bluuebunny/crossref_metadata_2025 huggingface dataset:
Vibration Monitoring of the Rock Formation Above the Hatshepsut Temple
Authors:   Technical University of Munich (TUM), Mendler Alexander ,   Technical University of Munich (TUM), Rada Erazo Juan David ,   Cairo University, Ismael Mohamed ,   Cairo University, Ezzy Mostafa ,   Technical University of Munich (TUM), Jacobs Benjamin ,   German Research Center for Geoscience (GFZ), Haberland Christian ,   Technical University of Munich (TUM), Krautblatter Michael ,   Cairo University, Helal Hany ,   Technical University of Munich (TUM), Grosse Christian U.
Published: June 2024

The Hatshepsut temple in Egypt is a masterpiece of ancient architecture. Just as distinct as the temple itself is the rock formation it is carved into, and recurring rock falls in the vicinity have raised concerns regarding the temple’s safety under progressing erosion and seismic activity. Due to the material characteristics (brittle carbonate rock), high stresses could lead to rapid crack propagation, and vibra-tion-based measures may be suitable precursors for imminent rock falls. This paper describes a two-day measurement campaign on the rock towers as a preliminary study prior to the implementation of a long-term seismic station. Next to operational modal analysis, horizontal-to-vertical spectral ratios, and standard spectral ratios are evaluated to cross-validate the resonance behaviour. To the author’s knowledge, this is the first study where stochastic subspace identification (SSI) is applied for the estimation and automated tracking of natural frequencies, mode shapes, and damping ratios in rock formations. One of the advantages of this method is the inherent uncertainty quantification, meaning for each vibration record, the mean values and the standard deviations are estimated for each modal parameter, giving deep insights into the reliability of the vibration-based monitoring of rock formations.
Expanding Irreducible Element models: beams and composites
Authors:   Sheffield University, Brennan Daniel S. ,   Queen's University Belfast, Kent Connor ,   Queen's University Belfast, Hester David ,   Sheffield University, Worden Keith ,   Queen's University Belfast, O'Higgins Connor
Published: June 2024

Population-based Structural Health Monitoring's (PBSHM) aspiration is to accumulate a deeper knowledge of a structure's health, by harvesting available data across a range of similar structures (or substructures). Whilst each structure is represented via a unified language called an Irreducible Element (IE) model, the comparison and similarity metrics are computed within a graph space. As such, each model is subsequently converted into an Attributed Graph (AG) before being placed into the PBSHM Framework's comparison graph space; the network.

This paper looks into the application of using the PBSHM Framework's comparison graph space as a complex network and determining if the theories of communities and community structures from network science, can be applied to the PBSHM Framework's network, to determine similarity and define yet unknown populations.
Impact Identification Method for Structural Health Monitoring of Stiffened Composite Panels using Passive Sensing Systems
Authors:   University of Twente, Ribeiro Marinho Natália ,   University of Twente, Loendersloot Richard ,   Royal Netherlands National Aerospace Laboratory (NLR), Grooteman Frank ,   Royal Netherlands National Aerospace Laboratory (NLR), Wiegman Jan Willem ,   University of Twente, Tinga Tiedo
Published: June 2024

Identification of impact and its resulting damage is a complex inverse inferential procedure. Estimating the magnitude of an impact from sensor data is challenging and still needs to be fully understood. Furthermore, only a few studies have solved practical impact identification problems relevant to real-world structures with different geometries and structural inhomogeneities (e.g. variable thicknesses and stiffeners). Accordingly, the current research presents an impact categorisation analysis using passive measurements from piezoelectric (PZT) sensors for a geometrically complex composite aircraft part. Intermediate-mass impact tests were performed on a stiffened thermoplastic composite plate to generate signals representing various impact scenarios that may occur during the lifetime of an aircraft.

The impacts were applied at critical locations, including the mid-bay, on top of the stringer, and at different thicknesses. This study investigated the effect of structural features on wave propagation in composite structures. The experimental results demonstrate that these features significantly change the wave field and, consequently, affect sensor readings. To differentiate between pristine and damaged states, an impact signal categorisation approach was employed. This analysis shows that a passive sensing system can categorise the impact using the transmitted energy from a time-domain signal. However, the spectral energy values showed low efficiency in categorising impacts. Overall, the results highlight the importance of considering structural features when deploying sensor systems for structural health monitoring and provide valuable insights into the effectiveness of impact identification and damage detection in realistic composite structures.
Decision fusion based on vibrometric data for increase of reliability of damage detection and indentification for plate-like structures
Authors:   AGH University of Science and Technology, Dworakowski Ziemowit ,   Air Force Institute of Technology, Kowalczyk Kamil ,   Air Force Institute of Technology, Niedbala Patryk ,   Air Force Institute of Technology, Dziendzikowski Michal
Published: June 2024

In the realm of structural health monitoring (SHM) for plate-like structures, the need for accurate and reliable damage detection and identification techniques is paramount. This paper explores the application of decision fusion algorithms to vibrometric data, with a focus on increasing the reliability of damage assessment for such structures.

The methodology involves leveraging diverse input data sources, including variations in data processing algorithms, excitation parameters, and multiple measurements. By amalgamating these sources of diversity, we aim to mitigate measurement noise and amplify damage indications, ultimately enhancing the precision of the final diagnostic outcome.

The study presents results obtained from laser vibrometry measurements on a 10 mm thick aluminum sample containing flat-bottom holes of varying diameters. A network of 12 PZT transducers was used to excite elastic waves, with impulse and chirp excitation techniques employed. Convolution techniques using OpenCV libraries were applied for data processing and fusion.

The outcomes reveal a significant reduction in background noise, demonstrating the effectiveness of the fusion approach. Furthermore, the presentation outlines a roadmap for the future development of this methodology, which includes automating data processing, evaluating the impact of damage indicators, processing parameters and usage of different excitation transducers.
Bird Strike Test Event Diagnosis Using a Hybrid SHM System
Authors:   Airbus Defence & Space, Military Aircraft, Guerrero Vázquez Santiago ,   Airbus Defence & Space, Military Aircraft, Garcia Alonso Jaime ,   Airbus Defence & Space, Military Aircraft, Guerra González Raúl
Published: June 2024

Several advances have been made in the recent years in the integration of Structural Health Monitoring (SHM) systems within Structural Integrity (SI) management. The current state-of-the-art Airbus Defence and Space (ADS) SHM System relies on both an analytical elastic wave propagation model and a Dynamic Distributed Fibre Optics Sensing (DFOS) Environmental and Operational Conditions (EOC) measurement and compensation model. This hybrid SHM system is able to properly diagnose mechanical impacts (events) and resulting damages that may occur during aircraft operation, while accounting for punctual alterations in the structure due to changes in ambient temperature or pressure. In this paper, a demonstrative application of this system is presented for the diagnosis of a Bird Strike Test Event. The test has been carried out in controlled conditions for a hybrid metal-composite fuselage cockpit structure, within the scope of CS2 PASSARO EU program. The results show the reliability of the ADS SHM system for supervised conditions.
Early detection of structural damage in UHPFRC structures through the combination of acoustic emission and ultrasonic stress wave monitoring
Authors:   Ecole polytechnique fédérale de Lausanne (EPFL), Bertola Numa ,   Ecole polytechnique fédérale de Lausanne (EPFL), Schumacher Thomas ,   BAM Federal Institute for Materials Research and Testing, Niederleithinger Ernst ,   Ecole polytechnique fédérale de Lausanne (EPFL), Brühwiler Eugen
Published: June 2024

Ultra-High-Performance Fiber-Reinforced Cementitious Composite (UHPFRC) offers several advantages compared to concrete, notably due to the strain hardening behavior under tensile actions. Structures made of this composite material are lightweight and highly durable, thanks to the UHPFRC waterproofing quality. Nonetheless, the tensile behavior leads to a different cracking pattern than conventional concrete that is not fully understood yet. This paper presents an innovative monitoring approach that combines passive acoustic emission and active ultrasonic stress wave to localize and quantify damage on a full-scale UHPFRC beam during the loading protocol. This monitoring technique involves 24 transducers that are embedded throughout a UHPFRC t-shape beam of 4.2 meters. Continuous monitoring using this unique passive and active approach enables accurate location events, such as micro-cracking within the beam. Moreover, this sensor network is able to determine the location of the macro-crack that is linked to the structural collapse just after the end of the elastic domain. This novel sensor network opens new possibilities to monitor the structural behavior and detect damage on UHPFRC structures before they affect the structural behavior in terms of deflection and strain.
Economic Impact Assessment of Structural Health Monitoring Systems on the Lifecycle of a Helicopter Blade
Authors:   Politecnico di Milano, Ballarin Pietro ,   Politecnico di Milano, Macchi Marco ,   Politecnico di Milano, Roda Irene ,   Politecnico di Milano, Sala Giuseppe ,   Leonardo Aircraft, Baldi Andrea ,   Politecnico di Milano, Airoldi Alessandro
Published: June 2024

Structural Health Monitoring Systems (SHMS) are currently widely investigated in the literature, however, their application to the aerospace industry is still limited. One of the reasons lies in the lack of methods aimed at evaluating their economic impact on the lifecycle of the structural element to be monitored [1]. In this work the economic impact of FBG-based SHMS was evaluated on a composite helicopter rotor blade, considering two perspectives: Beginning Of Life (BOL), concerning the blade production, and Middle Of Life (MOL), concerning its usage. The processes were modeled using the IDEF0 methodology, which allowed deriving the cost models for two scenarios: the current one, and the one including the SHMS. In BOL, the SHMS was supposed providing support to the development of new composite rotor blades, including the curing cycle definition and certification tests. In MOL the SHMS was supposed performing automatic scheduled inspections coupled with the traditional ones. The cost model was implemented in the Probabilistic Damage Tolerance Analysis [2] to compare the scenarios having the same blade Probability Of Failure and considering the performances of the SHMS in terms of Probability Of Detection and Probability of False Alarm. Results show that an economic benefit may be achieved using the SHMS in the development of new blades, potentially reducing the number of blades tested and number of autoclave cycles. Moreover, it was found that the traditional scheduled detailed inspection intervals could be extended if automatic SHMS inspections are performed in addition, maintaining the same Probability Of Failure.

[1] Büchter, K. D. et al. (2022). Modeling of an aircraft structural health monitoring sensor network for operational impact assessment. Structural Health Monitoring, 21(1), 208-224.
[2] Lin, K. Y., & Styuart, A. V. (2007). Probabilistic approach to damage tolerance design of aircraft composite structures. Journal of Aircraft, 44(4), 1309-1317.
Robotic-based terahertz spectroscopy imaging for non-destructive monitoring of water loss-induced damage in timber materials under low temperature stress
Authors:   University of Bourgogne, Hamdi Seif Eddine ,   University of Bourgogne, Kneip Jean-Christophe ,   University of Bourgogne, Jouvard Jean-Marie ,   ISET Sfax, Hamdi Zied ,   Université Clermont Auvergne, Moutou Pitti Rostand ,   Institut Arts et Métiers de Chalon-sur-Saône, Paillot Damien
Published: June 2024

Timber materials are widely used in various industries, from construction to furniture manufacturing. One of the critical factors affecting the structural integrity and longevity of timber is water loss-induced damage, which often occurs under low-temperature stress conditions. Monitoring and assessing this damage traditionally involve invasive and time-consuming methods. This study explores the application of robotic-based terahertz spectroscopy 3D imaging as a non-destructive and efficient system for assessing the impact of water loss on timber materials under low-temperature stress. The detection and localization of damage in a various species of timber-based structures were experimentally investigated by performing pulsed terahertz time-domain spectroscopy and using a six- DOF (degree-of-freedom) robotic arm. The thickness and damage length were monitored by designing the reflection geometry of the terahertz scanner. Additionally, the terahertz source was controlled with a six-DOF robot arm for path planning. The corresponding measured dielectric property reference value for the damage identification was computed using a captured terahertz wave at 12% of moisture content. The reflected signals were analyzed to determine the thickness, defectiveness, and locations of the defects through signal processing in the time and frequency domains. Finally, the validity of the structural health monitoring approach for 3D detection and visualization of water loss-induced defects in timber structures using terahertz waves, was discussed.
Smartphone application for structural health monitoring of stay cables: Edgar Cardoso Bridge
Authors:   Lusófona University, Figueiredo Eloi ,   Lusófona University, Moldovan Ionut ,   Lusófona University, Peres Nuno ,   Lusófona University, Pinto Sandra Silva ,   Lusófona University, Alves Pedro ,   Armando Rito Engenharia, Cabral Pedro
Published: June 2024

App4SHM is a mobile system for structural health monitoring of bridges. It consists of a front-end smartphone application, available on both Android and iOS, that is used to measure the vibration frequencies and a backstage server software, accessible through any internet browser, used to perform most of the calculations and to manage the measurement databases. This paper focuses on the App4SHM module for cables, where the frequency observations are directly converted into cable tension forces based on the material and geometrical characteristics of the cable. The conversion considers the sag of the cable, assuming that the cable force is large enough for the static profile of the cable to be accurately described by a parabola. Such an assumption is almost always valid for cable-stayed bridges.

Regardless of the module, App4SHM works in two modes. The training mode is used to collect observations under normal conditions, where the undamaged structure is operating under regular environmental and traffic conditions. The observations obtained in this way are used to train unsupervised machine learning algorithms to learn the normal behavior of the structure. The damage detection mode collects unlabeled observations which are tested against the normal behavior of the structure to detect abnormal behaviors that may be indicative of damage or excessive tensions.

Despite the natural limitations of the accelerometers installed on smartphones, the frequency content of the accelerograms obtained with App4SHM is in accordance with that produced by professional accelerometers. However, frequencies larger than 25 Hz cannot be reliably recovered because of the limited sampling frequency (< 50 Hz) using smartphones.

The paper illustrates the use of App4SHM for the measurement of cable tension forces in a large cable-stayed bridge undergoing retrofit, before and after the replacement of the cables.
Frequency Identification using Resonance Curve-Based Drive-by Monitoring: Field Validation
Authors:   Technische Universität (TU) Darmstadt, Lorenzen Steven Robert ,   Technische Universität (TU) Darmstadt, Rupp Maximilian Michael ,   Technische Universität (TU) Darmstadt, Hübler Clemens
Published: June 2024

Railway bridges have a long service life - in Germany, for example, the average is 122 years. During this time, vehicles are constantly evolving, leading to innovative axle configurations and higher speeds. This leads to loads for which the bridges were not originally designed, which can lead to resonant structural responses.
A key parameter for calculating and evaluating the dynamic behaviour of bridge structures, is the resonance frequency. Due to the large number of bridges in the network, conventional methods using sensors on the structure would be very laborious. A cost-effective alternative would be drive-by monitoring, using sensors on a passing train.
In railway bridge contexts, drive-by monitoring to identify frequencies faces challenges from short spans and high speeds, leading to brief contact periods. The brief periods cause a low resolution of the signals in the frequency domain. An approach to overcome this issue is the use of resonance curve-based drive-by monitoring. The term resonance curve describes the maximum structural responses as a function of speed. In conjunction with the known axle configuration of the train, the resonance frequency can be determined from the resonance curve.
In this paper, data from two field tests are analysed, in which synchronised acceleration measurements were conducted on the main girders of the bridges and the axle boxes of the trains. An ICE 4 was used on a bridge with a 19.5-metre span, and an ICE TD on a bridge spanning 16.4 metres.
The analyses show that frequency identification using indirect resonance curves is feasible under real operating conditions. Thus, this approach enables the determination of a key parameter essential for calibrating structural models. Furthermore, this method enables the avoidance of resonance crossings by speed adaptation, either by acceleration or deceleration. This strategy has the potential to significantly increase the service life of bridges.
Diagnostics and Prognostics of Pitting Corrosion in Large Civil infrastructure Using Multi-scale Simulation and Machine Learning
Authors:   University of California, San Diego (UCSD), Qian Guofeng ,   University of Michigan-Dearborn, Hu Zhen ,   University of California, San Diego (UCSD), Todd Michael D.
Published: June 2024

Corrosion, particularly pitting corrosion, represents a prominent form of deterioration in large infrastructure, characterized by its gradual and continuous capacity-reducing nature. As structures

age, the need for assessing and monitoring corrosion status becomes increasingly crucial for system

life-cycle management and optimal maintenance. However, the intricacies of the corrosion process pose challenges to effective diagnosis and prognosis. These challenges include the insufficient integration of mechanical stress into existing corrosion simulations, the computational intensity of most physics-based corrosion simulations hindering probabilistic studies and real-time predictions, and the difficulty in detecting pitting corrosion due to its localized nature, making it impractical to measure individual pits. This paper introduces a comprehensive prognosis and diagnosis framework applied to a miter gate case study. The framework seamlessly integrates multi-scale simulation, including mesoscale Phase-Field (PF) simulation and macroscale structural analysis, with Machine Learning (ML) methods to enable real-time corrosion diagnosis and prognosis. It facilitates efficient structural health monitoring (SHM) through a digital twin (DT) for corrosion damage. The core components of this framework encompass the development of multiscale simulation for simulating pitting corrosion in large structures, the construction of ML-based surrogate models to expedite simulations, image-based diagnosis, and the updating engine for prognosis.
Application of deep learning for structural health monitoring of a composite overwrapped pressure vessel undergoing cyclic loading
Authors:   BAM Federal Institute for Materials Research and Testing, Charmi Amir ,   BAM Federal Institute for Materials Research and Testing, Heimann Jan ,   BAM Federal Institute for Materials Research and Testing, Duffner Eric ,   BAM Federal Institute for Materials Research and Testing, Hashemi Seyedreza ,   BAM Federal Institute for Materials Research and Testing, Prager Jens
Published: June 2024

Structural Health Monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides extensive information about their structural integrity and their overall condition. Composite Overwrapped Pressure Vessels (COPVs) used for storing hydrogen gases at very high pressures are an example of a critical infrastructure that could benefit significantly from SHM. This can be used to increase the periodic inspection intervals, ensure safe operating conditions by early detection of anomalies, and ultimately estimate the remaining lifetime of COPVs. Therefore, in the Digital Quality Infrastructure Initiative (QI-Digital) in Germany, an SHM system is being developed for COPVs used in a hydrogen refueling station.

In this study, the results of a lifetime fatigue test on a Type IV COPV subjected to many thousands of load cycles under different temperatures and pressures are presented to demonstrate the strengths and challenges associated with such an SHM system. During the cyclic testing up to the final material failure of the COPV, a sensor network of fifteen surface-mounted piezoelectric (PZT) wafers was used to collect the UGW data. However, the pressure variations, the aging process of the COPV, the environmental parameters, and possible damages simultaneously have an impact on the recorded signals. This issue and the lack of labeled data make signal processing and analysis even more demanding. Thus, in this study, semi-supervised, and unsupervised deep learning approaches are utilized to separate the influence of different variables on the UGW data with the final aim of detecting and localizing the damage before critical failure.
Continuous mapping of deformation over a ship structure model based on pointwise measurements
Authors:   National Research Council (CNR), Dessi Daniele ,   Sapienza University of Rome, Saltari Francesco ,   Sapienza University of Rome, Venturi Andrea
Published: June 2024

The development of structural monitoring systems on-board vehicles aims to achieve a full-picture of the vehicle response under operating conditions. The use of different data sources is a key point for many SHM approaches which demand for extensive information and data fusion techniques to address the specific inverse problem in a robust way. Therefore, a procedure to transform pointwise measurements into a continuous representation of the monitored structure is presented in this paper.
To achieve the desired monitoring goals, a numerical approach which combines experimental recorded data and modelled responses from a ship structural model is developed. Craig-Bampton truncation technique is applied on the FE vehicle model to reduce elastic dofs and obtain a computationally cost-effective model. The main advantage of Craig-Bampton modes is the capability to keep unchanged the information at the sensor nodes. A state observer is then built to evaluate the elastic deflection field and the loads over the structure in the points where no measurement is available. A natural second-order observer is exploited as it is more suited for the present application than the Kalman filter. To enhance the observer performances, an optimization process is carried out based on a learning phase.
Following a previous paper about theoretical aspects of the virtual sensing approach on 1D structures. the developed techniques are here applied to the monitoring of the structural deflections and the ambient excitation relative to an elastically scaled model of a fast catamaran, tested in the towing-tank. Accelerations and strain-gage data are used to reconstruct the elastic deformations of the physical model metallic backbone scaling the reference ship behavior. Hydrodynamic load data relative to the demihull portions and the wetdeck are obtained as well.
MT-InSAR Optimisation for Structural Health Monitoring
Authors:   Delft University of Technology (TU Delft), Kuai Hao ,   Delft University of Technology (TU Delft), Macchiarulo Valentina ,   Delft University of Technology (TU Delft), Sharma Satyadhrik ,   City of Amsterdam, Program of Bridges and Quay Walls, Karamitopoulos Pantelis ,   Delft University of Technology (TU Delft), Messali Francesco ,   Delft University of Technology (TU Delft), Giardina Giorgia
Published: June 2024

The implementation of effective and sustainable Structural Health Monitoring (SHM) systems for the evaluation of infrastructure conditions is critical to address the deterioration and damage experienced by structures worldwide. Given the vast number of structures involved, resorting to traditional in-situ visual inspections and data gathering methods is becoming increasingly unfeasible.

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has recently gained attention as a viable solution for long-term SHM. This remote sensing technique combines multiple satellite radar images to measure changes in the Earth’s surface over time. Unlike conventional techniques, MT-InSAR does not require in-situ installations and offers extensive coverage, enabling observations across diverse location and structures.

However, the applicability of MT-InSAR monitoring depends on the relatively unpredictable distribution and location of permanent scatterers (PSs), which are influenced by surface characteristics and vegetation changes. Evaluating the reliability and capacity of MT-InSAR is therefore crucial to enhance its effectiveness in assessing the location and extent of structural damage.

In this study, we present an effective approach to determine the optimal number and position of PSs for detecting different structural damage mechanisms. The approach is exemplified through a case study of a quay wall in Amsterdam, with data inputs simulated using the Finite Element Method. The proposed method has the potential to evaluate the feasibility of MT-InSAR for a broader range of scenarios, enabling to detect specific structural conditions.
Correlation analysis of InSAR displacement data and inspection data towards multiple bridge monitoring in transportation network
Authors:   Univ. of Tsukuba, Nishio Mayuko ,   Univ. of Tsukuba, Sawayama Runa ,   NEC Corporation, Kumura Takahiro ,   NEC Corporation, Kinoshita Kosuke
Published: June 2024

This paper presents a study on multiple bridge monitoring in a wide area of transportation networks using satellite Synthetic Aperture Radar (SAR) technology. SAR has been expected to have potential to analyze displacements of multiple large-scale structures in wide areas and in long-term without installing any sensors on them. This study shows quantitative analysis of InSAR (interferometric SAR) data on multiple bridges regarding to structural condition using their periodic inspection data. The target bridges are seventeen bridges located at a region in Japan, Ibaraki prefecture. They are the bridges that has lengths over 100 m selected from ones operated by the rural-road office of MLIT, the transport ministry of Japan, where periodic inspection data are all opened and available. Their displacements are then obtained using Sentile1 satellite SAR data. The time period for the analysis is seven years from 2016 to 2022, with time interval of 12 days. Using those data, line-of-sight (LoS) displacements of each bridge are derived as a time-series data through interferometric processing. The LoS displacement time-series data are then analyzed for each bridge by principal component analysis (PCA) to extract significant components of time-series deformation behavior. The extracted principle components, air temperature, and inspection records are compared and analyzed. As a result, it is found that a stable correlation between the principle component and air temperature could be one of explanatory factors of bridge conditions. This may indicate the bridges having stable correlation between LoS displacements and air temperature are in a nominal and undamaged condition although further investigation to more bridges is required. Moreover, visualizations of time-series InSAR bridge deflection and extracted feature components and its pattern analysis in multiple bridges are shown to have high potential to contribute on multiple bridge operations in whole transportation network.
Drive-by bridge damage detection by vehicle-bridge interaction neural operator
Authors:   Kyoto University, Irawan Joshua ,   Kyoto University, Kim Chul-Woo
Published: June 2024

Drive-by bridge damage detection using vibrations of vehicles for damage detection of bridges has been studied since it needs no instrumentation on bridges. However, it is still a challenge to be able to extract features that are sensitive to bridge damage and relevant to the responses of the bridge. To address the challenges of drive-by bridge damage detection, machine learning-based approaches have also been actively investigated in recent research. With supervised learning methods, it is almost impossible to obtain training data on the damage state of a bridge. On the other hand, unsupervised learning approaches have been considered, but they only provide changes in relative features rather than physical properties that are useful for estimating the structural performance. To address the above issues, this study aims to propose and investigate the feasibility of drive-by bridge damage detection using a vehicle-bridge interaction neural operator (VINO). In the proposed method, numerical simulations of vehicle-bridge interactions considering the damage field of the bridge are first carried out to generate physically informed training data. The VINO is then constructed using the physically informed training data. However, VINOs are still insufficient to model real bridges. Therefore, in order to improve the practicality of the physically-informed VINO, data-informed fine-tuning is carried out using available data from real bridges. It is noted that no damage data is required in the fine-tuning stage. The feasibility of the drive-by bridge damage detection using VINO is discussed using an in-house experiment on a model bridge under a moving vehicle.
Development of CNN-based sub-sonic speed vehicle body frame micro damage assessment technology using guided wave spectrogram images
Authors:   Korea Railroad Research Institute, Lee Yonghee ,   Korea Railroad Research Institute, Hong Jiyoung
Published: June 2024

Due to advancements in diagnostic technology, sub-sonic speed trains are subjected to significant fatigue stress on their bodies. This phenomenon can be considered a menace, leading to significant accidents on railways due to the tiny size of defects on the train bodies. In this research, a deep learning approach is employed to identify the initial damage status. The goal is to develop an evaluation technique for verifying an initial wall-thinning crack using frequency spectrogram images. The wall-thinning effects of 6.3 mm thick aluminum AL-2024 T6 plates, primarily used in frames and bodies, are confirmed through the frequency responses of guided waves. To verify the effects of wall thinning, both higher-order harmonic frequencies of guided waves and nonlinear ultrasonic techniques are simultaneously used. To identify the tendency of frequency response, frequency spectrogram images, illustrated by short-time Fourier transforming analysis, are chosen. For training the detection system using the Region-based Convolutional Neural Network (R-CNN), spectrogram images are generated for wall-thinning steps with an increment of 0.2 mm (3% of the total thickness). The ResNet-50 is employed in the convolutional layer of the R-CNN engine to address the issue of gradient vanishing. The results of this research indicate that the depth of wall thinning in the plates can be identified. Based on the R-CNN training set, the initial wall-thinning failure can be verified with reliable accuracy.
How structural health monitoring can be embedded in a digital quality infrastructure: an example.
Authors:   BAM Federal Institute for Materials Research and Testing, Heimann Jan ,   BAM Federal Institute for Materials Research and Testing, Charmi Amir ,   BAM Federal Institute for Materials Research and Testing, Karapanagiotis Christos ,   BAM Federal Institute for Materials Research and Testing, Hashemi Seyedreza ,   BAM Federal Institute for Materials Research and Testing, Prager Jens
Published: June 2024

The digital Quality Infrastructure (QI) initiative “QI-Digital” in Germany is focusing on implementing new technologies and approaches to ensure that the task of quality assurance is more efficient and ready for the digital and green transformation of the economy. The implementation of quality control key elements, such as Smart Standards, Digital Certificates and QI-cloud solutions shall contribute to solving the socio-economic, ecological, and technological challenges of our time.

Hydrogen is a key energy carrier and has the potential to play a significant role in the energy transition, especially in mobility. An essential factor for the broad acceptance of hydrogen-based mobility is the availability of refueling stations that operate reliably and safely. Using the example of a Hydrogen Refueling Station (HRS) build within the QI-Digital initative, the Federal Institute for Material Research and Testing (BAM) aims to establish a real laboratory where modern measurement techniques and new digital methods are implemented to enhance operational safety, availability, and economic efficiency and render the technology more attractive for the industry.

In this work, we present an approach to establish a Structural Health Monitoring (SHM) system on a high-pressure buffer inside HRS and show how it could be embedded into a digital QI. The high-pressure buffers are essential components of the plant which are currently inspected periodically without regard to their operating history. Focusing on the transition to a continuous and digitally supported monitoring of the component’s integrity during operation the novel inspection scheme will be linked to a completely digitalized component-related documentation and tested using digital certificates. This allows the operational safety and, if necessary, the remaining useful lifetime to be assessed on an ongoing basis and to be a valuable contribution to increase the sustainability.
Study of Experimental and Assembly Uncertainties on Modal Parameter Estimates of Axial Piston Pumps
Authors:   University of Southern Denmark (SDU), Irissappane Vijayasankar ,   Danfoss, Svendsen Christian ,   University of Southern Denmark (SDU), Avendano-Valencia Luis David
Published: June 2024

Finite Element Model Updating (FEMU) plays a pivotal role in enhancing the accuracy and reliability of structural dynamics models. Nonetheless, uncertainties related to experimental vibration testing and natural manufacturing variability introduce unavoidable variability in the results of Modal Parameter Extraction (MPE), which is the basis for updating. In this regard, this study delves into the inherent variability of estimates from MPE following from experimental vibration testing Axial Piston Pumps (APPs). Accordingly, we investigate the effect of material properties, boundary conditions and measurement noise, through several experimental vibration tests involving assembly-disassembly and exchange of components. These variations account for inherent manufacturing variability and tolerances both at the component and material level. The obtained vibration data is processed using non-parametric FRF estimates and the Stochastic Subspace Identification (SSI) algorithm, resulting in a collection of FRFs and modal parameter estimates (natural frequencies, damping ratios and mode shapes). We use multivariate analysis techniques to quantify the uncertainty of these estimates and to assess the individual effect of assembly and population uncertainties. These results serve as a basis for further investigation towards the updating of a FE model of the APP, and the quantification of modal parameter uncertainties within the material and substructure connectivity properties.
Vibration-based Damage Diagnosis for a Population of Composite Structures Under Uncertainty via Hyper-Sphere and Transfer Component Analysis Based Methods
Authors:   University of Patras, Saramantas Ioannis E. ,   University of Patras, Sakellariou John S. ,   University of Patras, Fassois Spilios D.
Published: June 2024

The problem of vibration-based damage diagnosis, including the detection and type characterization of damages in a population of nominally identical composite structures under operational uncertainty is investigated. This is an important problem for various types of fleets, such as aircraft, drones, fixed wing UAVs and others, which becomes highly challenging if the variability in the composite materials and the manufacturing of the population is significant, while the diagnosis of early-stage damages is pursued. Such a population of 27 composite coupons subjecting to operating variability due to alterations in the temperature, the excitation and the experimental setup is considered in this study. Furthermore, early-stage delamination and impact-induced damages are implemented in 10 of the coupons. Damage diagnosis is herein attempted via two data-driven methods that may account for various types of uncertainty, which are founded on a number of stochastic Multiple-Input Single-Output Transmittance Function AutoRegressive with eXogenous pseudo-eXcitation (MISO-TF-ARX) models. The first employs Hyper Spheres for the representation of the healthy subspace under uncertainty using the parameters of the MISO-TF-ARX models and thus abbreviated as HS-MISO-TF, while the other is based on the Transfer Component Analysis of these parameters, abbreviated as TCA-MISO-TF. The methods’ damage diagnosis performance is experimentally assessed using the above composite coupons and a high number of 7 500 inspection test cases for damage detection, whereas damage characterization is based on 450 test cases. The damage detection and characterization results are presented through Receiver Operating Characteristics curves and confusion matrices, respectively. The obtained results indicate adequate damage detection with the HS-MISO-TF method to achieve 82.1% correct detection for 5% of false alarms, with the TCA-MISO-TF following with 76.4% correct detection. Damage characterization is remarkable with the HS-MISO-TF to correctly characterizing the considered damage type for the 100% of the considered test cases and the TCA-MISO-TF following with 92.2%.
Evaluating Unmanned Aerial Vehicles for Vibrational Analysis: Exploiting Integrated Capabilities of Nano-Drones as Nodes in a WSN
Authors:   University of Bologna, Zauli Matteo ,   University of Bologna, Zonzini Federica ,   University of Bologna, Sciullo Giulio ,   University of Bologna, Marzani Alessandro ,   University of Bologna, De Marchi Luca
Published: June 2024

Addressing the aging infrastructure challenge in both civil and industrial sectors has spurred the development of advanced sensor technologies and novel inspection methodologies, significantly contributing to the field of Structural Health Monitoring (SHM).
While conventional sensors, such as structure-mounted accelerometers, have demonstrated high effectiveness, their deployment may be difficult or even impossible in high-risk environments like post-seismic areas, where the installation procedure hampers the operators' safety. Hence, this study aims to evaluate the characteristics of a novel sensing platform compatible with autonomous deployment for vibration inspection of infrastructures.

In particular, we explore the use of Unmanned Aerial Vehicles (UAVs), with a specific focus on nano-drones, as a viable and promising solution due to their compact size and cost-effectiveness. In particular, Crazyflie 2.1 nano-drones are exploited as reference platforms, given their characteristics in terms of versatility and electrical parameters. The objective is to utilize the on-drone MEMS-based Inertial Measurement and Microcontroller Unit (IMU and MCU) to perform, at the same time, both flight control and navigation, and vibration inspection. This approach eliminates the need for additional hardware, going beyond other successful drone applications in SHM, such as visual inspections, sensors deployment, and distance measurements.

The proposed solution is validated through a comparison with commercial sensors for SHM. Multiple drones can be combined to deploy wireless sensor networks in hard-to-reach locations, ready for vibration monitoring with minimal software adjustments, offering a novel approach to SHM. The results indicate that the Crazyflie drone is capable of detecting even small variations in the dynamic response of a beam, with a maximum deviation of 1% compared to commercially available counterparts designed for SHM purposes.
Multi-class Damage Detection on Experimental Frames through Transfer Component Analysis
Authors:   Politecnico di Torino, Crocetti Alessio ,   Politecnico di Torino, Scussolini Linda ,   Politecnico di Torino, Miraglia Gaetano ,   Politecnico di Torino, Ceravolo Rosario
Published: June 2024

Machine learning techniques have been revealed to be useful in damage identification applications, exploiting, for example, algorithms able to detect anomalies in the monitored data. However, these datasets are not always enough to train a damage classifier reliably. This happens because the lack, or the low number of labelled data, produces outcomes prone to overfitting and bias (due to the low attainable statistical significance of the dataset). To avoid this problem, Transfer Learning technique can be exploited to make up for the lack of data available on a structure (target) using data recorded on another structural system (source), more or less similar, which is rich in data referring to the same structural behavior to be classified (e.g., damage detection in columns due to crack propagation).

In this work, a specific sub-application of Transfer Learning, named Transfer Component Analysis technique, is exploited on a benchmark system (numerically or experimentally) represented by a three-story aluminum scaled structure, subjected to increasing damage and mass variation over the three different floors. Special emphasis will be given to how accuracy is affected by data distribution, and in more detail, the authors will show how the increase in accuracy is related to the type of damage to be classified.
Combining UAV-based photogrammetry and structured light scanning to support the structural health monitoring of concrete structures
Authors:   Technische Universität Braunschweig (TU), Backhaus Jan ,   Technische Universität Braunschweig (TU), De Arriba López Vanessa ,   Technische Universität Braunschweig (TU), Maboudi Mehdi ,   Technische Universität Braunschweig (TU), Bestmann Ulf ,   Technische Universität Braunschweig (TU), Gerke Markus
Published: June 2024

The project „Optical 3D Bridge Inspection”, which is part of DFG’s priority program “100+”, aims to capture surface geometry and damages of prestressed concrete bridges with high-resolution optical tools to assist the structural health monitoring process. In a multi-scale and multi-epoch approach, the building structure is recorded in total with UAV-based cameras in millimetre-resolution – extracting deformation and areas of interest where damages are visible – and in those hotspots, an even higher resolved image block and also a micrometre-resolution structured light scanner (SLS) capture is taken. Detected damages are compared between multiple epochs, monitoring their development. In an experiment, we demonstrated all measurements and their linking possibilities on a reinforced concrete plate under controlled load.
The global image block showed that even the smallest cracks with a width of 0.05mm were visible in the images with a spatial resolution of 0.16mm per pixel. Also, the three-dimensional reconstruction based on the images was able to mirror the plate accurately in all epochs. However, it was shown that manually applied speckles reduced the noise drastically, compared to areas in which the surface only consisted of blank light concrete with only a few microfeatures. The underlying deformation was nevertheless accurately reconstructed in all areas.
It is shown that the SLS can measure the width and detailed shape of a crack, which enabled us to track changes between multiple epochs of different load. With feature-based matching of photogrammetry and SLS results, we can combine the advantages of the fast global UAV-based image approach and the micrometer-resolution local SLS scans and use them to support each other.
In this paper we report about those experiments in detail and analyze them. Based on that, we formulate a possible user story to apply both techniques to support structural health monitoring of bridges and decision-making in maintenance.
Phased Array Ultrasonic Imaging for Characterizing Steel with an Elevated Temperature Gradient.
Authors:   Indian Institute of Technology Madras (IITM), Page Kartik ,   Indian Institute of Technology Madras (IITM), Gantala Thulsiram ,   Indian Institute of Technology Madras (IITM), Balasubramanian Krishnan
Published: June 2024

In this research work a phased array ultrasonic Testing (PAUT) is used for inspecting steel at elevated temperature. Velocity of ultrasound is affected by temperature of medium, and the study of ultrasound is done when it is propagating through High temperature media of steel. For this purpose, a steel sample is heated from one surface and temperature gradient is generated in it. The methods like Full Matrix Capture (FMC) and Total Focusing Method (TFM) are used to generate real time ultrasonic image at particular instant of time. It is a method to generate the real time two-dimensional ultrasonic image. The FEA model of steel with temperature gradient is created and the ultrasonic inspection methods, FMC and TFM are applied in order to generate the image by taking elementary A-scan data from simulation and plotting the same to generate the image. The simulation results can be validated using high temperature experiments with steel samples and the experimental data can be taken to use TFM algorithm for ultrasonic evaluation. These TFM images provide a visual representation of the steel cross section at elevated temperature at specific instances, facilitating early-stage in-situ monitoring and optimization of the steel solidification process.