bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Application of Domain Adaptation for Population-Based Structural Health Monitoring
Authors: Giglioni Valentina , Poole Jack , Venanzi Ilaria , Ubertini Filippo , Dervilis Nikolaos , Worden Keith 
Published: June 2024

Abstract
In the field of civil infrastructure, Structural Health Monitoring generally suffers from a scarcity of labelled damage-state data. To solve this issue, this work adopts a Transfer Learning approach for leveraging information from a source structure, characterised by a rich class of damage labels, to improve inferences on a target structure with limited knowledge. The goal is to train a machine learning algorithm on a bridge undergoing damage and to afterwards transfer the available labelled damage-state data across the members of the investigated population. Given possible differences exhibited by each structure, a domain adaptation technique in the field of statistic alignment, called Normal Condition Alignment (NCA), is applied to match different distributions in a shared feature space. The methodology is validated on a heterogeneous population composed of two numerical bridges of different geometry and materials, representing the Z24 and the S101 benchmark bridges. Finite Element Models are built to simulate healthy conditions and several damage cases. The natural frequencies describing such scenarios are considered as damage-sensitive features and thus employed to characterise the two domains and fed to a supervised learning-based classifier. The presented approach is deemed effective to provide mappings that allow the exchange of health-state information from source to target datasets, becoming a promising approach to be applied within a population of real bridges.
Random vibration response-based damage diagnosis for a population of composite aerostructures under varying operating conditions and uncertainty: experimental assessment
Authors: Spiliotopoulos P E , Fera F T , Saramantas I E , Ofir Y , Kressel I , Tur M , Konis P , Kriatsiotis I M , Sakellariou J S , Fassois S D , Giannopoulos F , Spandonidis C , Tzioridis Z 
Published: June 2024

Abstract
The problem of random vibration response-based damage diagnosis (detection & type characterization) for a population of 26 composite aerostructures under varying operating conditions and uncertainty is experimentally investigated. Four damage scenarios are considered in a small sample of the population: Two distinct sizes of a square-shaped delamination and two energy levels of an impact-induced damage. The analysis of the intact (healthy) population indicates rich dynamical information within the range of 0 – 2 kHz with the uncertainty factors, due to manufacturing and experimental setup discrepancies compounded with the temperature and excitation variability, to be strong enough to “mask” the effects of the considered damages, thus leading to a highly challenging damage diagnosis problem. Damage detection is tackled via two unsupervised and robust data-driven vibration-response-only methods: The first is based on the Multiple Model (MM) and the second on the Hyper-Sphere (HS) based frameworks, while damage type characterization is tackled via a hierarchical classification. All methods utilize the Multiple Input Single Output Transmittance Function (MISO-TF) in order to properly compensate for the effects of the varying excitation. The results based on hundreds of test cases indicate impressive detection performance even for the smallest level damage scenarios, with the two detection methods complementing each other, as well as very good damage type characterization, accuracy despite the limited number of damaged aerostructures.
An Exploratory Study on Data-Driven Vibration Based Damage Detection and Characterization for a Population of Composite Aerostructures
Authors: Saramantas I E , Konis P E , Kriatsiotis I M , Ofir Y , Kressel I , Spiliotopoulos P E , Fera F T , Sakellariou J S , Fassois S D , Giannopoulos F , Spandonidis C , Tur M , Tzioridis Z 
Published: June 2024

Abstract
This study investigates the effectiveness of four robust to uncertainty data-driven methods of machine learning type for random vibration response-only damage detection and characterization in a population of composite aerostructures under various uncertainty factors. The employed methods are based on the Multiple Model (MM) and the Hyper-Sphere (HS) frameworks using Multiple Input Single Output AutoRegressive with eXogenous excitation Transmittance Function (MISO TF-ARX) models that may account for excitation uncertainty. The methods capabilities are explored via numerous Monte Carlo simulations using digital, Abaqus based, models allowing for maximum flexibility in experimentation. Based on these, a population of 90 Carbon-Epoxy square hollow beams with lightweight composite aerostructures properties is employed taking into account manufacturing, temperature and excitation uncertainty. In addition, early-stage debonding and delamination damages are inserted to a portion of the population at two levels per damage and at two distinct locations for the methods evaluation. All considered damages affect slightly and similarly the structural dynamics overlapping with the effects caused by uncertainty in order to increase the diagnosis (detection & characterization) difficulty and explore the methods’ performance limits. The results indicate almost perfect detection in all considered damage scenarios except from the low level debonding, while damage characterization (type, location and level), which is performed via a hierarchical classification scheme, is very promising.
Application of fixed-lag Kalman smoother for nonlinear state estimation
Authors: Al-Hagri Ammar , Caglio Luigi , Stang Henrik , Katsanos Evangelos 
Published: June 2024

Abstract
During their lifetime, civil structures and infrastructures are subjected to environmental and human-induced hazards that may lead to structural damages or, in some cases, to full collapse. Such failures are associated with increased risk for life and limb, while also environmental and monetary losses may be triggered with adverse societal footprint. Consequently, it is highly relevant to screen reliably the condition of the structural systems and, if necessary, to intervene to safeguard the structural integrity and avoid undesirable failures. To this end, several structural health monitoring schemes are becoming a key element for various structures and infrastructure systems, enabling the continuous assessment of their state. The latter, in turn, can allow for detecting damages at an early age, avoiding extensive failures or even sudden collapse. In order to identify the failures due to excessive loading events, measured time-varying responses are frequently used as the basis for damage detection. However, in reality, practical restrictions exist in mounting sensors on all the locations of the structure that an engineer would prefer to know the structural response. For example, sensing data from the submerged part of an offshore platform is commonly not available, while the latter is also valid for various locations in bridges where the access is limited to apply and operate sensors. Therefore, the available measured data is mostly incomplete since only a limited number of sensors can be employed. Additionally, various noise sources usually pollute the sparsely measured responses, and the overall quality is compromised. As a remedy to this challenge, a new framework is proposed herein to aid in estimating reliably the nonlinear dynamic response of a structural system with the use of limited and noisy measurements. Especially, a Kalman smoothing technique called the fixed-lag algorithm is coupled with an augmented extended Kalman filter, and such an integrated scheme is anticipated to increase the state estimation accuracy. The current study adopts a simulation framework as the testbed for the proposed scheme. The use of the FE software OpenSees allows the numerical nonlinear modeling of the structural system subjected to time-varying loads. Compared with the ones calculated directly from nonlinear response history analysis, the estimated responses based on the proposed scheme are found to be associated with increased accuracy.
Vibration-based FE-model updating for strain history estimation of a 3MW offshore wind turbine tower
Authors: Amador Sandro D R , Rasmussen Søren , Brincker Rune , Rex Simon , Skog Mathias , Gjødvad Johan F 
Published: June 2024

Abstract
At present, the fatigue-life design of Offshore Wind Turbines (OWTs) is based on design assumptions accounting for significant uncertainties both on loading and fatigue-resistance of different vulnerable structural details. Monitoring of strain histories can potentially have a significant positive impact when estimating the remaining life of OWTs, as the uncertainties are reduced. However, strain-based monitoring approaches present the important drawbacks, that only instrumented locations can be assessed and that there tend to be accessibility issues associated with the installation process. In this paper, the initial results of a novel methodology developed to estimate strain time histories are presented. The underlying idea of such an approach is to combine an updated FE model with the vibration response histories acquired with only a few sensors to estimate the strain histories at any location of an OWT tower.
Spatiotemporal extrapolation for vibration to strain estimation using domain adaptation
Authors: Eshkevari Soheila Sadeghi , Eshkevari Soheil Sadeghi , Dabbaghchian Iman , Marasco Giulia , Pakzad Shamim N. 
Published: June 2024

Abstract
Structural health monitoring relies on direct measurements from the structure for a variety of necessary investigations including operational modal analysis, life-cycle assessment, damage detection, and model updating. Although, because of complications and inherent inaccuracies in this direct measurement, engineers have been seeking to facilitate the data collection using machine learning-based approaches for a more robust strategy. In this work, a novel transfer learning framework is proposed to enable spatiotemporal strain estimation from acceleration measurement of the bridge for unmeasured or faulty locations and times. This task is executed by extraction of time-dependant and location-dependant contents of collected vibration signals and reconstruction of strain signal merely by providing the desired time and location. The framework has been verified on a simulation case study and showed high accuracy signal reconstruction metrics.
Instrumentation and Strain Monitoring of BFRP Bars in Pedestrian Bridge Deck Link slab
Authors: ElSafty A , Okeil A , Fletcher J , Fowler T , Jones D , Nolan S 
Published: June 2024

Abstract
Eliminating bridge deck expansion joints is considered a feasible alternative for addressing problems associated with deck joints, which results in reduced maintenance and improved bridge-deck design life. Link slabs introduce partially continuity for bridge girders and improve the seismic performance, wind and storm wave loads resistance. It also leads to lower cost, improved riding quality, and improved structural integrity, and may lead to longer spans. The goal of this study is to monitor the performance of a link slab employed in a pedestrian bridge where Basalt Fiber Reinforced Polymer (BFRP) bars were used for its reinforcement. The pedestrian bridge was constructed from simple-span concrete girders that were made continuous by creating the continuity link slab between the girders’ ends. The paper will present the link slab instrumentation, monitoring, and instrumentation system employed in the project. Strain, temperature, and elongation data were collected for the purpose of investigating the performance of link slabs. Two groups of sensors were used in this project, namely surface-mounted and embedded sensors. Monitoring of the link slab occurred during and after concrete casting. Several types of sensors including vibrating wire sensors with integrated thermistors were used in this study. The sensors were installed strategically to detect and capture strains in the BFRP bars, strains in the concrete link slab, and movement of the gap between adjacent girders’ ends. A data acquisition system recorded strains and deformations at regular time intervals. Data was collected during service over two periods of approximately 3 months each. The collected data was in effect a result of temperature and shrinkage of the concrete and did not include any significant live load effect, i.e., the bridge was not load tested with vehicular loads. Sensor measurements were corrected for ambient temperature changes as per the manufacturer’s manual. Based on the monitoring data, it can be concluded that the strains experienced by the sensors in the link slab BFRP were small compared to the BFRP rupture strain. After approximately 90 days since the initial casting date, the average strain recorded in the mid-joint gauges did not significantly change indicating that minimal creep or shrinkage restraint was experienced by the link slab. The maximum daily strain change due to thermal effects was about 500 microstrain. The cyclical nature of thermal effects could contribute to concrete cracking over time in the link slab if tension stresses build up due to global shrinkage and creep restraint of the connected Florida Slab Beams (FSB) spans.
Advanced signal processing methodology of vibration response data toward Structural Health Monitoring purposes
Authors: Ferrari R , Zola M , Cornaggia A , Rizzi E 
Published: June 2024

Abstract
This paper outlines a comprehensive and consistent methodology for signal processing analysis of vibration response data, applicable for final structural monitoring and identification purposes. The methodology combines classical and advanced techniques, including, in its pre-processing phase, the adoption of a Time Domain Compression (TDC) technique and the application of an AutoRegressive Moving Average (ARMA) modeling approach. The TDC technique removes lower-quality subsamples from the full data set, resulting in a higher-quality modified signal that may display a weakly stationary character. The ARMA modeling approach enhances the understanding of the response signals by modeling unknown source inputs; as a peculiarity, the inherent polynomial function applied to a white noise source in the model is interpreted as a filtering term that transforms the source into a non-white noise configuration, enabling the effective deciphering of the structure transfer function features. The research is part of a more comprehensive case study concerning the structural evaluation of a historical reinforced concrete arched bridge over the Adda river in Lombardy, Italy. The focus of this paper is specifically on the application of the TDC and ARMA techniques to the signal response data collected from the bridge under operational conditions.
Bayesian operational modal analysis in time domain using Stan
Authors: Cara Javier 
Published: June 2024

Abstract
Modal identification consists of determining the natural frequencies, damping ratios and mode shapes of a build structure using measured dynamic data. The Bayesian approach is very appealing when the goal is not only to estimate the modal parameters but also their uncertainty, calculated from their joint probability distribution. This paper presents an example of Bayesian modal identification in time domain using the state space model and the software Stan.
Modal analysis for damage identification of partially continuous multi-span bridges
Authors: Tomassini E , García-Macías E , Reynders E , Ubertini F 
Published: June 2024

Abstract
Among the compendium of highway civil infrastructures built in the last decades, many repetitive or quasi-periodic configurations can be found, such as multi-span simply supported bridges. Structural Health Monitoring (SHM) strategies for this kind of structures should properly consider such structural periodicity. Literature studies already demonstrated that multi-span bridges are particularly challenging target structures for Operational Modal Analysis (OMA) since the modal properties typically appear as dense clusters of poles with closely spaced frequencies and mode shapes with similar wavelengths. Hence, this circumstance considerably hinders the identification of physical poles using standard stabilization diagrams. The imperfect independence between spans is typically due to the presence of a continuous deck over the simply supported girders, weak deck/asphalt connections and/or imperfect expansion joints. In this context, the coupling degree of spans manifests through global mode shapes and frequencies spanning between the limit cases of simply supported and continuous multi-span conditions. In this light, this work proposes a novel method to assist the clustering of modal poles extracted using OMA. The proposed method is based on the analytical modal solution to the free vibration problem of multi-span beams with weak rotational coupling between adjacent spans. The developed method is validated through the modal identification of a real-world in-operation multi-span reinforced-concrete girder bridge.
A new fully automated operational modal analysis algorithm intended for large civil structures
Authors: Dederichs A C , Øiseth O 
Published: June 2024

Abstract
A new fully automated operational modal analysis (AOMA) algorithm based on the time-domain covariance-driven stochastic subspace identification (cov-SSI) system identification method is proposed. The new algorithm is intended for large civil structures, such as long-span suspension bridge for which an application example is included. It is shown that the new algorithm is capable of consistently detecting all expected structural modes of the Hardanger bridge, without requiring any prior tuning or parameter selection. The dominating three-step approach to AOMA algorithms in published literature on this topic is highlighted and used as a basis for the new algorithm; it first removes spurious poles using a Gaussian mixture model by analysing their distance to their nearest neighbour. Secondly, hierarchical clustering is used to regroup similar poles, and finally, the groups representing physical modes are selected through a similarity analysis of the size of hierarchical clusters.
Characterization and dynamic behaviour of Masonry Newly Concept Vault realized by 3d Printer
Authors: Sabbà M F , Rizzo F , Giannoccaro N I , Mansour S , La Scala A , Foti D 
Published: June 2024

Abstract
Masonry vaults represent one of the most recurrent types of horizontal structural elements in architecture in European countries, even in areas characterized by a high level of seismic risk. Therefore, their structural evaluation remains of primary importance. This paper proposes to apply already consolidated structural analysis methodologies on a contemporary vaulted space; in particular, on a small-scaled vault model. The aim, therefore, is to investigate the behaviour of a newly concept vault generated starting from the “Flat vault of Abeille” patented in 1699 starting by a scaled model made by 3D printer. The identification of this “new type” of vault is essential to design it correctly and to optimize the geometry for structural purposes. So, the dynamic identification of 1:8 small-scale vault model in 3D printing was studied. All the vault blocks were made in Polylactic Acid (PLA), have an infill of 70% and are assembled with dry joints. The paper describes the process to acquire the environmental and forced vibrations and to analyse them with the Operational Modal Analysis (OMA), identifying the natural frequencies and the mode shapes. Subsequently the results were compared with those of numerical distinct element method (DEM).
Modal Identification of a Steel Platform Specimen with a Subspace Implementation of the Poly-reference Complex Frequency Method Formulated in Modal Model (pCF-MM)
Authors: Amador S D R , Brincker R 
Published: June 2024

Abstract
When performing modal identification of structural systems, the main goal is to accurately extract the modal properties from the measured vibration data. The system identification can be carried out either in time or frequency-domain depending on the primary data used in the identification process, i.e., a time-domain free decay or frequency-domain function. In this paper, a subspace implementation of the new poly-reference Complex Frequency formulated in Modal Model (pCF-MM) is introduced. The technique operates in the frequency domain and uses either the Frequency Response Function or the Half Spectrum as primary data. The idea behind the subspace implementation of the pCF-MM is to use different subspaces of the observability and frequency-domain controlability matrices, carry out eigen-system realizations to estimate the modal properties corresponding to each chosen subspace. In order to illustrate its robustness, the subspace-driven pCF-MM is applied to the vibration data of a steel platform specimen to estimate its first vibration modes.
Characterization of the static and dynamic response of a post-tensioned concrete box girder bridge with vertically prestressed joints showing vertical deflections due to concrete creep deformation
Authors: Meoni A , Galassi Sconocchia G , Mariani F , Ierimonti L , Castellani M , Tomassini E , Venanzi I , Ubertini F 
Published: June 2024

Abstract
Time-dependent phenomena, such as creep of concrete, induce stress/strain redistributions in post-tensioned concrete bridges that must be taken into account during their design. The underestimation of time-dependent effects can negatively influence the load-deformation behavior of this bridge typology, leading to unexpected deformations of the deck, serviceability issues, and collapses. Numerical models able to simulate time-dependent phenomena should also be used to properly conduct the structural assessment of existing post-tensioned concrete bridges. However, before the introduction of specific materials’ constitutive laws to account for time-dependent phenomena, numerical models should be calibrated and validated according to the information gathered by testing the structures they represent, to carry out reliable numerical simulations. The paper presents the preliminary results obtained from the structural assessment of a post-tensioned concrete box girder bridge with vertically prestressed internal structural joints, which exhibits marked deflections of the box girder. A numerical model of the case study bridge was constructed, hence non-destructive and partially destructive tests were carried out on the structure to characterize its structural response. Overall, the resulting numerical model represents a solid basis for conducting more advanced analyses, such as those investigating the influence of time-dependent phenomena on the long-term structural response of the structure.