction with building owners. It conveys three opinions that the authors believe to be most interesting, particularly in the context of the benefits of the “Caution (Yellow)” tag. The article briefly introduces SHM, followed by the specific system in question and the data accumulated over the years in Japan. The first opinion is that the damage criteria, that is, the boundaries among the three tags, should not be considered permanent but subject to updates based on experience and newly acquired data. The second opinion is that SHM is one of the best means to characterize the damage (and fragility) of major nonstructural elements, while “Caution (Yellow)” has inherent relevance to the nonstructural damage, particularly for initial damage states. The third opinion is that “Caution (Yellow)” is a good message for building owners and managers with a solid contingency plan that includes readily available building maintenance workers and engineers. On the contrary, it tends to frighten building owners and managers when such a contingency plan is absent, discouraging them from installing SHM. Overall, it presents some valuable insights of interest to those wishing to further such SHM systems on a broader scale.

Composite aircraft structures commonly use bonded joints to adhere the wing and fuselage skins to the wing spars, stringers, ribs, and frames. Unfortunately the bond out-ofplane normal (peel) strength is many orders of magnitude less than the fiber strength of the adjacent laminates. Moreover, the adhesive failure is brittle in nature so that it is difficult to monitor an impending overload or fatigue failure. In order to avoid failures due to interlaminar damage without using high margins of safety, a Structural Health Monitoring (SHM) system can be used. While extensive literature on SHM systems for fiber-driven damage exists, there is no mention of an SHM system that specifically addresses these types of matrix-driven damage. The proposed SHM system spans the acquisition, location, reconstruction, and damage type decision of Acoustic Emission (AE) damage signals. An algorithm is presented to calculate the location of AE damage signals in a laminated composite structure using an over-determined approach to minimize location errors. Also, a novel procedure is presented to simultaneously reconstruct the loading source time series for a recorded damage signal and identify the source type. Parametric studies are presented for both algorithms to gauge their effectiveness under varying levels of noise, number of sensors, material properties and different types of uncertainty. Two experimental studies were undertaken to exercise the SHM algorithm presented in this study. The first experiment models a predominantly single type quasi-static damage case, and the second experiment studies a mixed damage impact case.

Primary and secondary aircraft structures are commonly made from laminated composite materials. Unfortunately, the interlaminar tensile and shear strengths are many orders of magnitude less than the in-plane strengths because they are driven by the strength of the matrix as opposed to that of the fibers. Moreover, the matrix strength is brittle in nature so that it is difficult to monitor an impending overload or fatigue failure. In order to avoid failures due to interlaminar damage without using high margins of safety, a Structural Health Monitoring (SHM) system can be used. While extensive literature on SHM systems for fiber-driven damage exists, there is no mention of an SHM system that specifically addresses these types of matrix-driven damage. The proposed SHM system spans the acquisition, location, reconstruction, and damage type decision of Acoustic Emission (AE) damage signals. An algorithm is presented to calculate the location of AE damage signals in a laminated composite structure using an over-determined approach to minimize location errors. Also, a novel procedure is presented to simultaneously reconstruct the loading source time series for a recorded damage signal and identify the source type. An example analytical study is presented to calculate the effects of uncertainty on the location and damage type predictions. Two experimental studies were undertaken to exercise the SHM algorithm presented in this study. The first experiment studies a mixed damage blunt impact case on a flat composite plate for different impact energies. The second experiment studies blunt impact of a reinforced composite plate that models a composite wing skin with bonded stringers and ribs.

The continuous Non-Destructive Evaluation of assets for long-term assurance of performance has led to several developments over the deployment of a Real-Time Structural Health Monitoring (SHM) system. Considering the challenges involved under the implementation of an SHM system for the applications working under harsh environmental conditions with limited access to power sources this work is aimed to contribute towards overcoming those challenges by using the noise from the structure’s machinery or any ambient source as an alternative energy source and employing Fiber Optics based sensing, for its applicability under harsh environments. The required SHM system is realized with the cross-correlation of a fully diffused noise field, sensed using the Fiber Bragg Grating (FBG) sensors at two random locations. With no control on the input received as noise, to this end, a method is developed based on a Deep Learning framework, which is aimed towards a Universal Deployment of the passive SHM system. The methodology is designed to perform the health monitoring of the system, independent of the input perturbations. The validation performed on simulation data has demonstrated the feasibility of the developed technique towards the required kind of passive SHM system.

In order to avoid the safety problems of the public building structure in the dense passenger flow environment, the structural health monitoring system is combined with the Building Information Modeling (BIM) method. This paper proposed a dynamic building information management system based on Structural Health Monitoring (SHM) information to ensure the security of its passenger flow information for a crowded exhibition hall. The continuous development of BIM (building information modeling) technology has greatly promoted the development of the construction industry. We can achieve real-time updating and visualization of sensor monitoring data and effectively manage different types of monitoring information as well as improve the controllability and safety of building structure monitoring by introducing BIM technology into structural health monitoring. A static building information model (BIM) was established to generate dynamic passenger flow information through video data learning, and provide a basis for structural health monitoring system for structural safety analysis and emergency response functions under dense passenger flow conditions. A dynamic building information management system suitable for structural health monitoring was built to display the dynamic network of traffic capacity in real time, so that the building information model is dynamic and predictable. The establishment for the combination mechanism of BIM and SHM system improved the data information circulation of BIM system. It could carry out dynamic health monitoring management and early warning control of building structures, realize information sharing in the process of health monitoring, and effectively improve the safety and operational efficiency of public building structures.

Structural health monitoring (SHM) systems collect and analyze data recorded from civil infrastructure to detect changes in the structural condition and to predict potential damage. However, sensor faults in SHM systems may lead to erroneous structural assessments, thus to inefficient maintenance operations, which could jeopardize the integrity of the structures. Artificial intelligence (AI) algorithms based on analytical redundancy have proven being effective foundations to implement sensor fault diagnosis but are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. Moreover, operating with centralized servers increases the likelihood of single points of failure, which compromises the robustness of SHM systems. To overcome the limitations and to enhance the robustness of SHM systems, this paper proposes a decentralized approach towards fault-tolerant SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Fault diagnosis algorithms based on AI are embedded into sensor nodes deployed for SHM, using Internet of Things technologies to be computed at the edge of civil infrastructure, avoiding large amounts of data to be sent to central servers. In addition, the sensor nodes embed algorithms that collect, process, and analyze the sensor data to assess the condition of the structures being monitored. The result is a smart (i.e., decentralized, fault-tolerant, and AIoT-based) SHM system that is calibrated in a laboratory experiment and then deployed on a bridge for field validation. It is expected that the approach presented in this paper will improve the robustness of SHM systems in assessing the structural condition of civil infrastructure.

Structure damage modelling for the simulation of elastic wave propagation is essential for simulation-based testing of structural health monitoring (SHM) systems. Reflection, transmission and other complex phenomena related to elastic wave interaction with structure defects need to be reproduced by the simulation method to replace physical testing by numerical one and aid the design process of SHM system. Authors review commonly used methods for the simulation of elastic wave propagation pointing out their advantages and disadvantages. Subsequently methods for crack and delamination modelling are presented. Proposed models are used for numerical testing of designed SHM system. Simulation results are compared with experimental ones, showing very good agreement. Finally, structure damage models are used for testing delamination localization algorithm.

This article presents a simple and versatile measurement system for structural health monitoring (SHM) based on the electromechanical impedance (EMI) technique. The proposed system allows real-time data acquisition from multiple sensors and includes compensation for temperature effects, eliminating one cause of incorrect diagnoses in structural monitoring. Besides the low cost compared to conventional impedance analyzers, the hardware and the software are simple and easier to implement than other measurement systems that have been recently proposed. Tests were carried out on an aluminum beam under different temperatures and on an aluminum plate with nine PZT (lead zirconate titanate) patches. The experimental results show conclusively that the proposed methodology is efficient and feasible for real-time SHM.

Fault diagnosis (FD), comprising fault detection, isolation, identification and accommodation, enables structural health monitoring (SHM) systems to operate reliably by allowing timely rectification of sensor faults that may cause data corruption or loss. Although sensor fault identification is scarce in FD of SHM systems, recent FD methods have included fault identification assuming one sensor fault at a time. However, real-world SHM systems may include combined faults that simultaneously affect individual sensors. This paper presents a methodology for identifying combined sensor faults occurring simultaneously in individual sensors. To improve the quality of FD and comprehend the causes leading to sensor faults, the identification of combined sensor faults (ICSF) methodology is based on a formal classification of the types of combined sensor faults. Specifically, the ICSF methodology builds upon long short-term memory (LSTM) networks, i.e. a type of recurrent neural networks, used for classifying ‘sequences’, such as sets of acceleration measurements. The ICSF methodology is validated using real-world acceleration measurements from an SHM system installed on a bridge, demonstrating the capability of the LSTM networks in identifying combined sensor faults, thus improving the quality of FD in SHM systems. Future research aims to decentralize the ICSF methodology and to reformulate the classification models in a mathematical form with an explanation interface, using explainable artificial intelligence, for increased transparency.

Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network

In Japan, structural health monitoring (SHM) of building structures began in the 1950s, but, until recently, its widespread use was not realized. A new trend arrived a few years ago, and currently over 850 buildings have SHM systems installed. The most recent SHM systems have been installed voluntarily by owners in the private sector; that is, the major development of recent Japanese SHM has been based on market forces. This article reports on why SHM was not accepted widely in the past, what were the keys for change of the atmosphere, how the building owners evaluate SHM after it is deployed, and what tangible benefits the building owners realize by experience on SHM implementation. To investigate those, an SHM system named q-NAVI is introduced as an example. The system has been deployed for 450 buildings, and they experienced a few significant shakings from recent earthquakes. SHM is also found effective for acquiring information on the quantification of fragility curves for various nonstructural components, using the data samples collected in recent earthquakes.