bluuebunny/crossref_metadata_2025 huggingface dataset:
Autonomous motivation to reduce sedentary behaviour is associated with less sedentary time and improved health outcomes in rheumatoid arthritis: a longitudinal study
Authors: O’Brien Ciara M. , Duda Joan L. , Kitas George D. , Veldhuijzen van Zanten Jet J. C. S. , Metsios George S. , Fenton Sally A. M.
Published: October 2022

Background

This longitudinal study investigated whether changes in autonomous and controlled motivation to reduce sedentary behaviour were associated with variability in sedentary, standing and stepping time and, in turn, disease activity, systemic inflammation, pain and fatigue in rheumatoid arthritis (RA).

Methods

People with RA undertook assessments at baseline (T1, n = 104) and 6 months follow-up (T2, n = 54) to determine autonomous and controlled motivation to reduce sedentary behaviour (Behavioural Regulation in Exercise Questionnaire-2), free-living sedentary, standing and stepping time (7 days activPAL3μ wear), Disease Activity Score-28 (DAS-28), systemic inflammation (c-reactive protein [CRP]), pain (McGill Pain Questionnaire) and fatigue (Multidimensional Assessment of Fatigue Scale). N = 52 participants provided complete data at T1 and T2. Statistical analyses: In a series of models (A and B), path analyses examined sequential associations between autonomous and controlled motivation to reduce sedentary behaviour with activPAL3μ-assessed behaviours and, in turn, RA outcomes.

Results

Models demonstrated good fit to the data. Model A (sedentary and stepping time): autonomous motivation was significantly negatively associated with sedentary time and significantly positively related to stepping time. In turn, sedentary time was significantly positively associated with CRP and pain. Stepping time was not significantly associated with any health outcomes. Model B (standing time): autonomous motivation was significantly positively associated with standing time. In turn, standing time was significantly negatively related to CRP, pain and fatigue.

Conclusions

Autonomous motivation to reduce sedentary behaviour is associated with sedentary and standing time in RA which may, in turn, hold implications for health outcomes.
Optimizing pathological assessment of breast cancer in Brazil: recommendations from a multidisciplinary working group on the tumor-tissue journey
Authors: Gobbi Helenice , Carvalho Filomena Marino , Rahal Rosemar Macedo Sousa , Klôh Maria Ines , Pitol Dimitrius Leonardo
Published: October 2022

Timely and correct assessment of histopathological, immunohistochemical and molecular features of biopsy and surgical specimens is of paramount importance in the provision of care to patients with breast cancer, particularly in the current era of precision oncology. In order to ensure that tissue samples are obtained, processed, analyzed and reported in an optimal way, a concerted effort is required by institutions and individuals, taking into account state-of-the-art scientific and technical knowledge and circumventing logistic and operational constraints. This may be particularly challenging in some settings due to several sources of economic, structural, organizational and communication inefficiencies. In the current article, we present a brief review of breast cancer epidemiology and challenges in the disease diagnosis, especially in Brazil, and report the results of a multidisciplinary working group convened in May 2020 in an expert panel to identify and discuss the barriers and challenges related to the journey of breast cancer samples in Brazil. Following the identification of the issues, the working group also discussed and proposed recommendations for improving the journey and quality of breast cancer samples based on their professional experience and the current scientific literature, including guidelines of national and international health organizations (e.g. World Health Organization), consensus of medical societies and other published literature on the topic. We outline the most salient issues related to that journey in Brazilian public and private medical institutions, based on the experts’ clinical experience, since all of them are actively working at both sectors, and discuss current recommendations to address these issues aiming at mitigating and preventing preanalytical and analytical issues affecting diagnostic and therapeutic decisions. Such issues are grouped under four headings pertaining to education, communication, procedures in the operating room and sample transportation, and procedures in the pathology laboratory. Selected recommendations based on the current literature and discussed by the group of Brazilian experts are reviewed, which may mitigate the issues identified and optimize diagnostic and therapeutic decisions for patients with breast cancer, currently the most frequent malignant tumor worldwide and in Brazil. This paper has been submitted and published jointly, upon invitation and consent, in both the Surgical and Experimental Pathology and the Mastology journals.
Robot-assisted thoracoscopic surgery for large apical schwannoma in an obese patient
Authors: Ishihara Shunta , Shimomura Masanori , Tsunezuka Hiroaki , Ikebe Satoshi , Inoue Masayoshi
Published: October 2022

Background

Neural tumors at the thoracic apex require a careful surgical technique because of the presence of vascular and neural structures and minimally invasive surgery can be challenging for these tumors, especially large tumor in obese patients. We report a case of a large apical schwannoma that treated with RATS in an obese patient.

Case presentation

An obese, diabetic, 36-year-old obese man with a body mass index of 34.7 presented with a 5.0-cm mediastinal mass in the left thoracic apex diagnosed via chest computed tomography. The magnetic resonance images suggested a schwannoma. Surgery was scheduled after glycemic control. Robot-assisted thoracoscopic surgery (RATS) was performed with in the right lateral position, and tumor was dissected from the surrounding tissue without severe nerve injury using bipolar forceps. The pathological diagnosis was benign schwannoma. The patient had an uneventful clinical course and was discharged on postoperative day 3.

Conclusion

RATS may be a useful approach in the narrow space of the thoracic cavity in obese patients.
Contrast-Enhanced Radiologic Evaluation of Gastric Conduit Emptying After Esophagectomy
Authors: Feenstra Minke L. , Alkemade Lily , van den Bergh Janneke E. , Gisbertz Suzanne S. , Daams Freek , van Berge Henegouwen Mark I. , Eshuis Wietse J.
Published: October 2022

Background

Nasogastric tube (NGT) insertion is the standard of care in many hospitals after esophagectomy for gastric conduit decompression. An upper gastrointestinal contrast passage evaluation (UGI-CE) is a diagnostic test to evaluate passage through the gastric conduit. The authors hypothesized that introducing routine UGI-CE after esophagectomy results in earlier removal of the NGT and resumption of oral intake.

Methods

This retrospective study evaluated two consecutive series of patients undergoing esophagectomy, one before (control group) and one after the introduction of a routine UGI-CE on postoperative day (POD) 3 or 4 (UGI-CE group). If contrast passage was found on the UGI-CE, the NGT was capped and removed. In the control group, the NGT was routinely capped and removed on day 5 after surgery. The primary outcome was the POD on which oral diet was initiated. The secondary outcomes were the day of NGT removal, NGT reinsertions, postoperative complications, and length of hospital stay.

Results

Each cohort included 74 patients. In the UGI-CE group, the contrast test was performed on median POD 3.5 (IQR, 3–4). The median day of NGT removal, initiation of clear liquids, and full liquid and solid intake was 1 to 2 days earlier in the UGI-CE group than in the control group (i.e. POD 4, 4, 5, and 6 vs. POD 5, 5, 6.5, and 8; all p < 0.001). The study found no significant differences in NGT reinsertions, pneumonias, anastomotic leakages, or hospital stay.

Conclusion

The routine use of a UGI-CE after esophagectomy led to earlier removal of the NGT and earlier resumption of oral intake.
A New Approach for Designing and Analysis of High Flat Gain Broadband Low Noise Amplifier Using Real Frequency Technique
Authors:   National Institute for Standards (NIS), Giza, Egypt, Magdy Reham , Shehata Gehan S. , Amar Ahmed S. I. , ElHawary M. , Mohanna Mahmoud A.
Published: October 2022

This paper introduces a high flat gain broadband Low Noise Amplifier (LNA) design approach, which is based on using a new methodology of Real Frequency Technique (RFT). To get this done, the optimum matching network has been designed with second-order LC lumped elements to minimize the Noise Figure (NF) and maximize the Transducer Gain (GT) based on selecting the optimum (ZS, ZL) LNA terminations that have been used over a 2.1-4.2 GHz. The design has demonstrated a high stable gain in the range of 19.19 –18.73 dB with ± 0.2 dB gain flatness over the specified band. Moreover, the NF has been obtained at 0.68 – 0.82 dB and a stable operation throughout a broad bandwidth. Furthermore, the proposed method results have been compared with an analytical methodology which is based on computing equivalent input and output circuits for the transistor model using its scattering parameters. For the verification purpose, the performances of the synthesized amplifier are compared using MATLAB platform and ADS simulation software. The output results have a good agreement with the proposed method outcomes, which makes this method a new promising technique for the high flat gain broadband LNA design.
Performance Enhancement of Microstrip Patch Antenna Based on Frequency Selective Surface Substrate for 5G Communication Applications
Authors:   Department of Physics, College of Education for Pure Sciences Ibn Al-Haitham, University of Baghdad, Baghdad, Iraq, Alsudani Ahlam , Marhoon Hamzah M.
Published: October 2022

Besides the evolution of wireless communications and their across-the-board utilise due to the many advantages they provide, on other hand, microstrip antennas have been popular since ancient years. These antennas are characterized by ease of design, low cost and small size, in addition to their ability to integrate and embed with most portable applications. Despite the many benefits mentioned, these antennas suffer from many disadvantages of poor gain, narrow bandwidth, and low efficiency. In this article, a rectangular Microstrip antenna (MPA) is designed, simulated, and analysed based on the finite internal technique that is supported by the Computer Simulation Technology (CST) antenna simulation software for the 5G applications of the 28 GHz band. In order to improve the proposed MPA performance, the Frequency Selective surfaces (FSSs) are introduced. Whereas the proposed MPA is situated between two FSSs superstrates, the upper one acts as a passive bandpass filter and the lower acts as a passive bandstop filter reflecting the leakage radiation from the ground plane of the MPA.
An Improved Multipath Energy Aware On-Demand Routing Protocol for MANETs
Authors:   Department of Electronics and Communication Engineering, Ramaiah Institute of Technology, Bangalore, India, P Rajendra Prasad , Shivashankar Shivashankar
Published: October 2022

With recent advances in the field of wireless network or Mobile Ad-hoc Networks (MANETs) technologies, usage of mobile device computing technologies is the fastest growing technology. Formerly in the wired network, telephone and cable companies provide wired access to the devices for homes and offices. Today, the tendency is to move to wireless technology. Effective MANETs come across with numerous difficulties and challenges. If a packet is to travel from one host to another host, it needs to pass through many interconnected networks. The accompanying challenges in the network restrict current protocols from routing in MANETs. Also, balancing energy consumption is the major design issues since nodes have restricted energy, memory, bandwidth etc. Thus, the main objective of this research is to design an energy efficient protocol to minimize the energy consumption in MANETs. The main contribution of this research work includes the development of the new routing protocol called Energy Aware on Demand Routing (EADR) protocol for MANETs. This routing protocol approach is to build the new route discovery by evaluating energy consumption, packet loss, throughput and network lifetime. The protocol identification of the new route path by selection of the nodes which have the minimum energy threshold valves for its path to reach the intended destination node in network. The algorithm evaluates in minimizing the packet loss, energy consumption that increased throughput and network life time.
Effect of Number of Nodes and Distance between Communicating Nodes on WSN Characteristics
Authors:   Faculty of Engineering, Al-Ahliyya Amman University, Amman 19238, Jordan, Iskandarani Mahmoud Z.
Published: October 2022

Wireless Sensor Networks (WSNs) are increasingly used in many applications. However, issues like topology, routing, energy, nodes distribution and density, and connection reliability are affecting their deployment. In this work, investigation into such parameters using shortest path first routing technique is carried out using MATLAB simulation in free space with ransom uniform nodes distribution. The principle of simulation is to select the best route based on node’s location and available energy to carry out data transmission. The investigation proved that as the number of WSN nodes grows, the number of discovered routes grows in a power relationship with the overall time of route discovery, which is accompanied by a decrease in the number of hops per route. The research also showed that as the distance between the source and destination nodes increases, the number of routes identified increases in an exponential relationship, as does the overall route discovery time and the number of hops per route. Also, the simulation results shows that there is a relationship between path loss and both distance, and number of WSN nodes, with exponential relationship between path loss and distance and power relationship between path loss and number of nodes. The work also established a logarithmic relationship between distance and number of WSN nodes. By relating path loss to both distance and nodes, energy consideration is being covered in this work as an indirect parameter that is affected by routes, distance, communication medium and number of nodes. The mathematical modeling indicates that there is a need to dynamically correlate distance between nodes, communication medium, node’s energy, and topology to enhance routing of exchanged messages and to enable lower energy consumption.
Free Space Optics Link Performance Estimation Under Diverse Conditions
Authors:   Electrical and Computer Engineering Department, University of Ottawa, Ottawa, Canada, Okoumassoun Tagnon P. , Antwiwaa Anita , Gerrar Nana K.
Published: October 2022

Free Space Optics (FSO) uses air as a medium of transmission of signals and requires Line of Sight (LOS) for operation. Communication systems that employ FSO have gained prominence in recent times and have been used in mobile communications, broadcast, security, and other applications. They have proven a worthy substitute for fibre optics albeit with some notable disadvantages. FSO systems are usually installed above ground level and are therefore exposed to the prevailing atmospheric conditions which can negatively affect the optical signal. Tropical regions exhibit adverse weather conditions such as rain, fog, and haze that can affect FSO signals. There is a dearth of research on the use of FSO systems in tropical regions and on methods to mitigate the effect of FSO signal interference. In this research, we propose diversity techniques to improve the link performance of FSO systems in a tropical region. The simulation results indicate a better Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) compared to other techniques.
impact of Bacillus Calmette-Guérin (BCG) vaccination on the course of COVID-19
Authors: Gałązka Jakub Krzysztof
Published: October 2022

Nowadays, Bacillus Calmette-Guérin (BCG) is the most commonly used vaccine worldwide, used typically for tuberculosis but also in non-muscle invasive bladder cancer management. Basing on previously confirmed antiviral features of BCG and first statistic data reports, BCG usage in COVID-19 prevention was claimed and its potential molecular mechanism was searched. In scientific literature there was 10 publications proving several possible mechanism of BCG interaction with SARS-CoV-2 infection immune response. The most often was cross-reactivity between various BCG and SARS-CoV-2 antigens, including those crucial for their clinical effects. In most cases, those antigens linking was shown according to bioinformatical research. According to this research, the potential role of BCG in COVID-19 management should be considered as significant, at least until the clinical trials conducted nowadays will be over.
FULL-SCALE DEFORMATION FIELD MEASUREMENTS VIA PHOTOGRAMMETRIC REMOTE SENSING
Authors: GRAVES WILLIAM , LATTANZI DAVID
Published: October 2022

3D remote sensing technologies have improved dramatically over the past five years and methods such as laser scanning and photogrammetry are now capable of reliably resolving geometric details on the order of one millimeter or less. This has significant impacts for the structural health monitoring community, as it has expanded the range of mechanics-driven problems that these methods can be employed on. In this work, we explore how 3D geometric measurements extracted from photogrammetric point clouds can be leveraged for structural analysis and measurement of structural deformations without physically contacting the target structure. Here we present a non-destructive evaluation technique for extracting and quantifying structural deformations as applied to a load test on a highway bridge in Delaware. The challenging nature of 3D point cloud data means that statistical methods must be employed to adequately evaluate the deformation field of the bridge. Overall, the results show a direct pathway from 3D imaging to fundamental mechanical analysis with measurements that capture the true deformation values typically within one standard deviation. These results are promising given that the mid-span deformation of the bridge for the given load test is on the scale of only a few millimeters. Future work for this method will also investigate using these results for updating finite element models.
CONVOLUTIONAL NEURAL NETWORKS FOR ULTRASONIC GUIDED WAVE-BASED STRUCTURAL DAMAGE DETECTION AND LOCALISATION
Authors: LOMAZZI LUCA , GIGLIO MARCO , CADINI FRANCESCO
Published: October 2022

Among the many methods proposed in the literature to perform structural health monitoring (SHM) of thin-walled structures, two of them appear to be particularly promising and complementary. On the one hand, integrating Machine Learning techniques into this field seems a remarkable solution, since these methods have been shown to be effective in recognising usually hard-to-detect recurring patterns in the measured signals related to the presence of damages in structures, thus improving the diagnostic performances of SHM frameworks. In particular, in the past years, Deep Learning algorithms have gained much importance in this field due to their capability of processing high-dimensional inputs (such as images), thus making it possible to automatically identify onsetting structural damages. On the other hand, ultrasonic guided wave-based approaches are commonly adopted to assess the structural integrity of plate-like structures and pipelines. These approaches, coupled with tomographic algorithms, typically allow performing damage detection and localisation with satisfactory results. However, such reconstruction algorithms are significantly sensors layout-dependent and, as such, they come with some still unsolved issues, leading, for example, to artifacts creation and unsatisfactory tomographic damage localisation performances in case of unevenly distributed network of sensors or when few sensors are installed on the structure. In this work, convolutional neural networks (CNNs) and ultrasonic guided waves are combined into a unique framework, which leverages on the advantages of the two methods to perform damage detection and localisation in platelike structures. Guided waves are excited and sensed by a network of sensors permanently installed on the structure. The information acquired is then converted into grayscale image as is, without performing any prior feature extraction procedure, which is further analysed by a set of CNNs. First, a classifier is employed to perform damage detection. In case damage is identified, the grayscale image is then analysed by two regression CNNs to localise the damage. The framework is tested using experimentally validated numerical simulations of guided waves propagating in a metallic plate available in the literature.
ELECTRO-MECHANICAL IMPEDANCE EXPERIMENT AND REAL-TIME DATA ACQUISITION FOR SUBORBITAL SPACEFLIGHT
Authors: NWOKOCHA FUNMILOLA , ZAGRAI ANDREI , HUNTER DAVID , AMON DALE , DEMIDOVICH NICKOLAS
Published: October 2022

Reusable space vehicles nowadays are regularly used to ferry payloads to space. The structural health monitoring (SHM) systems could be used to further improve safety of the vehicle and reduce operational costs. This contribution describes design, development and implementation of a realtime data acquisition SHM experiment for suborbital spaceflight. The aim of suborbital experiment is to demonstrate successful collection, spatial distribution, on board processing and storage of environmental, structural (SHM) and flight data. In this contribution, details of payload design and operation are provided focusing on SHM application in space environment. Due to space and mass limitation of the payload, a SHM experiment was designed with minimum use of hardware and materials. A miniaturized Canary impedance measurement unit was developed to include the real-time data analysis and communication capabilities as well as a raw data storage on a SD card. A small cantilever beam with an attached piezoelectric sensor was selected as a structural specimen for the in-flight electro-mechanical impedance test. The specimen was modeled analytically and compared to experimental data obtained in laboratory tests. The systems’ ability to process impedance data in near-real time was also validated. The results demonstrate ability of the developed SHM system to acquire, store, analyze and communicate the electro-mechanical impedance information to enable a new generation of smart space structures.
ON THE APPLICATION OF TOPOLOGICAL DATA ANALYSIS: A Z24 BRIDGE CASE STUDY
Authors: GOWDRIDGE T. , DERVILIS N. , WORDEN K.
Published: October 2022

Topological methods are very rarely used in structural health monitoring (SHM), or indeed in structural dynamics generally, especially when considering the structure and topology of observed data. Topological methods can provide a way of proposing new metrics and methods of scrutinising data, that otherwise may be overlooked. In this work, a method of quantifying the shape of data, via a topic called topological data analysis will be introduced. The main tool within topological data analysis is persistent homology. Persistent homology is a method of quantifying the shape of data over a range of length scales. The required background and a method of computing persistent homology is briefly introduced here. Ideas from topological data analysis are applied to a Z24 Bridge case study, to scrutinise different data partitions, classified by the conditions at which the data were collected. A metric, from topological data analysis, is used to compare between the partitions. The results presented demonstrate that the presence of damage alters the manifold shape more significantly than the effects present from temperature.
A PHYSICS-GUIDED DEEP LEARNING APPROACH TO MODELING NONLINEAR DYNAMICS: A CASE STUDY OF A BOUC-WEN SYSTEM
Authors: LIU WEI , LAI ZHILU , CHATZI ELENI
Published: October 2022

In this paper, we study the feasibility of applying the recently proposed Physicsguided Deep Markov Model (PgDMM) [1] for the modeling of hysteretic systems. PgDMM forms a hybrid probabilistic physics-guided framework, bridging physicsbased state space models with Deep Markov Models, thus delivering a hybrid modeling framework for unsupervised learning and identification of nonlinear dynamical systems. Previously, the framework has been tested on nonlinear elastic systems while in this work, we further test the framework on hysteretic systems described by Bouc-Wen type hysteresis. A physics-based model is used, which partially models the system’s dynamics, which is implemented by a learning-based term parameterized by a neural network. Our results indicate that the physics-based model essentially enforces a more structured and physically interpretable latent space, which is essential for generalization and prediction capabilities.
IMAGE TO IMAGE TRANSLATION OF STRUCTURAL DAMAGE USING GENERATIVE ADVERSARIAL NETWORKS
Authors: VARGHESE SUBIN , WANG REBECCA , HOSKERE VEDHUS
Published: October 2022

In the aftermath of earthquakes, structures can become unsafe and hazardous for humans to safely reside. Automated methods that detect structural damage can be invaluable for rapid inspections and faster recovery times. Deep neural networks (DNNs) have proven to be an effective means to classify damaged areas in images of structures but have limited generalizability due to the lack of large and diverse annotated datasets (e.g., variations in building properties like size, shape, color). Given a dataset of paired images of damaged and undamaged structures supervised deep learning methods could be employed, but such paired correspondences of images required for training are exceedingly difficult to acquire. Obtaining a variety of undamaged images, and a smaller set of damaged images is more viable. We present a novel application of deep learning for unpaired image-to-image translation between undamaged and damaged structures as a means of data augmentation to combat the lack of diverse data. Unpaired image-to-image translation is achieved using Cycle Consistent Adversarial Network (CCAN) architectures, which have the capability to translate images while retaining the geometric structure of an image. We explore the capability of the original CCAN architecture, and propose a new architecture for unpaired image-to-image translation (termed Eigen Integrated Generative Adversarial Network or EIGAN) that addresses shortcomings of the original architecture for our application. We create a new unpaired dataset to translate an image between domains of damaged and undamaged structures. The dataset created consists of a set of damaged and undamaged buildings from Mexico City affected by the 2017 Puebla earthquake. Qualitative and quantitative results of the various architectures are presented to better compare the quality of the translated images. A comparison is also done on the performance of DNNs trained to classify damaged structures using generated images. The results demonstrate that targeted image-to-image translation of undamaged to damaged structures is an effective means of data augmentation to improve network performance.
VIDEO MOTION COMPENSATION FOR FATIGUE CRACK DETECTION IN STEEL STRUCTURES
Authors: MOJIDRA RUSHIL , LI JIAN , MOHAMMADKHORASANI ALI , MOREU FERNANDO , COLLINS WILLIAM , BENNETT CAROLINE
Published: October 2022

Fatigue constitutes a critical limit state affecting the safety of civil infrastructure. Under repetitive loading, structural members are susceptible to fatigue cracking under stresses much lower than the yield strength of the material. In this study, computer vision-based fatigue crack detection using a short video stream taken from a nonstationary camera is presented. Videos taken from a hand-held camera, or an unmanned aerial vehicle (UAV) contain two types of movements: 1) true object movements, and 2) unwanted camera movement due to hand shaking or UAV hovering. In most vision-based structural health monitoring research, feature-based motion compensation techniques are used that require manual selection of fixed objects in the video for feature point selection. Feature point selection from true moving objects in the video could produce inaccuracy in video stabilization. In this study, we propose to use hierarchical model-based motion estimation for global motion compensation, which does not require manual selection of fixed objects. First, we construct a pyramid of target and reference images and then estimate motion from top to bottom of the pyramid while accumulating the geometric transformation, by which the camera movement can be removed. Then, we detect salient feature points in the region of interest and track the motion of feature points throughout the video using the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm. Subsequently, a crack detection, and localization algorithm is applied to search for differential point movements caused by fatigue crack opening and closing. To evaluate effectiveness of the proposed method, a laboratory experiment was conducted on a C(T) specimen with an in-plane fatigue crack. Results show that proposed method was able to effectively compensate the camera motion and detect the presence of the fatigue crack.
MULTI-VIEW DEEP LEARNING FOR RELIABLE POST-DISASTER DAMAGE CLASSIFICATION
Authors: KHAJWAL ASIM B. , CHENG CHIH-SHEN , NOSHADRAVAN ARASH
Published: October 2022

This study aims to enable more reliable automated post-disaster building damage classification using artificial intelligence (AI) and multi-view imagery. The current practices and research efforts in adopting AI for post-disaster damage assessment are generally (a) qualitative, lacking refined classification of building damage levels based on standard damage scales, and (b) trained based on aerial or satellite imagery with limited views, which, although indicative, are not completely descriptive of the damage scale. To enable more accurate and reliable automated quantification of damage levels, the present study proposes the use of more comprehensive visual data in the form of multiple ground and aerial views of the buildings. To have such a spatially-aware damage prediction model, a Multi-view Convolution Neural Network (MV-CNN) architecture is used that combines the information from different views of a damaged building. This spatial 3D context damage information will result in more accurate identification of damages and reliable quantification of damage levels. The proposed model is trained and validated on reconnaissance visual dataset containing expertlabeled, geotagged images of the inspected buildings following hurricane Harvey. The developed model demonstrates reasonably good accuracy in predicting the damage levels and can be used to support more informed and reliable AI-assisted disaster management practices.
DOES THE CURSE OF DIMENSIONALITY APPLY TO UNSUPERVISED SHM? INVESTIGATING THE TRADE-OFF BETWEEN LOSS OF INFORMATION AND GENERALIZABILITY TO UNSEEN STRUCTURAL CONDITIONS
Authors: SOLEIMANI-BABAKAMALI MOHAMMAD HESAM , LOURENTZOU ISMINI , SARLO RODRIGO
Published: October 2022

The curse of dimensionality (CD) brings difficulties in pattern recognition problems, such as those found in structural health monitoring (SHM). Dimensionality reduction techniques (DR) make data more manageable by reducing noise and noninformative portions. There exists a trade-off between CD and the loss of information due to the application of DR. Even though in supervised SHM, DR techniques are shown to be effective, for unsupervised SHM, the trade-off must be assessed due to the unknown data population of novel classes. This study assesses the trade-off concerning a novel method working with a raw frequency-domain feature, the fast Fourier transform (FFT). Different DR techniques are applied to the initial FFT-based feature to assess the trade-off, and detection results are compared. The results indicate that the loss of information can have detrimental effects, such as lowering the detection accuracy by 60% for the autoencoder-based DR. The accuracy reduction is present for all different DR techniques applied in the study; however, regularization lessens the accuracy decrements. This phenomenon indicates the assumption that novelties show themselves in less-vary portions of the baseline condition to be not true.
THE PROPAGATION CHARACTERISTICS OF LEAKY LAMB WAVE AND APPLICATION FOR RESIN IMPREGNATION MONITORING
Authors: LIU XIAO , WANG YISHOU , QING XINLIN
Published: October 2022

The leaky Lamb wave has a broad application prospect in the fields of biosensing, concrete construction, composite material manufacturing, and so on. This paper first theoretically investigates the propagation characteristics of leaky Lamb waves when the rigid mold is loaded with water or viscous resin by one side in the semi-infinite space. As the thickness of the liquid increases, more and more energy leaks into the liquid through the solid-liquid interface. There is more energy leaking into the viscous resin than that in the water. The phase velocity and energy velocity decrease as the liquid increases. Then the amplitude reduction and phase delay of A0 mode due to the liquid loading from theoretical analysis were verified by subsequent experiments. One valid application of leaky Lamb waves in resin impregnation monitoring during the Vacuum Assisted Resin Infusion of composite materials manufacturing process was also investigated experimentally.
A PARAMETRIZED REDUCED ORDER MODEL FOR RAPID EVALUATION OF FLAWS IN GUIDED WAVE TESTING
Authors: SIEBER PAUL , AGATHOS KONSTANTINOS , SOMAN ROHAN , OSTACHOWICZWIESLAW OSTACHOWICZ WIESLAW , CHATZI ELENI
Published: October 2022

Data from guided wave propagation in structures, produced by piezoelectric elements, can offer valuable information regarding the possible existence of flaws. Numerical models can be used to complement the attained data for refining the potential for flaw characterization. Unfortunately, evaluation of these models remains computationally expensive, especially for small defects, due to the short wavelength required for detection and, the in turn fine discretization in time and space. This renders real–time simulation infeasible, rendering GW–approaches less attractive for inverse problem formulations, where the forward problem needs to be solved several times. We propose an accelerated computation method, which exploits the properties of guided waves interacting with defects, where an extra band of waves is created, whose phase is differentiated, depending on the location of the flaw (e.g. notch) within the medium. To expedite the actual simulation for the inverse problem, the system is parametrized in terms of the location of the flaw and, in an offline phase, is repeatedly solved to produce snapshots of the system’s response. The snapshots are used to create a physics–informed interpolation of the solution of the wave propagation problem for different flaw locations. The gained information is then used in an inverse setting for localising the defect using an evolution strategy as a means to stochastic, derivative-free numerical optimization. The method is demonstrated in simulations of a 2D slice of a thin plate.
COMPARISON OF PLATE AND SOLID SPECTRAL ELEMENT MODELING OF COMPOSITE DELAMINATION FOR GUIDED WAVE SIMULATIONS
Authors: NICOLI SERGIO , AGATHOS KONSTANTINOS , KUDELA PAWEL , OSTACHOWICZ WIESLAW , CHATZI ELENI
Published: October 2022

As composite laminates have become essential to numerous modern industries, Structural Health Monitoring (SHM) strategies have to be implemented in order to manage the aging of structures made out of composite materials. In this paper, we present and compare two approaches involving solid and plate Spectral Elements (SE) in the time domain, with respect to their ability of modeling composite delamination: a task which lies in the context of modelbased data processing for non destructive evaluation techniques with Guided Waves (GWs). To this end, we review the multilayer flat shell delamination model [1], and propose a singlelayer alternative by extending the Layered Solid Spectral Element (LSSE) [2] method by means of partition of unity enrichment functions [3].These approaches are then compared in a numerical example with different damage scenarios. Finally, conclusions on the benefits and drawbacks of these two strategies are presented.
ON THE EXISTENCE OF ZERO GROUP VELOCITY MODES IN RAILS
Authors: WU YUNING , ZHANG KEPING , HUANG CHI-LUEN , LEE SANGMIN , POPOVICS JOHN , ZHU XUAN
Published: October 2022

Ultrasonic guided waves are of practical interests for nondestructive evaluation (NDE) and structural health monitoring (SHM) since users can promote desirable wave modes for damage detection, thickness measurement, surface condition characterization, stress measurement, and so on. This study focuses on demonstrating the existence of zero group velocity (ZGV) modes for guided waves in free rails. First, the team computed dispersion curves of AREMA standard rails to identify ZGV points through semi-analytical finite element analysis (SAFE). Second, finite element models were established to spatially sample wave propagation in free rails for wavenumberfrequency domain analysis. The results of finite element simulations were compared with dispersion curves produced by SAFE, and multiple points were identified with vanishing group velocity at non-zero wavenumbers. And resonances with positive and negative wavenumbers revealed that the observed standing waves phenomenon results from the interference of two traveling waves propagating with opposite directions. Our observation and developed methodology have potential applications for rail defect detection, support condition assessment, and rail stress measurement.
MESOSCALE HOMOGENIZATION NUMERICAL STUDY ON THE SIGNIFICANCE OF CONCRETE MESOSCALE STRUCTURE ON WAVE PROPAGATION OF RECTANGULAR RCFSTS WITH DEBONDING
Authors: WANG JIANG , XU BIN , CHEN HONGBING , GE HANBIN
Published: October 2022

In this paper, in order to efficiently distinguish the influence of both interface debonding defect and the mesoscale structure of concrete core on the stress wave field and the response of an embedded Piezoelectric-lead-zirconate-titanate (PZT) sensor in rectangular concrete filled steel tube (RCFST) members, a two dimensional (2D) mesoscale numerical concrete with homogenization approach considering the random distribution of circle, ellipse and irregular polygon aggregates is proposed firstly. Then, mesoscale homogenization simulation on stress wave fields within the cross-sections of RCFST members with and without interface debonding defects are carried out, respectively. The effect of both mesoscale structure of concrete core and the interface debonding defect on the stress wave field of each member is discussed. Therefore, the time-domain response of the embedded PZT sensor in the RCFST members coupled with PZT patches under sweep frequency excitation signal is determined and compared when both mesoscale models and their homogenization models are used. The sensitivity of the wavelet packet energy of the embedded PZT sensor response on the variation of both mesoscale structure of concrete core and the dimension of interface debonding defects is investigated. The detectability of interface debonding using stress wave measurement is illustrated efficiently with the proposed mesoscale homogenization modelling approach even the mesoscale structure of the concrete core is considered.
KALIX BRIDGE DIGITAL TWIN—STRUCTURAL LOADS FROM FUTURE EXTREME CLIMATE EVENTS
Authors: KAZEMIAN MAHYAR , NIKDEL SAJAD , MOHAMMADESMAEILI MEHRNAZ , NIK VAHID , ZANDI KAMYAB
Published: October 2022

Environmental loads, such as wind and river flow, play an essential role in the structural design and structural assessment of long-span bridges. Climate change and extreme climatic events are threats to the reliability and safety of the transport network. This has led to a growing demand for digital twin models to investigate the resilience of bridges under extreme climate conditions. Kalix bridge, constructed over the Kalix river in Sweden in 1956, is used as a testbed in this context. The bridge structure, made of posttensioned concrete, consists of five spans, with the longest one being 94 m. In this study, aerodynamic characteristics and extreme values of numerical wind simulation such as surface pressure are obtained by using Spalart-Allmaras Delayed Detached Eddy Simulation (DDES) as a hybrid RANS-LES turbulence approach which is both practical and computationally efficient for near-wall mesh density imposed by the LES method. Surface wind pressure is obtained for three extreme climate scenarios, including extreme windy weather, extremely cold weather, and design value for a 3000-year return period. The result indicates significant differences in surface wind pressure due to time layers coming from transient wind flow simulation. In order to assess the structural performance under the critical wind scenario, the highest value of surface pressure for each scenario is considered. Also, a hydrodynamic study is conducted on the bridge pillars, in which the river flow is simulated using the VOF method, and the water movement process around the pillars is examined transiently and at different times. The surface pressure applied by the river flow with the highest recorded volumetric flow is calculated on each of the pier surfaces. In simulating the river flow, information and weather conditions recorded in the past periods have been used. The results show that the surface pressure at the time when the river flow hit the pillars is much higher than in subsequent times. This amount of pressure can be used as a critical load in fluid-structure interaction (FSI) calculations. Finally, for both sections, the wind surface pressure, the velocity field with respect to auxiliary probe lines, the water circumferential motion contours around the pillars, and the pressure diagram on them are reported in different timesteps.
NUMERICAL STUDIES ON BRIDGE INSPECTION USING DATA OBTAINED FROM SENSORS ON VEHICLE
Authors: YAMAMOTO KYOSUKE , FUJIWARA SACHIYO , TSUKADA KENTO , SHIN RYOTA , OKADA YUKIHIKO
Published: October 2022

In this study, we proposed a methodology for a technique to simultaneously estimate the mechanical parameters of vehicles and bridges and road surface roughness from vehicle vibration. The MCMC (Markov chain Monte Carlo) method was used to search for parameters from vehicle vibrations generated by numerical simulation. The results obtained are estimable even in the presence of bridge stiffness reduction, which suggests the possibility of bridge damage detection using vehicle vibration.
A PHYSICS INFORMED NEURAL NETWORK INTEGRATED DIGITAL TWIN FOR MONITORING OF THE BRIDGES
Authors: MORADI SARVIN , AZAM SAEED (YASHAR) EFTEKHAR , MOFID MASSOOD
Published: October 2022

In recent years the Digital Twin (DT) paradigm has been studied as a futuristic tool for the next generation of infrastructures. Due to the interdisciplinary nature of the design, construction, monitoring, and maintenance of the infrastructures and the cooperation of several stakeholders throughout their lifetime, it is indispensable to introduce a comprehensive platform for the digital representation of infrastructures. Although the DT emphasizes the role of digital modeling and data analysis, there is a gap between physical modeling and data-driven tools. The newly introduced Physics Informed Neural Networks (PINNs) are capable of not only filling this gap but also representing a unified real-time platform for different users from various fields. These algorithms suggest an agile environment for users to introduce different criteria from the design stage to the health monitoring period. The PINN integrates both physical modeling and data analysis in a unique algorithm, helping them interact simultaneously and providing real-time, reliable responses. By means of the PINN, the DT can learn and update the model from various data sources with a unique platform, which plays an essential role in the rapid flow of information and transparency of data-based calculations. The dynamic ambiance of the PINN enables the users to interact with the modeling procedure and track the analysis. In this study, the details of the proposed platform for the integration of the PINNs in the DT are addressed for monitoring the bridges. Extensive numerical studies are provided for various scenarios of sensor equipment, including sensor type, data accuracy, and installation pattern. The performance of the proposed platform is evaluated for predicting subsequent responses to ensure the reliability of the responses in future decision makings.
SENSOR FAULT DIAGNOSIS COUPLING DEEP LEARNING AND WAVELET TRANSFORMS
Authors: ABADÍA JOSÉ JOAQUÍN  PERALTA , FRITZ HENRIEKE , DRAGOS KOSMAS , SMARSLY KAY
Published: October 2022

Sensor networks facilitate collecting measurement data necessary for decision making regarding structural maintenance and rehabilitation in structural health monitoring (SHM) systems. Nevertheless, the reliability of decision making in SHM systems depends on the proper operation of the sensors. Sensors may exhibit faults, entailing faulty data and incorrect judgment of structural conditions. Therefore, fault diagnosis (FD), comprising detection, isolation, identification, and accommodation of sensor faults, has been introduced in SHM systems, enabling timely detection of faulty data while advancing reliable operation of SHM systems. Traditional FD approaches based on “analytical redundancy” take advantage of correlated sensor data inherent to the SHM system, sometimes neglecting the fault identification step, and are implemented for specific sensor data. In this paper, an analytical redundancy FD approach for SHM systems, coupled with machine learning algorithms and wavelet transforms, capable of processing any type of sensor data is presented. A machine learning (ML) regression algorithm is proposed for fault detection, fault isolation, and fault accommodation, and an ML classification algorithm is proposed for fault identification. Continuous wavelet transform (CWT) is used as a preprocessing step of fault identification, exposing fault patterns in the data. The ML-CWT-FD approach is validated using data from a real-world SHM system in operation at a railway bridge implementing a deep neural network as ML regression algorithm and a convolutional neural network as ML classification algorithm. As a result of this paper, the ML-CWTFD approach is demonstrated to be capable of ensuring reliable SHM systems.