bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimating Modal Properties of Nonlinear Cables Using Targetless Visual Tracking
Authors: Riveros N , Boroschek R L , Beltran J F 
Published: June 2024

Abstract
One way to study the health state and operational force on a cable under varying environmental conditions is to estimate its modal properties. In this study, the vibration of a nonlinear polymer cable is examined in the laboratory using computer vision techniques, such as videos, in comparison with traditional sensors. Two types of excitations are used to induce cable vibration: broad band ambient vibrations and initial conditions (deformation and impact). Displacements are measured simultaneously with traditional sensors and consumer-grade cameras (including smartphones) from different perspectives (full-field and local measurements). The traditional sensors include six ultrasonic sensors equidistantly located along the cable and an accelerometer near one end. For each video obtained, multiple feature points of the cable are tracked using the KLT algorithm without artificial targets, and the displacement records are obtained in pixels. Modal properties are determined using parametric techniques like SSI-COV and ERA. To determine displacement in metric units and mode shapes with perspective correction, a methodology is proposed using camera calibration. The non-linearity of the cable is studied by analyzing the temporal evolution of frequencies over time. The performance of the proposed methodology was then compared to that of conventional sensors.
An effective approach based on reliability methods for high-dimensional Bayesian model updating of dynamical nonlinear structures
Authors: Jerez D. J. , Jensen H. J. , Beer M. , Figueroa C. 
Published: June 2024

Abstract
Bayesian model updating represents a sound formulation to incorporate the unavoidable uncertainties arising in the system identification of infrastructure assets. However, the treatment of cases involving a relatively large number of model parameters remains an open issue, especially for dynamic nonlinear structural models. In this context, an effective implementation of subset simulation is considered within the framework of Bayesian model updating with structural reliability methods (BUS). For improved numerical efficiency, a substructure coupling technique for dynamic analysis is implemented to develop a reduced-order model strategy. To assess the capabilities of the proposed method, an application example that considers a three-dimensional bridge model equipped with nonlinear devices is presented.
Novelty detection across a small population of real structures: A negative selection approach
Authors: Delo Giulia , Mattone Massimiliano , Surace Cecilia , Worden Keith 
Published: June 2024

Abstract
Vibration-based Structural Health Monitoring (SHM), exploits a variety of approaches for novelty detection. In particular, many data-based methods try to recognise patterns by exploiting analogies with the human body’s natural defences at a cellular level. These algorithms fall within the Artificial Immune System (AIS) class and can be chosen, according to their peculiarities, to solve specific problems in diverse application areas. This study investigates the damage-detection process in different operational conditions, obtained by applying structural modifications to a laboratory-scale aeroplane, which follows the geometric features of the GARTEUR benchmark project. Damage identification is performed by exploiting the Negative Selection Algorithm (NSA), already applied by some of the authors on numerically-simulated case studies, and chosen for its capability of self/non-self discrimination under varying operational or environmental conditions. The research is expanded by using sparse autoencoders for feature dimensionality reduction. The method is applied to an experimental dataset acquired via Scanning Laser Doppler Vibrometer (SLDV) measurements, to identify consistent damage-sensitive features from the frequency response functions, and to obtain a reliable fault-detection performance.
R/C buildings’ seismic damage prediction based on semi-supervised automatic differentiation variational inference deep autoencoder
Authors: Demertzis K , Kostinakis K , Morfidis K , Iliadis L 
Published: June 2024

Abstract
Structural damage from earthquakes has been assessed using a variety of methodologies, both statistical and, more recently, utilizing Machine Learning (ML) algorithms. The effectiveness of data-driven procedures, even when applied to extremely time-consuming scenarios and data sets that reflect substantial expertise and research, completely depends on the quality of the underlying data. The performance of the intelligent model can also be impacted by a lack of in-depth knowledge and expertise in using complex machine learning architectures. This can also prevent some crucial hyperparameters from being adjusted, which ultimately reduces the algorithm’s reliability and generalizability. The present research offers a Bayesian-based semi-supervised Automatic Differentiation Variational Inference (ADVI) deep autoencoder for forecasting seismic damage of R/C buildings. It is a state-of-the-art, intelligent technology that automatically converts the variables in the issue into actual coordinate space using an upgraded ADVI technique. Finally, using a brand-new Adaptive Learning Rate Gradient Algorithm (ALRGA), it chooses a technique in this area that is a function of the changed variables and optimizes its parameters. Using the sophisticated ADVI technique to establish a posterior distribution without having an analytical solution is an upgraded version of the semi-supervised learning method. Estimating seismic damage to buildings is accelerated and greatly simplified by the suggested methodology, which eliminates the computational complexity of the analytical methods. By performing Nonlinear Time History Analyses of 3D R/C structures exposed to 65 earthquakes, a realistic dataset for the model evaluation is produced. The system’s strong generalizability and the proposed methodology’s detailed convergence stability reveal that it is a valuable method that can outperform other ML algorithms.
Bayesian updating of the displacement-strain transformation matrix
Authors: Adarsh S , Panda Jagajyoti , Ray-Chaudhuri S 
Published: June 2024

Abstract
The updating of the displacement-strain transformation matrix has not garnered much attention over the years, as most studies focus on updating mass, damping, and stiffness matrices. In this study, updating of the parametrized displacement-strain transformation matrix is done in a Bayesian probabilistic framework by combining the data from both acceleration and strain sensors. The Bayesian framework ensures that the uncertainties, especially modeling uncertainties, are explicitly considered and provides multiple possible estimates of the parameters, unlike a classical estimation framework where only one value is estimated. Samples from the posterior probability density function of the parameters are simulated using the transitional Markov Chain Monte Carlo method. The updated displacement-strain transformation matrix can be used to obtain strains with higher fidelity in numerical simulations and to obtain robust response predictions incorporating modeling uncertainties. In addition, once a transformation matrix has been updated, only acceleration data are required for damage localization, i.e., strain measurements are not required post-updating, thereby reducing the overall health monitoring costs and data storage burden. The Bayesian updating procedure is investigated using experimental data from a four-story shear building laboratory model.
Translating Assembly Accuracy Requirements to Cut-Off Frequencies for Component Mode Synthesis
Authors: Janssen Lars A.L. , Besselink Bart , Fey Rob H.B. , Wouw Nathan van de 
Published: June 2024

Abstract
One of the most popular methods for reducing the complexity of assemblies of finite element models in the field of structural dynamics is component mode synthesis. A main challenge of component mode synthesis is balancing model complexity and model accuracy, because it is difficult to predict how component reduction influences assembly model accuracy. This work introduces an approach that allows for the translation of assembly model accuracy requirements in the frequency domain to the automatic selection of the cut-off frequencies for the model-order reduction (MOR) of components. The approach is based on a mathematical approach for MOR for coupled linear systems in the field of systems and control. We show how this approach is also applicable to structural dynamics models. We demonstrate the use of this approach in the scope of component mode synthesis (CMS) methods with the aim to reduce the complexity of component models while guaranteeing accuracy requirements of the assembly model. The proposed approach is illustrated on a mechanical, three-component structural dynamics system for which reduced-order models are computed that are reduced further compared to reduction using standard methods. This results in lower simulation cost, while maintaining the required accuracy.
A Hazard-consistent Residual Drift Model for SMRF
Authors: Zahra Faridah , Macedo Jorge , Málaga-Chuquitaype Christian 
Published: June 2024

Abstract
The seismic permanent or residual drift is a structural response parameter of relatively easy collection and one whose usefulness in damage assessment has been proven in many past earthquake events. Oftentimes, residual drifts are a key factor when deciding on the demolition of a number of structures, even if said structures do not show other evident signs of severe damage. However, general studies on seismic residual drift demands are far less common than those dealing with its transient maximum drift counterpart. In this paper, we offer a simple data-driven predictive model for residual drift demands in Steel Moment Resisting Frames (SMRF). To this end, our model is based on a database generated by nonlinear response history analyses (NRHA) on 24 deteriorating SMRFs under the action of 596 hazard-consistent ground-motion records resulting in a database of more than 14 000 structural responses. The model development is preceded by an extensive feature selection process that employs several Machine Learning (ML) algorithms. Importantly, all available seismic codes and studies regarding residual drift demands available to date have overlooked the issue of hazard consistency, therefore disregarding the causal connection between seismic hazard level and the corresponding ground motion suite used. In contrast, we use the Conditional Scenario Spectra method, which generates a set of realistic earthquake spectra with an assigned rate of occurrences based on their spectral shape and intensity, hence, our results preserve the critical relationship of hazard consistency. In this context, our model highlights the severe over- and under-estimations associated with hazard-inconsistent estimates of residual drifts.
Computational homogenization method for the bending analysis of submarine power cables
Authors: Fang Pan , Li Xiao , Jiang Xiaoli , Hopman Hans , Bai Yong 
Published: June 2024

Abstract
The complex structure and material property of a cable, particularly the stick-slip issue among its components pose the challenge for the bending analysis of submarine power cables. The calculation time and convergence problem of a full model makes the simulation unpractical during the design phase. This paper takes advantage of the peculiar structural property of helical components inside a cable, proposing a computational homogenization approach for analyzing the cable behavior under bending from global and local perspectives. This method assumes a macro model that is based on the theory of periodic beamlike structure, and a short-size micro model that is solved through a detailed finite element study. Results demonstrate the efficiency and capability of the proposed model that considers the structure nonlinearity and contact condition of a multi-layer cable with helical wires.
Railway vibration emission model based on a multi-contact source-receiver mobility approach
Authors: Villot Michel , Guigou-Carter Catherine 
Published: June 2024

Abstract
Railway ground-borne vibration emission is an ongoing issue, which shows a need for vibration emission characterization and prediction where the approach used should ensure transferability from one site to another. This transfer is often calculated using heavy frequency-based (narrow band) numerical models. This paper suggests a quicker approach, where the rolling stock/track/ground system is considered as a source-receiver mechanical system and the three parts of the system represented by their mechanical mobilities. In such a mobility approach, the passing train acts as primary vibration source (represented by a line of uncorrelated forces applied to the track), and the track acts as a vibration transfer system between passing train and ground. The couplings between passing train and track and between track and ground are both represented by a multi-contact line with the same spatial resolution. The output quantity is the line of uncorrelated contact forces (contact force density) applied to ground and the quantity used to evaluate the railway vibration emission is the vibration power transmitted to ground. An application of this mobility approach to IC trains rolling on ballasted track laid on typified homogeneous soils (soft, medium and stiff) is presented.
A numerical validation of a 3D hybrid meshless methodology for dynamic soil-structure interaction problems
Authors: Arcos Robert , Soares Paulo J , Conto Kenny F , Costa Pedro Alves , Godinho Luís 
Published: June 2024

Abstract
In this paper, a hybrid meshless methodology for the simulation of dynamic soil-structure interaction problems is verified in a three-dimensional context. The method aims to predict the ground-borne noise and vibration levels to which a new building to be constructed close to a ground vibration source will be subjected. The method is based on three steps: incident wave field evaluation in a set of collocation points, incident wave field virtualization by a set of virtual forces and structure response determination applying the set of virtual forces to a coupled soil-structure model. The proposed meshless method is verified in the context of the problem of one and four shallows foundations embedded in a homogeneous half-space subjected to harmonic loading using synthetic data for the incident wave field in the ground surface. Discussions on the practical deployment of the method are also included.
Quantification of Polymorphic Uncertainties in Structural Dynamics: Case Study of a Guyed Mast
Authors: Marwitz Simon , Lahmer Tom , Zabel Volkmar 
Published: June 2024

Abstract
Aging vibration-prone structures may require up-to-date proofs of their load-bearing capacity for retrofitting or life-cycle assessments. The structural vibration response is affected by naturally variable structural properties and loads, known as aleatory uncertainties. With modeling, additional epistemic uncertainties are introduced due to missing knowledge of model parameters. The model responses then include both types of uncertainties in a mixed and nested form. Quantifying these polymorphic uncertainties is particularly important in structural dynamics, where a deterministic model may significantly underestimate responses, such as resonance phenomena.
A case study is conducted to characterize the vibration behavior of a guyed mast under polymorphic uncertainties. A simplified linear structural model is built using commercially available FEM software. Polymorphic uncertainties in the input parameters are modeled and propagated using in-house code.
Aleatory uncertainties are modeled by probability theory, and a quasi-Monte Carlo simulation based on weighted sampling facilitates an efficient reuse of computed samples. Epistemic uncertainties are modeled by evidence/belief function theory and their propagation is performed using interval optimizations on Radial Basis Function interpolators and quasi-Monte Carlo sequences. Particular emphasis is placed on computational efficiency, as the developed method is designed to be used for more complex numerical models. Statistics and aggregation of belief functions are used to yield explainable results.
The case study scenario is a retrofit of a guyed mast based on outdated design documents. Epistemic uncertain parameters include unknown structural damping, cross-section tolerances, material properties, the additional mass from antennas and cables, and the pre-tensioning forces of the guy cables. Aleatory uncertainties arise from temperature effects on the guy cables and the viscosity of the dampers, as well as from icing in winter. The results highlight the intrinsic variations of natural frequencies, the characterization of resonance bands due to epistemic uncertainties, and the effectiveness of a deterministically designed Tuned Mass Damper (TMD) in a structural model influenced by polymorphic uncertainties.
The developed methodology is applicable to moderately computationally expensive models of any type. Model runs are efficiently re-used and the uncertainty propagation can be highly parallelized. The method reveals where enhancing knowledge about partly-known model parameters is beneficial and permits robust retrofit designs or increased confidence in life-cycle estimates.
Model updating for the simulation of surface strains on printed circuit boards considering parameter uncertainty
Authors: Schmidt H , Käß M , Lichtinger R , Hülsebrock M 
Published: June 2024

Abstract
The efficient and reliable design of power electronic components plays an important role in the development process of electrically driven vehicles. One key aspect is the reliability of solder joints on printed circuit boards (PCB) that greatly depends on the surface strain at the solder joint locations. It is therefore unavoidable to use precise simulations of surface strains to reliably estimate the solder joint lifetime. This work presents a procedure of model updating of a printed circuit board model that considers the variability of board behavior due to uncertainties in the material composition or the manufacturing process. Hierarchical Bayesian model updating is applied to incorporate this variability. The printed circuit board is seen as a multi-level model that is updated in two steps. Experimental data from system and component level are used to sequentially update the printed circuit board and the board mounting. The experimental data combine modal information and measured frequency response functions. The proposed procedure is applied to a test PCB and the updated model is validated with experimental surface strain data.
An innovative only-output method to identify a structural system
Authors: Russotto S , Denoël V , Pirrotta A 
Published: June 2024

Abstract
Structural Health Monitoring (SHM) is nowadays common in many branches of engineering since it allows to have a continuous or periodic report of the structural conditions and therefore to promptly intervene if there are incipient damages. The first step to perform a SHM is the identification of the dynamic parameters, i.e. natural frequencies, damping ratios and modal shapes, and it is a crucial step since a modification of the structural parameters can be a direct consequence of structural damages. Among the structural identification methods, Operational Modal Analysis (OMA) methods have received increasing attention from the researchers since they do not require the knowledge of the structural excitation that is due to ambient vibrations and that is usually modeled as a white noise. This aspect makes this kind of methods cheaper and simpler than the classical Experimental Modal Analysis (EMA) methods.
In this paper an innovative OMA method is proposed. It is a semi - automated method that allows to identify natural frequencies, damping ratios and modal shapes of a structural system and that can be used also from users that have not knowledge in stochastic dynamics and signal analysis. First of all, the modal shapes are estimated through the use of signal filtering techniques applied on the stochastic properties of the output process and then natural frequencies and damping ratios can be estimated from the mono - component analytical signals obtained by performing a decomposition of the analytical signals matrix. The proposed method has been used to perform the dynamic identification of a real historic building situated in Palermo, i.e. Chiaramonte palace, and the results obtained have been compared with those obtained by using other OMA methods.
On reduction of noise emissions from thin-walled slender tubes by means of acoustic metamaterials
Authors: Søndergaard S H , Søe-Knudsen A 
Published: June 2024

Abstract
The dynamic response of a thin-walled slender tube is dominated by noisy flexural modes. One topic of interest in research today is the use of acoustic metamaterials to reduce noise emissions from structures. These aspects are combined in the present work in which a solution for reducing the noise emission from thin-walled tubes is sought. The Wave Finite Element Method (WFEM) is used to estimate the dispersion relation of the free propagating waves in the tube. Extrudable rings of thermoplastic material are added to the inside of the steel tube as local resonators for changing the response. The rings are added in sections to create a periodic waveguide which introduces interference stop-bands for the flexural waves. Additionally, local bending modes of the rings couple with the global modes of the tube to reduce global vibrations at the resonant frequencies of the rings. The results of free wave propagation analyses obtained through WFEM are compared to the forced response of the structure in standard linear harmonic analyses conducted in the commercial FE software Ansys. This is done in different loading scenarios to determine the impact of adding the resonator rings to different excitations and frequencies.
Determining stop band locations in periodic waveguides by conventional finite element modal analysis
Authors: Hansen Kristian , Johansen Alexander K , Kragh Oliver 
Published: June 2024

Abstract
The use of metamaterials for the mitigation of structural wave propagation shows relevance in a wide range of engineering solutions. At the present time, the design of such metamaterials is often based on iterative design and numerical optimization around the Wave Finite Element Method. This approach is computationally heavy. In this paper, it is demonstrated how post-processing of mode shape results from conventional finite element modal analysis can identify partial stop bands in periodic waveguides. This provides a foundation for iterative design and efficient numerical optimization of periodic waveguides. When the modal analysis is performed on a symmetric unit periodicity cell, using ‘class-consistent’ boundary conditions, the eigenfrequency-locations coincide with the beginning and end of partial stop bands. Identifying which eigenfrequencies pair up, to indicate a partial stop band, is a matter of determining which modeshapes belong to the same branch in the dispersion diagrams. A novel algorithm is developed and tested, able to pair-wise match modal solutions belonging to the same branch of the dispersion diagrams, only based on mode shape information, available from any commercial finite element code.