bluuebunny/crossref_metadata_2025_split huggingface dataset:
Damage detection in 3D printed plates using ultrasonic wave propagation supported with weighted root mean square calculation and wavefield curvature imaging
Authors: Wojtczak E , Rucka M , Andrzejewska A 
Published: June 2024

Abstract
3D printing (additive manufacturing, AM) is a promising approach to producing light and strong structures with many successful applications, e.g., in dentistry and orthopaedics. Many types of filaments differing in mechanical properties can be used to produce 3D printed structures, including polymers, metals or ceramics. Due to the simplicity of the manufacturing process, biodegradable polymers are widely used, e.g., polylactide (polylactide – PLA) with a practical application for manufacturing complex-shaped elements. The current work dealt with the application of ultrasonic guided waves for non-destructive damage detection and imaging in AM plates. Two specimens with defects were manufactured from PLA filament. Different sizes of damage areas were considered. The specimens were tested using the guided wave propagation technique. The waves were excited using a PZT actuator and recorded contactless with the scanning laser Doppler vibrometry (SLDV) in a set of points located at one surface of the sample. The collected signals were processed with two methods. The first was the weighted root mean square (WRMS) algorithm. Different values of the calculation parameters, namely, averaging time and weighting factor were considered. The WRMS damage maps for both samples were prepared to differentiate between intact and damaged areas. The second approach was wavefield curvature imaging (WCI) which allowed the determination of damage maps based on the curvature of the wavefront. The compensation of wave signals was performed to enhance the quality of results. It was observed that the size of the defect strongly influenced the efficiency of imaging with both methods. The limitations of the proposed approaches were characterized. The presented results confirmed that guided waves are promising for non-destructive damage imaging in AM elements.
Estimation of bolt tension using transverse natural frequencies of the shank and protruding end
Authors: Rashid D. S. , Giorgio-Serchi F. , Hosoya N. , Garcia Cava D. 
Published: June 2024

Abstract
Bolted connections are crucial components in mechanical systems with the purpose of assembling different components. However, the tension of bolts may decrease due to recursive loading and environmental conditions. Monitoring the tension of bolted connections can help prevent failures and identify damage at an early stage. Vibration-based techniques have shown promising results for evaluating the tension present in a bolted connection. However, previous studies have neglected the effect of the protruding end, which can significantly affect the dynamic response of the bolted connection. In this study, a novel extended model based on an Euler-Bernoulli beam is proposed that accurately captures the effects of the protruding end on the estimation of the transverse natural frequency of the bolt. The proposed technique incorporating the transversal natural frequencies associated with the protruding end of the bolted connection provides a more representative frequency response of the bolted connection. The results show that there is a significant shift in the natural frequencies at higher modes which are associated with the protruding end. The findings have important implications for the monitoring of bolted connections and provide a basis for more accurate and reliable health and performance assessments. Further research could focus on the validation of the proposed model using experimental data and its application in practical settings.
Accelerometer configuration assessment of Milad Tower utilizing operational modal analysis
Authors: Sadeqi Amirali , Esfandiari Akbar , Behboodi Saeed , Sanayei Masoud 
Published: June 2024

Abstract
In operational modal analysis, the observability and quality of the extracted modal parameters, apart from the characteristics of structures, loads, and noise, largely depend on the data acquisition system. Especially in large-scale structures, because of the low frequency- and low amplitude of vibration, the configuration and arrangement of the sensors are of particular interest. Milad Tower, with a height of 435m in Tehran, is the sixth tallest telecommunication tower in the world. The authors have previously identified the tower’s modal parameters by developing an automated data-driven subspace approach and investigated the long-term variations of modal parameters for structural health monitoring purposes. This paper adopts this identification approach to a Milad Tower finite element model subjected to random and seismic loadings. The identified modal parameters are compared with those obtained based on real acceleration records. The MEMS accelerometers’ performance is investigated by stabilizing identified natural frequencies for different setups and scenarios. Also, the performance of various accelerometers temporarily mounted on the tower is compared, and a response signal measured under the most recent earthquake in Tehran is presented. The qualitative and quantitative assessments of the sensors are discussed, and required improvements are recommended.
Quantum Machine Learning for Structural Health Monitoring
Authors: Yaghoubi Vahid 
Published: June 2024

Abstract
Nowadays, employing deep learning for Structural Health Monitoring is a common practice. However, one of the main challenges here is the lack of data. Several methods have been developed to address this issue. Quantum machine learning is known to be trained faster and with less data, therefore, it could be a suitable option to be used for this purpose. However, since at the current stage limited numbers of qubits can remain stable at the same time, hybrid quantum-classical deep learning approaches can be a replacement. In this study, the benefit of incorporating a quantum layer into a classical deep learner for detecting damage is investigated. For this purpose, a deep learning model with and without a quantum layer is used to predict damage in a wind turbine blade by using ultrasonic inspection data. The results indicate the benefit of employing hybrid quantum-classical ML in detecting damage.
A Vehicle Bridge Interaction based approach for the monitoring of bridges through an electric mobile platform
Authors: Fiandaca D , Denoël V , Pirrotta A 
Published: June 2024

Abstract
The main purpose of the Structural Health Monitoring (SHM) techniques applied in bridges is to monitor many structural parameters to prevent serious damage that may eventually lead to collapse of the entire structure. To date, Operational Modal Analysis (OMA) methods are widely seen as the most reliable SHM technique. These encompass a series of procedures for deriving the modal parameters of a structure using the data acquired under its operating conditions, without recording the external excitation. However, traditional OMA methods generally require expensive setup and time-consuming procedures. With the aim of overcoming these issues, in this paper, an innovative Vehicle-Bridge Interaction (VBI) based approach is investigated as a low-cost monitoring system for the identification of the structural modal parameters. These are obtained from the analysis in the frequency domain of the measured vehicle’s vibration response during several passages on the structure. Therefore, the proposed approach has been adopted in an experimental campaign on a pedestrian bridge in the city of Palermo (Italy). In particular, a very low-cost setup comprising a scaled-up electric vehicle as a mobile platform and few accelerometers has been used.
High-performance beam finite element for predictive response in monitoring existing bridges
Authors: Fusco D , Rinaldi C , Addessi D , Gattulli V 
Published: June 2024

Abstract
In recent years, several advanced technologies, such as Artificial Intelligence (AI) techniques, have been developed to automate inspections and monitoring processes of existing bridges. In this context, the efficiency of computational models is crucial in model updating for monitoring systems and training neural networks. Although the nonlinear structural response of the bridges can be efficiently analysed through two-dimensional and three-dimensional finite element (FE) models, these commonly require high computational efforts. This work adopts a high-performance beam finite element based on a damage-plasticity model, implemented in the OpenSees framework, for prestressed reinforced concrete girders. The beam FE relies on a force-based (FB) formulation which is more efficient than the classical displacement-based approach. The constitutive law of the concrete fibers is based on a plastic-damage model, which considers two different damage parameters for the compression and tensile behaviour to take into account the re-closure of the tensile cracks. Dynamic responses in both linear and nonlinear regime are simulated under white noise excitation. ANNs are trained in a subset of the predicted responses in the linear range and the trained network is used to simulate the high amplitude response in which nonlinear behaviour is experienced. Interesting results are acquired useful for further investigations.
Structural health monitoring of a curved roadway bridge: model calibration and collapse simulation aspects
Authors: Farneti Elisabetta , Meoni Andrea , Natali Agnese , Celati Simone , Cavalagli Nicola , Salvatore Walter , Ubertini Filippo 
Published: June 2024

Abstract
This work aims to explore the potential of the Applied Element Method (AEM) for structural analysis application in the framework of bridge monitoring, focusing in particular on the aspects of model calibration and simulation of the collapse behaviour. An AEM model of a curved roadway bridge undergoing slow landslide-induced movements was built and calibrated by using the results of ambient vibration testing and modal identification. A simulation under increasing displacements caused by the landslide was carried out, predicting the deformation evolution of the bridge and comparing it with the current state of damage observed on site.
Cases of sensor fault assessment
Authors: Lydakis E , Amador S D R , Koss H , Tarpø M , Brincker R 
Published: June 2024

Abstract
The field of structural health monitoring (SHM) is based on collecting data, and according to that, useful information about the monitored structures is obtained. Therefore, it is essential that the signals from the sensors used in the monitoring campaign can be trusted. However, some of these sensors will sometimes fail or will, at least, pick up excessive noise that might influence the quality of the information extracted from the data. In this paper, a new technique of sensor fault detection is illustrated that is based on constraints between multiple output signals from the monitored structures.
Experimental machine learning approach for building structural health monitoring application
Authors: Mghazli Mohamed Oualid , Zoubir Zineb , Rallu Antoine , Lamdouar Nouzha , Elmankibi Mohamed 
Published: June 2024

Abstract
The research and development field of structural health monitoring is evolving, and as a result, it now provides choices for disaster prevention and life cycle extension. This can help boost structural safety and infrastructures resiliency. Researchers have taken an interest in the use of machine learning techniques for these applications as a result of the promising outcomes that those approaches have delivered. In spite of this, damage detection applications necessitate the use of sophisticated and reliable data-driven enabled technologies. This is because of the complex behavior of structures that is notoriously difficult to track. In this investigation, triaxial accelerometers are used for data acquisition purposes in order to monitor and examine the structural performance of an experimental four-story building frame when subjected to a variety of structural configurations. The effectiveness of supervised machine learning algorithms was analyzed and compared to that of unsupervised learning approaches. In spite of the fact that supervised models have demonstrated good performances for the detection of initially established anomalies, they have only partially exhibited performances for newly produced damage patterns, which restricts their application in real life cases. The results obtained by the unsupervised algorithms, on the other hand, were comparable and satisfying, revealing essential capabilities for damage detection applications.
Feasibility study on monitoring the structural health of a transmission tower with controlled damage scenarios
Authors: Bel-Hadj Yacine , Fernandes Adelmo , Weijtjens Wout , Devriendt Christof 
Published: June 2024

Abstract
The Smart Tower project, a collaboration between Vrije Universiteit Brussel (VUB) and Elia, aims to explore the application of Structural Health Monitoring (SHM) for transmission towers. The tower was equipped with a variety of sensors, including Strain Gauges (SG), Resistance Temperature Detectors (RTD), Piezoelectric Transducers (PZT), accelerometers (ACC), and Acoustic Emission (AE) sensors. It was then subjected to controlled damage scenarios to simulate typical failure modes. The study analyzed the collected sensor data to assess its suitability for SHM. The results revealed that the acceleration data features were sensitive to the simulated damage, with modal parameters identifying almost all relevant damage instances. However, minor damage to specific areas, such as bolt loosening, affected only high-frequency modes and required sensors in close proximity to the damage. Conversely, significant damage, like bar removal, impacted low-frequency modes and was detectable by all accelerometers. Although the PZT sensor was able to identify multiple modes, it could not detect the simulated damage due to its location. These findings illustrate the potential of SHM for transmission towers and underscore the importance of sensor placement in detecting specific types of damage.
Investigation of modal damage-sensitive features of a scaled three-storey steel frame for vibration-based damage detection.
Authors: Marafini Francesca , Zini Giacomo , Barontini Alberto , Monchetti Silvia , Betti Michele , Bartoli Gianni , Mendes Nuno , Cicirello Alice 
Published: June 2024

Abstract
The application of vibration-based Structural Health Monitoring (SHM) for damage detection is characterised by three fundamental aspects: the features extracted as representative of the structural condition that can be directly linked to some form of damage, the metrics selected as novelty or damage index, and the statistical model or classifier built to identify underlying patterns indicative of changes in the structure’s state. Focusing on the first step to improve the performance of vibration-based SHM strategies, the extracted features should be robust to noise, sensitive to the presence of a specific type of damage. Further, damage should induce patterns that are distinguishable from environmental and operational variabilities and other forms of damage such as ageing phenomena. In this paper, the problem is framed as an outlier detection problem and the the use of different modal parameters as Damage Sensitive Features (DSFs) is investigated, evaluating them based on the detection performance of an unsupervised One-Class Support Vector Machine (OCSVM) classifier. In particular, an artificial dataset is generated from the calibrated numerical model of a three-storey steel frame structure in different damage scenarios. The methodology is validated against available experimental data. For the case investigated the natural frequencies were sensitive to damage and robust to noise.
Dynamic Characterization of a Full-Scale Three-Story Mass Timber Building Structure
Authors: Uarac P Patricio A. , Barbosa Andre R. , Sinha Arijit , Simpson Barbara G. , Ho Tu X. 
Published: June 2024

Abstract
A veneer-based engineered wood product known as Mass Ply Panels (MPP) was recently introduced and certified per ANSI-PRG 320. A full-scale three-story mass timber building structure was constructed and tested at Oregon State University to demonstrate the potential of MPP in the design of resilient, structural lateral force-resisting systems. The building structure comprised MPP diaphragms, laminated veneer lumber (LVL) beams and columns, and an MPP rocking wall design. Two opportunistic vibration tests were performed to charac-terize the dynamic properties of the structure. First, an implosion of a stadium within 600 m of the building location was used as the main excitation source, during which bi-directional horizontal acceleration data were collected for approximately 18 seconds. Second, an ambient vibration test was conducted to collect horizontal acceleration data for one hour. In both tests, sixteen accelerometers were used to measure the response of the structure. Modal features were extracted using an output-only method and compared with the estimates from a finite element model. Lessons learned can be used to inform future modeling efforts of a mass timber building to be tested on the Natural Hazards Engineering Research Infrastructure (NHERI) Experimental Facility at the University of California San Diego high-performance outdoor shake table.
An experimental study on the performance of virtual sensing using optimal and regular physical sensors placement
Authors: Bourdalos D M , Zisopoulos S S , Tcherniak D , Sakellariou J S 
Published: June 2024

Abstract
Vibration analysis is highly beneficial in a variety of engineering areas. However, in many real-world applications, vibration data acquisition may be challenging due to the accessibility of the desired sensor locations. It can be also costly if many measurement points are required. Consequently, a few vibration estimation methods have been proposed, which are referred to as “virtual sensing”. Virtual sensing claims to be able to replace a physical sensor with a virtual one, whose signal should closely resemble the signal from the physical sensor if it was placed at the same location. The signal from such a virtual sensor is estimated based on a numerical model of the structure under test and data from a number of physical sensors. In this study, the well-known modal expansion and decomposition-based virtual sensing method is examined, and its sensitivity to the amount and location of physical sensors is explored. Two sensor placement scenarios are considered: (i) the most common scenario where the physical sensors are placed in the nodes of a regular mesh, and (ii) where the sensors configuration is generated by the optimal sensors placement (OSP) algorithm. The experimental examination is performed on a simple test structure (rectangular aluminum plate) using time and frequency domain performance indicators for three excitation profiles (pseudo-random, burst pseudorandom, and sinusoidal). The results demonstrate that the use of OSP significantly improves the performance of virtual sensing.
Indirect assessment of tension force in cables equipped with TMDs
Authors: Pomaranzi G. , Argentini T. , Zasso A. 
Published: June 2024

Abstract
In this paper, an indirect method to obtain the actual tension force in stay cables is presented. It is based on a combined experimental and numerical approach, where the experimental measurement of the first natural frequency of the cable is combined with a FE model of the cable to finally obtain an estimate of the actual tension force. The method is suitable to be applied to cables eventually equipped with tuned mass dampers. It is here presented referred to an application case, the stays of the Hovenring Bridge. Being those cables endowed with two tuned mass dampers, the well-established indirect method based on the string theory cannot be applied to the present case. Therefore a new approach is proposed: it makes advantage of a FE model of the cable, made by equivalent tensioned beam elements and includes the effects of the dampers through equivalent mass-spring-damper systems. A complete description of the method is presented along with its validation through a comparison with a direct measurement of the tension force in 4 cables.
Quantifying uncertainties of empirical damping estimates from full-scale responses of a polar vessel
Authors: Van Zijl C M , Bekker A 
Published: June 2024

Abstract
Damping is critical for accurately predicting wave-induced ship vibrations. Operational modal analysis (OMA) is useful for identifying damping estimates from measured outputs. In reality, periodic and non-stationary wave excitation violates fundamental OMA assumptions which, along with noisy finite output measurements, produce bias and random errors. To address uncertainty in empirical damping estimates of the two-node vertical bending (2nVB) modal damping of a polar vessel, OMA is performed on full-scale measurements obtained during dedicated open water test sequences. The effect of user-selected block size and model order for the data-driven stochastic subspace identification algorithm is investigated. Random errors in damping estimates generally decrease with increasing ship speed possibly due to higher signal-to-noise ratios in the higher speed cases. Further, higher encountered wave lengths, which occur at low ship speeds, may increase periodic wave excitation of the vessel and requires higher model orders to eliminate bias. In general, block sizes of 40 to 80, and model orders ≥ 80 yielded the most stable estimates. Uncertainty in damping estimates are estimated using a first-order sensitivity analysis and, even for the most stable estimates, relatively large random errors are obtained. A conservative estimate for the 2nVB damping is ≤ 2%. Damping estimates are observed to decrease with increasing ship speed possibly due to hydro-elastic effects.
Effect of eccentricity on sensing in spider web inspired cable nets
Authors: Masmeijer T.C.P. , Méndez Echenagucia T. , Slavič J. , Loendersloot R. , Habtour E. 
Published: June 2024

Abstract
Spider webs are multifunctional tools that, besides capturing prey, monitor vibration in the web to localize the prey. Spiders obtain an evolutionary benefit from improving monitoring because better information processing leads to more successful captures, leading to higher chances for survival. Based on this, combined with the costly energy requirements of signaling, biologists observe that the spider’s web operates as an extended cognition of the spider’s central nervous system. Here an extended cognition means that the web filters signals to make decoding information easier. Spider webs contain characteristic design patterns, such as an eccentric location of the central hub. This paper investigates how this eccentricity enables a spatially dependent dynamic response of web-like structures, potentially enabling extended cognition. Modeling consists of modal analyses in a Finite Elements Model on web-like structures where a mass is sequentially moved across all nodes. This yields the natural frequencies of the structure depending on the mass location. Analysis shows that the natural frequency forms patterns based on the mass location, and more importantly, it shows how these patterns become more intricate by including eccentricity. The resulting maximum change in natural frequency grows as the eccentricity in the design grows. This shows that eccentricity influences the dynamic response, hence indicating that eccentricity could enable the web’s extended cognition. The numerical model of spider web-inspired structures can investigate the effects of the geometry on any network-like structures besides spider webs. The authors believe the model can be utilized for impact/anomaly detection on network-like structures.