A novel pro-active approach towards SHM-based bridge management supported by FE analysis and Bayesian methods

Inspection strategies with guided wave‐based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h‐NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.

Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks

The increasing use of composite materials in aerospace, automotive, and civil engineering demands robust approaches for structural health monitoring (SHM) and failure prediction. Due to their high strength-to-weight ratio and corrosion resistance, fiberreinforced polymers (FRPs) are essential in modern lightweight structures such as unmanned aerial vehicles (UAVs) [1]. However, they remain prone to fiber breakage, matrix cracking, and interlaminar delamination, which are often difficult to detect visually, making real-time monitoring critical for safety. This study presents a structural analysis of a carbon-fiber UAV wing using finite element analysis (FEA) in ANSYS to evaluate stress distributions, deformations, and failure modes under various loading scenarios, including progressive damage and fatigue. The Hashin and Tsai–Wu criteria were implemented to differentiate fiber- and matrix-dominated failures, including interlaminar fracture. Numerical results were compared with experimental data from the literature [5] to validate the approach. These results support the integration of FEA with SHM systems using Fiber Bragg Grating (FBG) sensors and Digital Twins for lightweight, sustainable composite structures in aerospace and civil infrastructure.

The importance of developing robust monitoring systems that can detect and locate progressive deterioration in structures or abrupt damage induced by extreme loading events is well recognized in the aerospace, mechanical, and civil engineering communities. In the case of civil structures such as buildings, bridges, off-shore platforms, or dams, the most commonly used approach for structural health monitoring ~SHM! relies on visual inspections that are labor intensive, often not timely, and may miss hidden damage. A good example of these limitations is found in the fractures induced by the 1994 Northridge earthquake in welded momentresisting beam-column connections in many steel buildings that were not discovered until several months after the earthquake because there was no distress apparent from visual inspections. An alternative to visual inspections and localized testing that is being aggressively investigated in various engineering disciplines is that of structural health monitoring based on vibration signals. A typical SHM system consists of a distributed set of motion sensors ~e.g., accelerometers, velocimeters, or fiber-optic strain gauges! connected through a data acquisition system to a central processing unit that may be in an operator room. For remote real-time monitoring of structures, the processed data must be on-line, possibly through a dedicated line or via an Internet link. The basic idea is well established, namely, the dynamic characteristics of a system ~usually natural frequencies and mode shapes! that can be identified from recorded motions are a function of the physical parameters ~mass and stiffness distributions!. Therefore, changes in physical properties resulting from damage may be inferred from changes in the identified dynamic characteristics using suitable algorithms. Then, in theory, damage may be detected, localized, and assessed through vibration monitoring. In practice, however, realization of the concept has proved to be a very challenging proposition because the inverse problem that must be solved, for typical operating conditions, is poorly conditioned, that is, results are sensitive to measurement noise and modeling errors. An important part of the current effort to affect positive progress in SHM technology in civil engineering structures is the development of well-defined benchmark problems that allow comparison of the performance of various techniques for realistic conditions. This special issue of the Journal of Engineering Mechanics contains eight papers that define and study a benchmark developed by the Task Group on Structural Health Monitoring that was formed in 1999 under the sponsorship of the Dynamics Committee of ASCE and the International Association of Structural Control ~IASC!. These papers expand on some partial and

A wavelet-based approach is proposed for structural damage detection and health monitoring. Characteristics of representative vibration signals under the wavelet transformation are examined. The methodology is then applied to simulation data generated from a simple structural model subjected to a harmonic excitation. The model consists of multiple breakable springs, some of which may suffer irreversible damage when the response exceeds a threshold value or the number of cycles of motion is accumulated beyond their fatigue life. In cases of either abrupt or accumulative damages, occurrence of damage and the moment when it occurs can be clearly determined in the details of the wavelet decomposition of these data. Similar results are observed for the real acceleration data of the seismic response recorded on the roof of a building during the 1971 San Fernando earthquake. Effects of noise intensity and damage severity are investigated and presented by a detectability map. Results show the great promise of the wavelet approach for damage detection and structural health monitoring.

The BeiDou navigation satellite system (BDS) provides precise positioning, navigation, and timing (PNT) services in the Asia-Pacific Region, but the BDS-based structural health monitoring (SHM) approach (SHM) is rarely studied, especially in civil engineering. Moreover, how BDS can be applied to complete the tasks of SHM in a real project is also not fully investigated, especially working in conjunction with other techniques. This study aims to propose a BDS-based approach for SHM in civil engineering. The performance of the proposed approach is investigated through a case study—the Tianhan Grand Theater (TGT). A specific Tianhan system corresponding to BDS is proposed to complete the SHM tasks of TGT. Based on the collected data, the trusses with maximum displacement and stress are found by BDS to evaluate structural health in the construction stage. The results show that the maximum displacement and stress have certain safety reserves and meet the requirements of the specifications and regulations. Thus, BDS can satisfactorily complete the tasks of SHM for Long-span steel structures. This study gives a clear view to engineers and researchers that how to apply BDS in structural construction and provides a valuable real case for evaluating the performance of BDS in SHM.

The present paper takes place in the context of identification problems: the aim is to propose a new approach for the detection of damages within large structures via the deployment of a set of moving sensors. The goal of the method is to optimally control the motion of the swarm driving the sensors in the optimal measurement regions, in order to perceive the field as clean as possible and to enhance the identification of any possible irregularity along the structure. The optimal control of the sensors motion and the consequent damage identification are achieved through a two-step strategy. The first one is based on: i) models for the dynamics of the structure, for the dynamics of the sensors; ii) LQR control logic to define the optimal trajectory of the swarm. The second step includes the identification of the damage module: the process of data analysis is investigated through use of the technique based on Empirical Mode Decomposition, combined with the Hilbert-Huang Transform.KeywordsStructural Health MonitoringMoving SensorsOptimal ControlEmpirical Mode DecompositionHilbert-Huang Transform

In this work, we report on some research developments to support the upscaling of nondestructive evaluation and structural health monitoring (SHM) approaches, mainly based on ultrasonic guided waves, for aeronautical applications. In particular, first a spiral-shaped piezoelectric sensor sensitive to the wave direction of arrival (WDA) of guided waves propagating in plate-like structures is described. Next, a miniaturized sensor node capable to perform locally signal processing algorithms, as those developed for the spiral sensors, is presented. The sensor node weights 4 g, measures approximately 20 × 24 mm, is characterized by a low power consumption and exploiting a data-over-power (DoP) network communication reduces to a minimum the need for cabling. At such the spiral sensors and sensor node result to be suitable for the upscaling of SHM approaches devoted to locate impacts as well as delaminations in aircrafts fuselage and wings. Next, impact and/or damage coordinates are passed to an augmented reality (AR) framework that allows the NDT inspector, if equipped with a proper visual device, to see in real time directly on the structure the position of the impact/damage. In the end, by guiding the technician directly to spots potentially damaged this could speed up the NDT visual inspection phase as well as it should reduce the possibility that damages would go undetected. Applications of the above methodologies/technologies for impact localization on an aluminum plate are presented.

The authors have been developing a new tiny (~12 mm cube and <4 g) Bluetooth wireless accelerometer unit based upon the technology that is prevalent in modern Smart Phones and Smart Watches. The primary intended application of this unit is for training/teaching purposes in vibration-based Experimental Modal Analysis (EMA) approaches for Structural System Identification (SSI) and Structural Health Monitoring (SHM). Digitised tri-axial acceleration data from a single unit can be streamed using purpose-specific software on a notebook or tablet at up to 400 Hz where the longest clear distance of this unit from the streaming device, (smart phone, tablet, laptop computer), is less than approx. 15 m. It is anticipated that tri-axial data synchronously captured from up to 8 accelerometer units at a sweep rate of 100 Hz per set, can be achieved on completion of the second stage of development of this system. This will certainly be suitable for use on model structures for teaching purposes, but also for industrial use (machinery; on-board vehicles; smaller structures) where the clear distance from the computer (or tablet) to these units is less than approx. 15 m. This paper focuses on the performance testing of a collection of commercially available accelerometers and compares these against the capabilities being aimed for in our new miniature accelerometer units.

The Lamb wave approach in Structural Health Monitoring (SHM) mainly cares the defects detection. It relies on the analysis of signals for physical propagation. In this paper, a sparse representation approach for modeling the signal scattering in discretized monitoring area is proposed. The overcomplete dictionary can be determined explicitly by the Lamb wave propagation theory with columns reflecting the signals at time instant. Through introducing the dictionary learning method, the signal scattering from defects can be formed as a sparse coding problem with the learnt dictionary from the given data. It has the potential to conquer the complex structure condition for thin-wall plate. The experiments illustrates the effectiveness of the presented approach.

The structures damage conditions assessment requires numerous precautions to ensure the safety of people during site visits and inspections. Among several methods providing useful information about the conservation status of the structures, dynamic monitoring techniques are suitable to retrieve the global behavior of the buildings. The anomalous features diagnosis of the structural dynamic response is an index of alterations of the material state and, in the worst cases, is related to the presence of damaged structural elements. This paper proposes the use of remote control systems for the structural evaluation of the damage state of buildings and describes the results achieved in an interesting application: the experimental dynamic analysis carried out on the inaccessible damaged bell tower of the Church of Santi Giacomo and Filippo in Mirandola (Italy). The study is based on observations performed using the IBIS-S ground-based radar interferometer to remotely measure the displacements of several elements of the building above 0.01 mm amplitude. This totally noninvasive and nondestructive approach has proved to be reliably implemented as a useful method to structural health monitoring procedures and especially for extensive and fast inspection analyses aiming at the first evaluation of the damage level and the soundness of slender buildings after earthquakes.

Floating covers used in waste water treatment plants are one of the many structures formed with membrane materials. These structures are usually large and can spread over an area measuring 470 m × 170 m. The aim of this paper is to describe recent work to develop an innovative and effective approach for structural health monitoring (SHM) of such large membrane-like infrastructure. This paper will propose a potentially cost-effective non-contact approach for full-field strain and stress mapping using an unmanned aerial vehicle (UAV) mounted with a digital camera and a global positioning system (GPS) tracker. The aim is to use the images acquired by the UAV to define the geometry of the floating cover using photogrammetry. In this manner, any changes in the geometry of the floating cover due to forces acting beneath resulting from its deployment and usage can be determined. The time-scale for these changes is in terms of weeks and months. The change in the geometry can be implemented as input conditions to a finite element model (FEM) for stress prediction. This will facilitate the determination of the state of distress of the floating cover. This paper investigates the possibility of using data recorded from a UAV to predict the strain level and assess the health of such structures. An investigation was first conducted on a laboratory sized membrane structure instrumented with strain gauges for comparison against strains, which were computed from 3D scans of the membrane geometry. Upon validating the technique in the laboratory, it was applied to a more realistic scenario: an outdoor test membrane structure and capable UAV were constructed to see if the shape of the membrane could be computed. The membrane displacements were then used to calculate the membrane stress and strain, state demonstrating a new way to perform structural health monitoring on membrane structures.

Acoustic emission (AE) is a phenomenon that occurs in materials that are exposed to stress. Defects in the material, such as cracks can grow and release energy in the form of a mechanical wave that propagates through the material. With the help of piezoelectric sensors, the mechanical wave is converted into an electrical signal. The signals are recorded by the measuring system and are available for further processing. Acoustic emission as a method that records and processes the incoming signals in real time is therefore very suitable for 24/7 long-term structural health monitoring. With new approaches in data processing, acoustic emission can be moved from the classic periodic test approach to an online monitoring process. The in-service monitoring brings measuring challenges with it, like application on hot surfaces and inside explosion hazard areas. New developments in hardware and software provide tailor-made solutions for a variety of monitoring tasks. The use of artificial intelligence algorithms helps to improve the analysis of measurement data and guarantees safe operation of pressure equipment and infrastructure around-the-clock. Some examples of continuous monitoring are shown on pressure vessels from the industry sector and on infrastructure buildings e.g., steel bridges. Also, a new lean system approach in structural health monitoring with acoustic emission is presented.

Vibration-based approach for structural health monitoring of ultra-high-performance concrete bridge

This study presents an approach for structural health monitoring (SHM) of remote and hazardous structures using unpiloted aerial vehicles (UAVs). The method focuses on overcoming the challenges associated with traditional sensor deployment techniques, which are often costly and risky due to the decaying nature of the targeted structures. Utilizing a multi-rotor UAV platform, a streaming camera is integrated into a recovery cone to aid in visual alignment during deployment and retrieval providing a safe and cost-effective means of sensor delivery. The paper covers the design of a video-broadcasting deployment system with integrated electropermanent magnets (EPMs), housed in a 3D-printed recovery cone, supplemented by redundancy measures to enhance safety and reliability. This proposed system significantly improves the user’s spatial awareness and aids in precise sensor package alignment, facilitated by multiple camera views providing a dual purpose of conducting visual inspection in addition to aiding in sensor delivery. The experimental analysis presented in this study validates the system’s effectiveness, demonstrating the utility of camera-aided sensor delivery for rapid SHM applications. Navigation challenges due to proximity to metal structures and the difficulties associated with signal strength and reflections are also reported. The contribution of this work is a methodology for aerial sensor deployment and retrieval using a lightweight 3D-printed recovery cone with integrated cameras for navigation and sensor alignment.