bluuebunny/crossref_metadata_2025 huggingface dataset:
Features of marketing in the pharmacological business in the postcovid period
Authors: Novikova Mariya
Published: December 2023

Pharmaceutical marketing is an activity for the development, production and marketing of medicines based on a preliminary study of the needs of the pharmaceutical market.

The active development of the digitalization of the pharmaceutical market was given by 2020, it marked the beginning of remote trade in medicines, closer contacts of company representatives with medical professionals through videoconferences, an increase in e-mail newsletters, an explosive growth in the volume of advertising on the Internet, the development of native advertising.

Currently, promotion via the Internet plays a huge role, but the standard way of making a purchase decision remains. The buyer still goes through the stages of making a purchase: knowledge, interest, research, action, commitment. But the path from obtaining primary knowledge about the product to making a purchase becomes more difficult, since in addition to advertising, the recommendation of a doctor or pharmacist, the buyer studies the product using the Internet.
CLINICAL CASE OF THROMBOANGIITIS OBLITERANS IN A MIDDLE-AGED MAN
Authors: Il'ina T.
Published: December 2023

The article presents a clinical case of a patient with thromboangiitis obliterans with such obvious clinical manifes tations as cyanosis, numbness of the toes, intermittent claudication, trophic changes in the skin of the hands, feet and legs. Despite the verified diagnosis and the patient’s adherence to treatment, a progressive course of the disease is noted.
UNSUPERVISED DAMAGE DETECTION FOR SMART EXTRATERRESTRIAL HABITATS USING AUTOENCODERS AND INFORMATION FUSION
Authors: WANG ZIXIN , JAHANSHAHI MOHAMMAD R. , BILIONIS ILIAS , FU YUGUANG , MAGHAREH AMIN , DYKE SHIRLEY J
Published: December 2023

In the context of smart and resilient extraterrestrial habitats, the structural health monitoring (SHM) of habitats is crucial and challenging due to the harsh space environments. To this end, a novel anomaly detection framework is developed based on active sensing and information theory. Active sensing involves the excitation of the structure at specific locations and collecting acceleration data using sensors. The collected data from different excitation points and sensor locations are analyzed, and the extracted information is fused to further enhance anomaly detection. More specifically, an unsupervised anomaly detection framework using autoencoders (AEs) has been developed. Continuous wavelet transforms (CWTs) of acceleration signals are utilized to train AEs. Information fusion strategies are proposed to enhance the robustness of the approach to both aleatoric and epistemic uncertainties. Two unsupervised learning approaches developed by standard AE and variational autoencoder (VAE) are systematically compared. The numerical study based on the ASCE benchmark model and the experimental study based on a geodesic dome testbed have been carried out to validate the performance of the proposed framework and study its limitations. The framework's ability to extract information from multiple sources allows it to identify anomalies that might have been missed by traditional detection methods. For instance, it is shown that the proposed approach increases anomaly detection accuracy by up to 39.8% under a relatively small damage scenario compared to state-of-the-art approaches.
GRAPH CONVOLUTIONAL NEURAL NETWORKS BASED STRAIN ESTIMATION
Authors: MARASCO GIULIA , ESHKEVARI SOHEILA  SADEGHI , PAKZAD SHAMIM N.
Published: December 2023

Infrastructural owners have to manage and control a huge number of structures that are subject to many phenomena that can alter their performance. During their service life, infrastructure like bridges are exposed to cycles of dynamic loads, causing potential fatigue failure. For the life-cycle assessment aim, most procedures require the use of strain data. The acquisition of strain data is not easy, especially in critical positions, and the high costs would restrict the wide use of such procedures. This study proposes a technique based on the graph theory to obtain strain from acceleration data. As shown in neuroscience and biological fields, this type of architecture is able to achieve good outcomes not relying on handcrafted design parameters. Besides, their capacity of grasping spatio-temporal dependencies turns out to be a key point in the damage detection framework. A numerical case study involving a two-span beam has been investigated to explore the potentiality of this architecture for acceleration-strain conversion. Here the graph nodes represent the sensor locations and the combination of geometric and signals aided to create a spatial-temporal GCN architecture. The obtained outcomes are promising and make the framework extendable to real case studies.
BIDIRECTIONAL LONG SHORT-TERM MEMORY NETWORK FOR MAGLEV BRIDGE ACCELERATION DATA RECONSTRUCTION
Authors: JIANG GAO-FENG , WANG SU-MEI , NI YI-QING
Published: December 2023

Maglev is a developing transportation mode in the recent years, and its safety and stability are required to be ensured by structural health monitoring system. However, the data measured by some sensors may be partially or totally unavailable due to the external electromagnetic interference or sensor failure. While, in some cases, those missing data are important to identify and analyze damage of maglev bridges. Since the structural dynamic characteristics of maglev bridge are complicated, the data reconstruction of those sensors becomes challenging. Therefore, this study proposes a bidirectional long short-term memory (BiLSTM) network to accurately reconstruct the maglev bridge acceleration data. The design of long short-term memory helps to sufficiently exploit the time series data and learn the hidden features of maglev bridge acceleration data between sensors. The bidirectional architecture enables the network to simultaneously learn the time series data from past and future, which is beneficial to extract more hidden features. A dataset collected from an in-site experiment for maglev bridge is used to verify the feasibility of the proposed method. The lost acceleration data from abnormal sensors is predicted by the acceleration data recorded from normally operating sensors. The results shows that the difference between predicted and true acceleration data is at a very low magnitude. Consequently, the proposed method can be applied for the high-performance reconstruction of maglev bridge acceleration data.
LONG-PERIOD DYNAMIC CHARACTERISTICS OF EMBEDDED TRACK USING MACHINE LEARNING
Authors: HE YUANPENG , ZHANG YANG , NI YI-QING
Published: December 2023

Different from traditional fastener systems, embedded track is a rail placed in the groove and wrapped by a variety of polymer materials, thus realizing the longitudinal continuous support and vibration noise reduction. Due to its superior dynamic characteristics, it has been initially used in trams, subways and high-speed railways. With the promulgation of the Noise Law, its demand is also increasing. However, its structure and mechanism are relatively complex, and its dynamic characteristics changes with the service life. In addition, its performance is difficult to measure directly and service life is as long as 30 years or more. In order to analyze the dynamic characteristic changes of the embedded track throughout its life cycle, fatigue tests are performed by subjecting the embedded track to sinusoidal excitation of different amplitudes and periods. This allows to simulate its service process during the whole life cycle. Meanwhile, the vibration response of embedded track at different stages is collected. Unfortunately, it is difficult to judge the performance and state of embedded track according to the vibration response directly. In order to solve this problem, this paper proposes an embedded track long-period dynamic response analysis method based on machine learning. This method can evaluate the performance change of the embedded track without any label only based on the dynamic response. Among them, self-supervised deep learning networks are used to autonomously extract deep features of the vibration response. These features are then classified by clustering algorithms into different phases of the service life. Finally, the change law of vibration and noise performance of embedded track in different stages is explored. The proposed method can determine the performance status of the pre-embedded track based on the field vibration response test results. It also estimates the decay process of track performance with service life and determines the maintenance cycle according to the performance requirements.
SMART STRUCTURAL HEALTH MONITORING FOR HIGH-SPEED RAILWAY BRIDGES
Authors: CASTRO-TRIGUERO RAFAEL , SEVILLA RAFAEL GALLEGO , LEON-GARCIA FERNANDO
Published: December 2023

The management of ageing civil infrastructure represents one of the most pressing challenges of modern societies. The last Infrastructure Report Card of the American Society of Civil Engineers (2017) estimated that 39% of the more than 600.000 bridges in USA are over 50 years old and estimated the rehabilitation costs at $123 billion. A similar picture emerges across Europe. Spain counts more than 2.606 km of high-speed railway, and a total of 207 critical bridges. An important number of them presents of one form of degradation or damage. The replacement rate is very low and that means an increase of ageing bridges in the next years. Despite large financial efforts have been dedicated to research in this field, the methodology has not been successfully completed/integrated, and life-extension of bridges is still sparsely implemented in practice. The last report of the European Joint Research Centre warned about the weak link between research and the wide-scale adoption of Structural Health Monitoring (SHM) technologies. The economic efficiency of the bridge is the total life cycle cost divided by its service life. New alternatives such as reliability-centered maintenance and reconditioning programs play an ever increasingly important role. However, these improvements are just the first glimpse of an ambitious and promising opportunity: bridge life-extension. To address these deficiencies the present methodology involves important advances on the real application of: SHM methodologies for high-speed railway ageing infrastructures, smart sensors, energy autonomy of the long-term monitoring system, and structural prognosis based on data-driven decision making.
A NON-PARAMETRIC MIXED LEARNING TECHNIQUE FOR MITIGATING ENVIRONMENTAL EFFECTS ON STRUCTURAL MODAL FREQUENCIES
Authors: ENTEZAMI ALIREZA , MARIANI STEFANO
Published: December 2023

Health monitoring of civil structures via machine learning is a powerful approach to the early detection of any damage pattern. Besides structural damage, also environmental and operational variabilities are known to affect the inherent structural properties. Although the induced variations in the monitored properties are not harmful, their confounding influence can lead to economic and human losses. For these reasons, a novel unsupervised learning strategy is here proposed, aiming to properly account for the environmental effects on the structural modal frequencies. The offered solution is a non-parametric mixed learning strategy resting on hierarchical clustering, local nonnegative matrix factorization, and Mahalanobis-squared distance (MSD). By means of the hierarchical clustering, training data consisting of modal frequencies relevant to the undamaged condition are subdivided into local clusters, which are then exploited in order to get rid of the environmental effects. The reconstructed data are finally used to train a non-parametric novelty detector based on the MSD, to obtain scores for decision making regarding the current state. To validate the proposed method, a set of modal frequencies of a steel arch bridge in its long-term monitoring has been considered; results show that the proposed methodology is effective in taking aside the environmental variability from the time history of the collected modal frequencies of the structure.
STATIC AND DYNAMIC BRIDGE MONITORING WITH DISTRIBUTED FIBER OPTIC SENSING
Authors: STRASSER LISA , LIENHART WERNER , WINKLER MADELEINE
Published: December 2023

Structural health monitoring (SHM) of bridges and other critical infrastructure is progressively gaining more importance, especially since many bridges in Central Europe and North America are coming to the end of their design life time. Efficient monitoring is therefore necessary to ensure safe operation. In order to measure not only fast changes due to traffic loads but also slow changes caused by temperature or degradation, several different sensor types are usually needed. Distributed Fiber Optic Sensing (DFOS) can provide both static and dynamic information. This article focuses on the monitoring of a steel-concrete composite highway bridge in Austria. Two of the seven spans were equipped with fiber optic sensing cables. By using different DFOS techniques such as Distributed Acoustic Sensing (DAS) and Brillouin Optical Frequency Domain Analysis (BOFDA), both dynamic and static monitoring can be realized seamlessly along the sensing cables. The combination of both fiber optic measurement techniques allows the determination of the vibration behavior of the bridge where not only the eigenfrequencies can be observed, but also slow changes caused by external influences such as temperature. We demonstrate that individual vehicles can not only be identified in DAS measurements, but also characterized in terms of mass and speed. To verify the static and dynamic DFOS results, point-wise sensors such as Robotic Total Stations (RTS) and accelerometers are used as reference.
MICROSECOND MODEL UPDATING FOR 2D STRUCTURAL SYSTEMS USING THE LOCAL EIGENVALUE MODIFICATION PROCEDURE
Authors: OGUNNIYI EMMANUEL A. , VEREEN ALEXANDER B. , DOWNEY AUSTIN R. J.
Published: December 2023

Systems that experience high-rate dynamics, such as blast or impact, are susceptible to rapid alterations that could result in loss of life and financial investments. These systems are characterized by a high dynamic response with a high-rate (< 100 ms) and high amplitude (> 100 g³). A system exposed to high-rate dynamic environments is frequently prone to rapid plastic deformation, which can cause structural, electrical, and sensor damage. A feedback loop of fast-acting actuators empowered with rapid state estimates can be utilized to stop further harm. The state estimator must be quick and resilient to the significant uncertainties, non-stationarities, and strong disturbances associated with high-rate dynamic systems. A model for 2Dimensional systems is developed to demonstrate high-rate tracking or estimation of a structure where a change in stiffness at locations on the system represents damage. The Local Eigenvalue Modification Procedure (LEMP) algorithm is applied to solve the system’s equation quickly and efficiently within a set latency for state estimation. LEMP utilizes a single generalized eigenvalue solution for the initial system and simplifies altered state equations by transforming them into modal space, isolating the DOFs that contribute to the changes between states, and defining equations in terms of the initial state, thereby reducing computational time. This preliminary work develops a 2D finite element model using classical plate theory. A 2D model simulation of the plate’s initial state is carried out on Abaqus and compared to the analytical model formulated solved using the generalized eigenvalue approach to test the formulated model. The changes made to the plate are then solved using LEMP to avoid solving the time-consuming eigenvalue solution. In this work, the change in the system is demonstrated by change in stiffness at different locations on the plate. Results report the performance metrics for the considered case. The approach’s applicability to deployment on edge computing systems for real-time model updating of structures operating in high-rate dynamic environments is discussed.
SENSITIVITY TO AXIAL STRESS AND TEMPERATURE OF LOCAL RESONANCES IN RAILS
Authors: WU YUNING , ZHANG KEPING , ZHU XUAN , POPOVICS JOHN
Published: December 2023

Measuring rail thermal stresses or rail neutral temperature (RNT) in continuous welded rails (CWRs) is a challenging task for the railroad industry, especially in a nondestructive and nondisruptive manner. This paper examines the potential of local resonances for thermal stress measurement in rails. Local resonances associated with zero-group velocity (ZGV) and cutoff frequency points usually demonstrate sharp resonances in amplitude spectra, which can be utilized for Nondestructive Evaluation (NDE) and Structural Health Monitoring (SHM), and we previously reported their existence in rails. In this study, we promote local resonances by bonded piezoelectric elements on a short rail sample. Two tests were performed: (i) the sample is subjected to stepwise increasing uniaxial compressive loads in a constant temperature environment, and (ii) the same sample is free to expand and subjected to rising temperatures in an oven. By measuring local resonant frequencies, we quantified the sensitivity of the resonant frequencies to axial stress and temperature. The results show that appreciable sensitivities of the local resonances are found under varying stress and temperature levels and can be utilized for in-situ rail thermal stress determination.
UNCERTAINTY QUANTIFICATION OF THICKNESS ESTIMATION VIA FULL-FIELD ULTRASONIC INSPECTION
Authors: SHAH NEEL , JACOBSON ERICA M. , FLYNN ERIC B. , WACHTOR ADAM J.
Published: December 2023

Acoustic steady-state excitation spatial spectroscopy (ASSESS) is a full-field ultrasonic inspection technique that can be utilized for structural health monitoring applications. ASSESS can rapidly identify damage or defects over a 360o field of view of a structure using geometry mapping and a scanning laser Doppler vibrometer (LDV) – enabling inspection and characterization across multiple large surfaces in a single measurement. However, variables like the incidence angle between the surface and LDV beam change when inspecting large or geometrically complex structures; greatly affecting the signal-to-noise ratio (SNR) of the measurement and the accuracy of damage estimation to an extent that has not previously been quantified. This work experimentally quantifies the uncertainty of thickness estimation derived from full-field steady-state wave fields over various incidence angles to provide trusted operating bounds. A rotation stage and hexapod assembly were used to orient an aluminum plate with manufactured thickness losses to up to 80o both horizontally and vertically in increments of 5o. The plate specimen was designed with an assortment of manufactured damage, including discrete regions with both continuous and discontinuous material thickness loss. An affixed transducer excited the plate with a 199 kHz tone and an LDV recorded local surface response velocities. A LiDAR was used to measure the inspection surface geometry and random sample consensus (RANSAC) planar extraction was utilized to determine the orientation of the specimen with respect to the LDV and correct perspective distortions in the wavefield. This work lays an essential foundation for identifying physical limitations of the measurement system for scanning large and complex structures and providing quantified uncertainties for detected damage within a trusted operating range – an imperative step for adoption of this inspection technique within highly standardized industries.
ANALYSIS OF ACOUSTIC WAVE GENERATION BY LOW FREQUENCY AIR-COUPLED TRANSDUCERS
Authors: WANDOWSKI TOMASZ , RADZIENSKI MACIEJ , MALINOWSKI PAWEL , KUDELA PAWEL
Published: December 2023

In this paper phenomenon of acoustic waves generation in the air by low-frequency air-coupled transducers (ACT) is investigated. Numerical modelling of acoustic waves propagation in the air was investigated in the COMSOL Multiphysics software. The propagation of acoustic waves generated by a single ACT was modelled, to consider its angular pressure characteristics. Furthermore, the process of acoustic waves generation by multiple ACTs was investigated. Numerical results were validated by experimental measurements based on the scanning laser Doppler vibrometry using the acousto-optic phenomenon. The results of this research are focused on the elastic wave generation in the structures by ACT and are targeted towards non-destructive testing.
ANALYSES BY GLOBAL-LOCAL METHOD OF ULTRASONIC GUIDED WAVES PROPAGATION IN PRISTINE AND DEFECTIVE PLATES FOR ACCURATE QUANTITATIVE SHM
Authors: CAPRIOTTI MARGHERITA , ZHANG MINGYUE , ESCALONA LUIS , SCALEA FRANCESCO LANZA DI , SPADA ANTONINO
Published: December 2023

The efficient and accurate modeling of ultrasonic guided waves (UGWs) can be a very effective tool to optimize UGWs inspections and enable defect characterization when performing structural health monitoring (SHM). Among the several strategies, hybrid methods provide the capability of representing accurately the hosting structure and defect configurations, while leveraging the decreased computational costs of reducedorder and/or semi-analytical methods. In this work, the Global-Local method is used to simulate UGW propagation in composite plates with defects, to study the effect of the incident mode interaction with the scatterer and its features (size, location, local zone relation). To do so, the evolution of the method is presented to account for the inclusion of evanescent modes and to accommodate the forced solution framework. In the first improvement, the role of evanescent modes will be discussed with respect to accuracy and computational cost considerations, providing guidelines for effective UGW modeling in composite materials. In the second upgrade, the 2D time-space response to different spatial and temporal sources is observed and analyzed in terms of its spectral content, to differentiate pristine and defective conditions, in composite materials with increasing defect severity. These improvements in the numerical framework of the Global-Local approach are significant to enable quantitative SHM and prognostics by accurate and efficient predictions of UGW scattered responses, and to advance defect characterization by UGWs by analyses of UGW data.
NON-DESTRUCTIVE EVALUATION OF FSW TOOL PLUNGE DEPTH IN THIN METALLIC SHEET WELD USING ULTRASONIC GUIDED WAVES
Authors: MEHTA MANISH KUMAR , GAUTAM GOVINDA , JOGLEKAR D. M. , DWIVEDI D. K.
Published: December 2023

Due to their high strength-to-weight ratio, thin aluminium alloy sheets are widely used in industries such as automotive, marine, railway, and aerospace. Welding has been discovered to be an effective method of joining, particularly in comparison to mechanical fasteners and semi-joining processes. However, fusion welding techniques can be vulnerable to hot cracking, which can result in solidification and liquation cracking. For lap welded joints in thin metallic sheets, friction stir welding (FSW) has proven to be very efficient. Non-destructive testing (NDT) methods are utilized to examine weld morphology. The ultrasonic method has demonstrated the ability to measure weld penetration depth in thick metallic plates using bulk and surface waves. However, the tool plunge depth (TPD) in FSW, which is analogous to the weld penetration depth of fusion welding, plays a crucial role in the weld strength of thin structures that has not been investigated using non-destructive methods. This study aimed to predict the optimum tool plunge depth for sound welding in thin structures by employing a guided Lamb wave-based non-destructive method. At a low frequency of 25kHz, only anti-symmetric (Ao) mode was transmitted through low TPD weld and through optimum TPD weld both Ao and symmetric mode (So) was getting transmitted. However, through high TPD weld both Ao and So was getting transmitted as well as extra anti-symmetric (AoAoc or SoAoc) mode was observed due to mode conversion. This different scattering behaviour of Lamb wave signals for varying tool plunge depths was analysed to predict the FSW tool plunge depths with accuracy.
APPLICATION OF DAMAGE DETECTION OF STEEL STRUCTURE LAMB WAVE MODAL SUPERPOSITION IMAGING BASED ON SCANNING LASER VIBRATION MEASUREMENT
Authors: WANG ZIPING , LI BINGQIAN , CUI HANGRUI , FERNÁNDEZ-LÓPEZ ANTONIO , GÜEMES ALFREDO
Published: December 2023

Due to their high strength, high-temperature resistance, good toughness, and plasticity, steel structural plates are widely used in major engineering structures such as high-speed rail, vehicles, and civil engineering. However, they are easily damaged by external loads and impacts during long-term service. Aiming at the shortcomings of dispersion and multi-modal effects in the current Lamb wave detection, and since damage information cannot be effectively identified, this paper studies, through simulation and experiments, the Lamb wave driven by a sinusoidal modulation five-peak wave signal in a steel structure plate with flat-bottomed holes. The finite element numerical model of the propagation was established, and the corresponding damage detection experimental platform including steel and aluminum boards was built. The experimental data were obtained by Scanning Laser Doppler Vibrometer (SLDV). It can be seen from the experimental results that under the condition of the same frequency, the damage reflection energy of the two different modes is relatively obvious. Through the data fusion of the two modes, the results obtained are more accurate than the damage imaging results of the single mode in the frequency wavenumber (f-k) domain. Finally, the damage location experiment of one through hole in the steel board and the double flat bottom holes in aluminum plate are carried out to verify the feasibility of the algorithm in isotropic plate.
DEBOND LOCALIZATION IN HONEYCOMB CORE SANDWICH COMPOSITES USING A0 GUIDED WAVE MODE
Authors: MITRA AUROVINDA KUMAR , JOGLEKAR DHANASHRI M.
Published: December 2023

Honeycomb core composite sandwich structures (HCCSSs) are widely employed in the marine, defense, and aerospace industries due to their lightweight design, the superior ratio of strength-to-weight and high load-bearing capabilities. Regardless of such commendable advantages, the skin-core junctions are susceptible to debond damage from intensive loading on these structures that may threaten the safety and structural integrity of the assembly. In this study, a novel framework for the guided wave based non-destructive evaluation (NDE) of a three-dimensional HCCSS is presented. The total focusing method with full matrix capture (TFM-FMC) technique is employed to detect and quantify debond at multiple locations in the finite element model of the three-dimensional HCCSS. The fundamental antisymmetric (A0) mode of guided waves is considered for multiple debond assessments in the investigated structure. The A-scan responses of the propagating guided wave of a debond HCCSS are compared with that of a pristine model. This comparison served as a valuable damage precursor resulting from interaction effects. A substantial amplification of the modal amplitude in the case of debonded HCCSS is noticed compared to that of the pristine model. The A-scan responses captured at a location distant from the debonded zone showed little difference. The TFM-FMC algorithm based structural monitoring framework is utilized to locate and quantify the debonded zones in the HCCSS. The proposed health monitoring framework is considered to be substantially effective in the detection of debond damages in a composite sandwich structure.
ENERGY BASED ULTRASONIC TECHNIQUES FOR EARLYSTAGE DAMAGE DETECTION IN CONCRETE STRUCTURES
Authors: BASU SUKANYA , SASMAL SAPTARSHI
Published: December 2023

During the service life, concrete structures undergo several adversities that lead to weakening in concrete and results in crack initiation. At initial stage of damage, breathing /clapping effect prevails inside the material. It results in additional nonlinearity, called as contact acoustic nonlinearity (CAN) in the structure which is often subsumed due to the material nonlinearity and leads to unidentified progression of damage inside structure. In the present study, ultrasonic investigations have been carried out to detect the flexural cracks in a scaled model of a reinforced concrete (RC) girder-deck system under four-point bending where the damage stages can be categorized as, undamaged (Stage 0), crack initiation (Stage I), crack propagation (Stage II), and severe damage (Stage III). Data acquired from ultrasonic actuation-sensing at different damage stages are processed in time-frequency domain and frequency domain using energy based ultrasonic parameters, such as, wavelet packet energy and spectral energy distribution. Further, a new energy based nonlinear acoustic parameter (ENAP) is introduced that works on the harmonic phenomenon induced by the breathing cracks. The proposed energy based methods (especially nonlinear methods) are found to be very efficient for detecting all the damage levels successfully.
ROBOTIC ULTRASONIC INSPECTION OF LARGE AND COMPLEX STRUCTURAL ASSETS
Authors: GILMOUR ADAM , TABATABAEIPOUR MORTEZA , MCMILLAN ROSS , TZAFERIS KONSTANTINOS , HAMPSON RORY , JACKSON WILLIAM , ZHANG DAYI , LAWLEY ALISTAIR , MOHAMED AASIM , MACLEOD CHARLES , GACHAGAN ANTHONY , PIERCE STEPHEN G , DOBIE GORDON
Published: December 2023

The implementation of robotic inspection solutions can provide massive improvements in the non-destructive testing and evaluation of large industrial structural assets. Current on-the-market remote inspection solutions are often limited in scope and lack the positional accuracy necessary for robust and repeatable structural health monitoring. This paper, therefore, describes multiple robotic navigation and inspection techniques to tackle a variety of challenges. Conventional inspection systems utilizing ultrasonic phased arrays for defect detection in large structures like storage tanks and flat steel plates typically employ a localized inspection approach. However, these systems often suffer from slow inspection speeds and an increased potential for human error, particularly in the case of large structures requiring repetitive inspections. The first challenge addressed is the inspection of large, thick steel plates used in the manufacture of industrial assets. The proposed solution focuses on achieving precise positional accuracy for spot gauge-thickness measurements and detecting laminations and inclusions. The system's novelty lies in utilizing the inspection sample itself for positional tracking. While the introduction of an automatic crawler-based approach speeds up the inspection process, it remains time-consuming and generates a substantial amount of data. To tackle this challenge, when the thickness of the inspected component allows, ultrasonic guided waves (UGW) can be employed for mid to long-range inspections. The second example outlines the integration of UGW with robotic inspection systems, which could significantly reduce inspection times by enabling rapid scanning and providing access to inaccessible regions. The final system looks at complex geometry, such as welded inserts or piping networks, and the implementation of ultrasonic phased array capabilities remotely and accurately. Standard crawlers for weld inspection are limited to simple geometry such as plates and large-bore pipes. The proposed system combines the flexibility of a sixdegree- of-freedom robotic manipulator with the remote capabilities of a magnetic base. Imbedding a sensor-driven system for live geometry assessment and tool path planning.
A STREAMLINING REMOTE SENSING AND DIGITALIZATION PROCESS FOR BRIDGE INSPECTION
Authors: SHI ZHENHUA , ZHANG HAIBIN , MONDAL TARUTAL  GHOSH , HARTNAGEL BRYAN A. , CHEN GENDA
Published: December 2023

One of the major challenges that prohibited the wide application of remote sensing photogrammetry in modern bridge inspection is the lack of advanced sensing platforms with a data analysis process. This study explores remote sensing for bridge inspection with a suite of unmanned aerial system (UAS) platforms accompanied by a follow-up digitization process for bridge inspection. To fully inspect a bridge of interest, a team of four complementary remote sensing UASs and equipment is employed, including a confined-space-specific Elios2 drone for bridge underdeck inspection, a DJI M600 equipped with LiDAR, thermal and hyperspectral sensors for above bridge deck sensing, an Anafi Parrot for side and above bridge deck RGB imaging, and a supplementary hand-held high-resolution camera for drone-inaccessible and highpriority areas. The digitized bridge inspection results cover multiple aspects of the bridge, including an interactive digital object represented by a dense point cloud, RGB images to reflect any visible surface deterioration (such as cracks, spalling, corrosion, and efflorescence), thermal images to reflect bridge deck near-subsurface delamination, and hyperspectral images to reflect any invisible bridge surface change. The digitized bridge model provides a comprehensive but intuitive digital representation of the inspected bridge. Compared to traditional bridge inspection practices with descriptive and subjective results, remote sensing photogrammetry shows great potential in efficient and consistent bridge inspection with fine details, which is believed to be necessary for reliable bridge condition assessment and long-term bridge health monitoring.
DEEP LEARNING-BASED CORRODED CRACK IDENTIFICATION IN REINFORCED CONCRETE USING SCNET MODEL
Authors: XU YING , ZHOU SIWEI
Published: December 2023

In order to improve the efficiency and accuracy of corroded cracks detection and classification in reinforced concrete, a corroded cracks identification model Steel Corrosion Net (SCNet), based on deep learning Convolutional Neural Network (CNN), is proposed. The SCNet combines massive initial data with a multi hidden layer neural network framework, and achieves feature learning and accurate classification through model training. The data set of 39000 crack figures is firstly built by original data collection and data enhancement. The training process of the SCNet consists of defining the loss function, the selecting back propagation optimization algorithm, continuously entering data to the network framework and running back the propagation algorithm until the error drops to a certain range. Afterwards, a SCNet three-classification neural network model is built and tested using TensorFlow learning framework and Python. According to the training and testing accuracies of the model, the structure and parameters of the SCNet network are optimized. The results of SCNet are compared with those obtained by two traditional testing methods. The results show that the proposed SCNet model achieves a classification accuracy of 96.8%. In other words, it can effectively identify and classify the corroded cracks in reinforced concrete, with high accuracy and measurability.
EVALUATION OF U-NET FOR TIME-DOMAIN FULL WAVEFORM INVERSION IMPROVEMENT
Authors: ANWAR SHOAIB , YUNKER AUSTIN , KETTIMUTHU RAJ , ANASTASIO MARK A. , VILLA UMBERTO , HE JIAZE
Published: December 2023

Ultrasound computed tomography (USCT) is one of the advanced imaging techniques used in structural health monitoring (SHM) and medical imaging due to its relatively low-cost, rapid data acquisition process. The time-domain full waveform inversion (TDFWI) method, an iterative inversion approach, has shown great promise in USCT. However, such an iterative process can be very time-consuming and computationally expensive but can be greatly accelerated by integrating an AI-based approach (e.g., convolution neural network (CNN)). Once trained, the CNN model takes low-iteration TDFWI images as input and instantaneously predicts material property distribution within the scanned region. Nevertheless, the quality of the reconstruction with the current CNN degrades with the increased complexity of material distributions. Another challenge is the availability of enough experimental data and, in some cases, even synthetic surrogate data. To alleviate these issues, this paper details a systematic study of the enhancement effect of a 2D CNN (U-Net) by improving the quality with limited training data. To achieve this, different augmentation schemes (flipping and mixing existing data) were implemented to increase the amount and complexity of the training datasets without generating a substantial number of samples. The objective was to evaluate the enhancement effect of these augmentation techniques on the performance of the U-Net model at FWI iterations. A thousand numerically built samples with acoustic material properties are used to construct multiple datasets from different FWI iterations. A parallelized, high-performance computing (HPC) based framework has been created to rapidly generate the training data. The prediction results were compared against the ground truth images using standard matrices, such as the structural similarity index measure (SSIM) and average mean square error (MSE). The results show that the increased number of samples from augmentations improves shape imaging of the complex regions even with a low iteration FWI training data.
FAST PREDICTION OF DYNAMIC STRUCTURAL RESPONSE USING REDUCED BASIS FUNCTION COMBINED WITH NEURAL NETWORK
Authors: CAO JIXING , QUEK SER-TONG , ZHANG SHANLI , ZHANG CHI , CAI MINBO , SI MICHAEL
Published: December 2023

Frequent calibration of the dynamic characteristics of jack-up platforms are important for accurate evaluation of their safety and comfort to occupants. As jack-ups are large in scale involving many physical mechanisms and hydrodynamic coupling, it is challenging to rapidly optimize the estimation of system parameters and predict the dynamic responses under potential scenarios. This study develops a novel offline-online framework to address these issues. In the offline phase, a set of reduced basis functions is extracted from a collection of high-fidelity datasets, and the corresponding coefficients are employed to train neural network models. The online phase involves mapping model parameters to the coefficients of reduced basis functions using the trained neural network. The trained neural network combined with the reduced basis function is then utilized to predict dynamic response. The feasibility of the proposed method was evaluated through a numerical model of a jack-up under wave loads. The results indicate that the method is effective, robust, and promising for the rapid evaluation of large-scale structures.
FROM INITIAL TO FINAL STATE: SINGLE STEP PREDICTION OF STRUCTURAL DYNAMIC RESPONSE
Authors: YUAN LEI , NI YI-QING , HAO SHUO , ZHANG WEI-JIA
Published: December 2023

Accurately predicting the structural response under dynamic loads is of great importance to evaluate the structure's performance, monitor the structure's health and control the vibration. The development of a real-time prediction method is challenging because the model must have a low computational burden and high running speed. Due to the computing convergence requirements and complexity constraints, traditional numerical methods such as the finite element method and the finite difference method must take thousands to millions of short-time steps to calculate the final structural dynamic response we really want. After costing a lot of computing resources and time, these internal steps are worthless but significantly reduced the efficiency of numerical methods. To tackle this problem, in this paper, we propose a single-step method named physics-informed implicit Runge-Kutta (PI-IRK) to predict the structure dynamic response straightly from the initial to the final state. Specifically, we fuse discrete-time physics-informed neural networks (PINNs) and implicit Runge–Kutta method with low-expense hide stages. In the proposed method, deep neural network models are employed as the core to predict the Runge-Kutta stages and the final state. We integrate physics information such as implicit Runge–Kutta form of structure vibration governing equation and boundary constraints as the prior information into the neural networks model. With the assistance of the prior information, the proposed PI-IRK model is an unsupervised learning model that can be trained without any measurement data. Without any internal steps, the PI-IRK model can straightly predict the final structural dynamic response after training. The accuracy of the proposed method is demonstrated by predicting the structural response of a cantilever beam under a distributed dynamic load even with a large time step.
PHYSICS-INFORMED GUIDED WAVEFIELD DATA COMPLETION
Authors: TETALI HARSHA VARDHAN , HARLEY JOEL B.
Published: December 2023

Ultrasonic wavefields are widely employed in nondestructive testing and structural health monitoring to detect and evaluate structural damage. However, measuring wavefields continuously throughout space poses challenges and can be costly. To address this, we propose a novel approach that combines the wave equation with computer vision algorithms to visualize wavefields. Our algorithm incorporates the wave equation, which encapsulates our knowledge of wave propagation, to infer the wavefields in regions where direct measurement is not feasible. Specifically, we focus on reconstructing wavefields from partial measurements, where the wavefield data from large continuous regions are missing. The algorithm is tested on experimental data demonstrating its effectiveness in reconstructing the wavefields at unmeasured regions. This also benefits in reducing the need for expensive equipment and enhancing the accuracy of structural health monitoring at a lower cost. The results highlight the potential of our approach to advance ultrasonic wavefield imaging capabilities and open new avenues for Nondestructive testing and structural health monitoring.
DEVELOPMENT OF A FIBER-SHAPED MLPEROVSKITE SENSOR FOR STRUCTURAL HEALTH MONITORING IN COMPOSITES
Authors: CARANI LUCAS BRAGA , ROBINSON JAPHIA , RAHMAN MD. MOSTAFIZUR , OKOLI OKENWA
Published: December 2023

Composite materials are widely used in various industries due to their high strengthto- weight ratio and appealing properties. However, the detection and assessment of damage in composite structures can be challenging, requiring the use of effective structural health monitoring (SHM) techniques. This study explored the potential of an embedded fiber-shaped mechanoluminescence (ML)-perovskite sensor for real-time monitoring of impact events in composites. The sensor was fabricated by coating a perovskite photodetector with a sensing layer comprising copper-doped zinc sulfide (ZnS:Cu)/polydimethylsiloxane (PDMS) blend, using carbon nanotube (CNT) yarns as a substrate. The sensor was characterized and its ability to detect low-energy impact events, which is an important indicator of SHM, was assessed. ML materials emit light when subjected to mechanical stress, which can be detected by a perovskite photodetector and converted to an electrical current. The variations in the electrical current can be measured to provide in-situ and real-time information on the condition of the structure. CNT yarns are a suitable candidate for embedded sensors in composite structures due to their high sensitivity and exceptional mechanical properties. The flexibility of the CNT also allows the sensor to be weaved within a composite fabric, minimizing the intrusiveness effect of the embedment process. The experimental result showed that the CNT ML-perovskite sensor demonstrated good sensitivity and response time to mechanical stimuli, and a direct correlation was observed between the intensity of the electrical current and the impact energy. The ML-perovskite device showed the potential as a promising embedded sensor for impact monitoring, which could significantly contribute to the field of SHM.
Wolf Vostell y la música de la vida
Authors: Flores Galán Alberto
Published: December 2023

El texto analiza iniciativas artísticas de Wolf Vostell que incorporan la música y el sonido. El arte multimedia de Vostell, que muy a menudo yuxtapone realidades y procesos a priori antagónicos, otorga al sonido un papel relevante en sus happenings, instalaciones o piezas de videoarte, también en conciertos fluxus y una ópera fluxus. El estudio aborda asimismo conceptos vostellianos como schwebung o dé/coll-age musik y la utilización por parte del artista hispano-alemán de sonidos cotidianos que son ordenados con criterios musicales. Recordando el aforismo de Wolf Vostell «la vida como ruido, el ruido como vida», el texto otorga una atención considerable a los ruidos. Estos pudieron localizarse ya en los precedentes futuristas y dadaístas, y la investigación mira al pasado tomando en consideración que ni Wolf Vostell ni sus compañeros fluxistas se desvinculan de la historia del arte o de la música. Se plantea una reflexión acerca de la manifestación de los preceptos sonoros/musicales de Wolf Vostell en Malpartida de Cáceres, localidad extremeña en la que funda en 1976 el Museo Vostell Malpartida. De acuerdo con la mirada de su fundador, se trata de un centro museístico que ya en su localización y su denominación explora la oposición de contrarios que se retroalimentan… también en lo que se refiere al sonido.
CONVERSOS, JEWS AND CONVIVENCIAS: HISTORY LOST IN TRANSLATION
Authors: Gutwirth Eleazar
Published: December 2023

The colloquial usages of convivencia constitute only one case of the relevance of translation, as they differ from convivencia between religions in medieval Spain and convivencia as it appears in the thought of Américo Castro. In the following lines we set it within the frame of its time, its field of scholarship and its historiographical, theoretical and philological presuppositions. We show that it encompasses precise fields such as food studies. An examination of one concrete example throws into question not only the equation of literature and history, but also the “translation” of medieval convivencia as a romantic image of idyllic, harmonious, friendly relations.
Relaciones temáticas y ecos entre el Scrutinium Scripturarum y el Defensorium unitatis christianae
Authors: García Fuentes Héctor Javier
Published: December 2023

Este artículo tiene como objetivo relacionar el Scrutinium con el Defensorium. Se demostrará a través de la comparación de los textos una influencia directa del primero en el segundo. Estrategias y elementos del Scrutinium se verán reorganizados en el Defensorium de acuerdo a sus objetivos particulares. Basándose en la metodología escolástica ―el uso de la ratio y auctoritas― y el vínculo entre el Antiguo Testamento y el Nuevo como principios argumentativos, ambos tratados exponen ideas muy similares respecto a los judíos y los judeoconversos. A pesar de que las dos obras acentúan el factor judío, ninguna dejará de ser ortodoxamente cristiana. Así pues, se pondrá de relieve la singular característica de estos tratados cristianos escritos por conversos muy conscientes de su condición hebrea.
FAITH UPON INTELLECT. THE ARGUMENT OF PEDRO DE LA CABALLERÍA IN FAVOR OF TRINITY
Authors: Sadik Shalom
Published: December 2023

In this article we analyze the arguments of the Jewish apostate Pedro de la Caballería in his work ‘Tractatus zelus Christi contra iudaeos, sarracenos et infideles’. We will see that Pedro do not give rational argument in favor on the Christian dogmas. By opposite he argues that people have to believe only according to the authority of the scripture and other authoritative texts. By the end of the article we will conclude that his opinion on rationality come probably from his Jewish background as a follower of kabbalah.