bluuebunny/crossref_metadata_2025 huggingface dataset:
What is the Effect of Lidocaine Compared to Lidocaine and Clove Oil on Episiotomy Site Anesthesia: Results from a Randomized Clinical Trial
Authors: Eidi Shirinbolagh Zeinab , Partash Nasim , Khayattimotlagh Bonab Shiva , Ebrahimi Elham
Published: June 2024

Background: Episiotomy is a commonly performed obstetric procedure that often results in perineal pain for women. Objectives: This study aimed to compare the effects of lidocaine alone and lidocaine combined with clove oil on pain at the episiotomy site. Methods: This randomized clinical trial was conducted at Baharloo Hospital in Tehran, involving 65 nulliparous women who underwent episiotomy. Perineal pain was assessed using the Visual Pain Intensity Scale (VAS) and the McGill Questionnaire. The control group received a 2% lidocaine injection, while the intervention group received a combination of 0.8% clove oil and 2% lidocaine to alleviate pain before the episiotomy. The clove oil was applied topically to the episiotomy site 10 minutes prior to the lidocaine injection. During the episiotomy repair stage, both groups received a 2% lidocaine injection. Pain intensity was measured using the aforementioned tools at 1, 6, and 12 hours after episiotomy repair. Results: There was no statistically significant difference in average pain intensity between the two groups before and 12 hours after the intervention (P > 0.05). However, at 1 and 6 hours after the intervention, the combination of clove oil and lidocaine resulted in significantly lower pain compared to the lidocaine-only group (P < 0.05). Conclusions: Considering the greater effectiveness of lidocaine combined with clove oil in reducing episiotomy site pain intensity and the absence of side effects, this method can be recommended as an adjuvant therapy to reduce pain at the episiotomy site.
FREQUENCY FOR THE USE OF CBCT ANALYSIS IN DENTISTRY
Authors:   Department of Oral Rehabilitation, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania, Cojocaru Melania Olimpia , Drăghici Emma Cristina ,   Department of Oral Rehabilitation, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania, Khaddour Antonia Samia ,   Department of Oral Rehabilitation, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania, Ghiţă Răzvan Eugen ,   Department of Oral Rehabilitation, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania, Marinescu Iulia Roxana ,   Department of Oral Rehabilitation, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania, Simionescu Adrian Mihail ,   Private Practice, Popescu Sanda Mihaela ,   Department of Oral Rehabilitation, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania, Scrieciu Monica ,   Department of Prosthetics Dentistry, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
Published: June 2024

Cone beam computed tomography (CBCT) is a method that allows a three-dimensional evaluation of hard craniofacial structures with a lower radiation dose than classic CT. This method began to be used more and more frequently in dentistry. Background: The aim of the study was to evaluate the frequency for use of CBCT in dentistry. Methods: The study was addressed to dentists of various specialties from Oltenia region that completed an online questionnaire with 10 questions. Data were collected regarding the physician's specialty, the type of interventions for which the dentist recommends CBCT, how necessary this investigation is considered, and how invasive it is considered to be. The questionnaire was launched online for a period of 4 weeks, and dentists from the Oltenia region were invited to participate. The statistical analysis was carried out with the help of the Google Docs program, the section dedicated to forms, "Google Forms". Results: Young doctors chose this technology more frequently than doctors with more than 5 years of experience. Although implant planning is the primary reason for CBCT imaging in dentistry, CBCT is increasingly being used for other diagnostic purposes, such as periodontics and endodontics. In nearly 20% of cases, the primary diagnosis and/or treatment plan was changed following CBCT evaluation. Conclusions: There is an increasing trend in the use of CBCT in dentistry, especially by young graduates.
Jamaliyat al-Lughah al-'Arabiyyah wa al-Wasail al-Iliktruniyah wa Isykaliyat al-Adab al-Raqmi
Authors: Muhammad Jidda Hassan
Published: July 2024

: What has been mentioned in this article represents the elements involved in creating interactive digital text, the components that are verbal and non-verbal signs. The visual and audio components that accompany every poetic or prose text. The (interactive) recipient clicks the mouse button on a specific link, connection, or options. In the form of files, icons, or links that appear on the screen, and this matter cannot be implemented on paper together, then the animated effects and the text hypertext technique, especially in the poetic sentence. We then realize how much we have missed in terms of civilization, so we accept today what we rejected yesterday and on a wide table of interpretations, explanations, and defenses. Nearly four years ago, specifically in June 2004, a creative Iraqi writer, the poet (Salman Daoud Muhammad), launched an appeal in the Iraqi newspaper (Al-Adeeb), a distinguished cultural lung with an active presence in the contemporary Iraqi cultural scene, about the electronic book and the digital realist novel. Especially regarding the novel (Muhammad Sanajla) Shadows of the One / The Vision of Digital Realism, it awakens the questions of the present with an eye to the past about how to absorb this technical newness to replace the paper novel and what is the state of the next novel, imagination and language.
Enhancing Roads Infrastructure Monitoring with FBG Sensors and Multi-Threshold Signal Tracking
Authors:   University of Antwerpen, Golmohammadi Ali ,   University of Antwerpen, Hasheminejad Navid ,   University of Antwerpen, Van den bergh Wim ,   University of Antwerpen, Hernando David
Published: June 2024

Accurate monitoring techniques are needed to improve pavement durability

by ensuring their structural integrity and longevity. This study developed an

innovative real-time monitoring system utilizing fiber Bragg grating (FBG) sensors

embedded in asphalt layers. The data collected by FBG sensors includes a baseline

primarily influenced by temperature and peak shape data associated with short-term

loading from passing vehicles. Therefore, analyzing FBG data and identifying

statistical patterns poses a challenge. To address this challenge, employing a signal

tracker proves to be a valuable option for monitoring the baseline and scanning signals

for subsequent analysis. Moreover, considering the varying loading weights on FBG

sensors due to diverse vehicles, tracking FBG signals across multiple thresholds

enhances precision. A MATLAB-based program was developed incorporating a

multi-threshold signal tracker capable of efficiently processing FBG data by tracking

signals in both positive and negative multi-thresholds. This tracker effectively

distinguishes between different loading events and extracts the baseline signal after

tracking. Subsequently, the system provides outputs, including loading distribution,

number of loading events, traffic, and temperature effects. Finally, to validate the

system, real FBG data collected during an extensive monitoring campaign was

processed using the developed program. The obtained outputs contribute to a

comprehensive assessment of pavement performance, thereby facilitating the

development of more accurate maintenance and rehabilitation strategies.
Damage discrimination on drone fuselage with OBR Based Fibre-Optic Networks
Authors:   Technical University of Catalonia (UPC), Mujica Luis Eduardo ,   Technical University of Catalonia (UPC), Ruiz Magda Liliana ,   Technical University of Madrid (UPM), Fernandez Antonio
Published: June 2024

This paper presents a methodology for a structural health monitoring (SHM) system based on optical backscatter reflectometry (OBR) fibre optic networks. The study focuses on the use of machine learning methods to monitor the microdeformation of a drone fuselage and to detect and discriminate be-tween different types of damage when the structure is subjected to static loads. A total of 1500 points are measured for each load, providing a comprehensive understanding of the structural response. In addition, four types of damage, in-cluding partial disbonding at different locations, are induced with varying de-grees of extension to evaluate the sensitivity and accuracy of the SHM system. The results show that the OBR-based SHM system is capable of detecting and localising damage with high accuracy, indicating its potential as a reliable and effective tool for real-time structural health monitoring in aerospace applica-tions. The methodology presented in this study could be extended to other types of structures and damage scenarios, contributing to the development of more advanced and effective SHM systems.

Keywords: Machine Learning, OBR sensor network, Unmanned Aerial Vehi-cle, Drone.
A graph learning approach for automatic subsurface defects segmentation in building façade using RGB and infrared images
Authors:   Hong Kong University of Science and Technology (HKUST), He Sudao ,   Hong Kong University of Science and Technology (HKUST), Zhang Shenghan ,   RaSpect Intelligence Inspection Limited, Mishra Dhanada ,   RaSpect Intelligence Inspection Limited, Yuen Matthew Ming-Fai
Published: June 2024

Infrared thermal imaging technology provides a non-destructive and automated inspection solution for analyzing building facades. However, conventional methods that rely on detecting subsurface defects through abnormal heat loss often suffer from the limitation of misidentifying other materials present on the building facade. To address this challenge, this study proposes a graph learning approach for segmentation of subsurface defects by integrating both visual and thermal information. The proposed methodology encompasses several key steps. Firstly, the infrared image is partitioned into internally connected superpixels with similar characteristics using a linear iterative clustering algorithm. Subsequently, these superpixels are employed as nodes to construct a weighted undirected graph. The edges of the graph are defined based on the connectivity of superpixels in the image plane, while the weights are determined by the Wasserstein distance between each node. Furthermore, a multimodal graph reuse technique is introduced to construct a new graph based on an RGB image, thereby incorporating additional visual information. By defining the defect as tightly connected in the RGB graph but loosely connected in the infrared graph, the segmentation of defects involving multiple materials is formulated as a multi-graph N-cut problem. The feasibility and effectiveness of the proposed method are demonstrated through laboratory testing conducted on concrete specimens, as well as on-site monitoring carried out on a tiled building façade.
Improving the identification of dynamic parameters of bridges by UAV: approach for SHM
Authors:   University of Zagreb, Duvnjak Ivan ,   University of Zagreb, Ereiz Suzana ,   University of Zagreb, Goricanec Jurica ,   University of Zagreb, Orsag Matko ,   University of Zagreb, Bartolac Marko
Published: June 2024

The structural integrity of bridges is susceptible to deterioration caused by traffic loads, environmental conditions, and weather phenomena. To maintain the safety and stability of bridges, regular inspections of their elements are required. Recent advances in the field of unmanned aerial vehicles (UAVs) provide a viable option for accurate and autonomous inspections of bridge structures without disrupting normal operations. This study focuses on the determination of the dynamic structural parameters of a suspension bridge.
The methodology focuses on determining the natural vibration frequency of the bridge cables using UAV imagery. This paper contains a detailed theoretical presentation of the algorithm used, accompanied by a careful analysis of simulation results and empirical data from laboratory experiments and real case studies. In addition to the image processing-based approach for estimating the vibration frequency, the sensors on board the drone are also scrutinized and their effectiveness in detecting structural vibrations after the drone lands on the bridge surface is evaluated.
Structural Health Monitoring on Road Bridge: Application Cases of Optical Strand Sensor in JAPAN
Authors:   JGC Holdings Corporation, Yoshimura Naoki ,   OSMOS Group, Cartiaux François-Baptiste ,   OSMOS Group, Surpas Arnaud
Published: June 2024

In Japan, many of the major civil infrastructures such as road bridges, railways and tunnels was built during our high-growth period from the mid-1950s to the early 1970s. The number of these structures that has been more than 50 years old after construction, has been rapidly increasing in recent years. In this context, expectations for Structural Health Monitoring (SHM) have been rising to optimize its management and maintenance to extend its lifespan and ensure the safety of users.

Although a variety of IoT sensors are developing and their precision is improving day by day, SHM still has crucial challenges, e.g., if it can really identify critical damages to structures, determine quantitative assessment of the serviceability limit, evaluate the safety of a structure quickly after major events such as earthquakes, or so on. In order to achieve our original goals, it is essential to accumulate practical applications of SHM by dedicating a specific sensor solution to a specific issue.

The authors have introduced a unique and competitive Optical Strand Technology developed by OSMOS Groupe in France and have been providing the service of SHM mainly on civil infrastructures for more than 20 years in Japan. This paper presents some application cases of SHM to road bridges by means of Optical Strand Sensor which provide for different types of purposes and future prospects.
Particle Filter for temperature compensation of FBG strain sensors for event segmentation
Authors:   Vrije Universiteit Brussel (VUB), Weil Maximillian ,   Vrije Universiteit Brussel (VUB), Weijtjens Wout ,   Vrije Universiteit Brussel (VUB), Devriendt Christof
Published: June 2024

Structural Health Monitoring (SHM) plays a crucial role in assessing the integrity and performance of structures. Fibre-Bragg-Grating (FBG) technology has emerged as a valuable tool for strain measurements in SHM applications. However, FBG strain sensors are susceptible to temperature variations that can confound accurate strain measurements, potentially leading to errors in fatigue damage calculations and event detection.

This research addresses the challenge of temperature compensation in FBG-based SHM setups. Instead of the traditional approach of placing temperature sensors near every strain sensor, which can escalate costs and complexity with a growing number of sensors, we propose a filter-based alternative. The proposed approach leverages the slower strain changes induced by temperature effects compared to those caused by external loads, e.g. a passing train.

The key innovation lies in a filtering method that isolates temperature-induced strain variations as filtered data while treating load-induced strain as process noise. We explore the application of state-based filters such as the Kalman Filter (KF) and Particle Filter (PF). While the KF is limited to normally distributed noise, which does not represent load-induced strain effectively, the PF can handle various, adjustable noise distributions, that can be tailored to specific requirements.

The research showcases the PF's ability for temperature compensation on multiple structures, such as a pedestrian bridge equipped with only 8 temperature sensors for 82 strain sensors loaded either in tension or compression. This novel approach enables more reliable strain measurements of events for detection and analysis in SHM systems.
AIoT-enabled decentralized sensor fault diagnosis for structural health monitoring
Authors:   Technische Universität Hamburg-Harburg (TUHH), Chillón Geck Carlos ,   Technische Universität Hamburg-Harburg (TUHH), Al-Zuriqat Thamer ,   Technische Universität Hamburg-Harburg (TUHH), Elmoursi Moayed ,   Technische Universität Hamburg-Harburg (TUHH), Dragos Kosmas ,   Technische Universität Hamburg-Harburg (TUHH), Smarsly Kay
Published: June 2024

Structural health monitoring (SHM) systems collect and analyze data recorded from civil infrastructure to detect changes in the structural condition and to predict potential damage. However, sensor faults in SHM systems may lead to erroneous structural assessments, thus to inefficient maintenance operations, which could jeopardize the integrity of the structures. Artificial intelligence (AI) algorithms based on analytical redundancy have proven being effective foundations to implement sensor fault diagnosis but are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. Moreover, operating with centralized servers increases the likelihood of single points of failure, which compromises the robustness of SHM systems. To overcome the limitations and to enhance the robustness of SHM systems, this paper proposes a decentralized approach towards fault-tolerant SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Fault diagnosis algorithms based on AI are embedded into sensor nodes deployed for SHM, using Internet of Things technologies to be computed at the edge of civil infrastructure, avoiding large amounts of data to be sent to central servers. In addition, the sensor nodes embed algorithms that collect, process, and analyze the sensor data to assess the condition of the structures being monitored. The result is a smart (i.e., decentralized, fault-tolerant, and AIoT-based) SHM system that is calibrated in a laboratory experiment and then deployed on a bridge for field validation. It is expected that the approach presented in this paper will improve the robustness of SHM systems in assessing the structural condition of civil infrastructure.
A multivocal literature review of digital twins, architectures, and elements in civil engineering
Authors:   Technische Universität Hamburg-Harburg (TUHH), Smarsly Kay ,   Technische Universität Hamburg-Harburg (TUHH), Peralta Patricia ,   Technische Universität Hamburg-Harburg (TUHH), Dragos Kosmas ,   Technische Universität Hamburg-Harburg (TUHH), Ahmad Muhammad E. ,   Technische Universität Hamburg-Harburg (TUHH), Al-Zuriqat Thamer ,   Technische Universität Hamburg-Harburg (TUHH), Chillón Geck Carlos ,   Technische Universität Hamburg-Harburg (TUHH), Al-Nasser Heba
Published: June 2024

Recent structural health monitoring (SHM) strategies in civil engineering increasingly leverage digital twins, which digitally represent the structures being monitored as well as the SHM systems installed on the structures. Despite the widespread adoption of digital twin applications in recent years in SHM, there is a lack of agreement on a common definition of digital twins. Furthermore, there is no consensus on digital twin architectures and on the internal elements that constitute digital twins. A common digital twin definition would advance digital twin implementations and operability, and insights into digital twin architectures and digital twin elements would be vital to enhance the reliability and performance of digital-twin-based SHM systems. A significant number of digital twin definitions have been proposed and a plethora of reviews have been published; however, little emphasis has been given to digital twin architectures and internal elements. This paper presents a multivocal review of digital twins in civil engineering, aiming to provide a panorama of the digital twin landscape in civil engineering with explicit insights into the architectures and internal elements used in digital twin applications. From a methodological standpoint, the review follows a twofold approach that encompasses (i) peer-reviewed, indexed literature (“white literature”) as well as (ii) non-indexed sources (“gray literature”) that include industrial digital twin applications. Besides the multivocal review, a generic digital twin reference architecture is drawn from the review results and a digital twin definition is formulated both in an informal and formal (i.e., mathematical) manner. It is expected that the generic reference architecture and the definitions proposed in this study may serve as a blueprint for digital-twin-based SHM applications, with significant implications for researchers, practitioners, and policymakers in structural health monitoring.
Extending Structure Life via Intelligent Digitization: the Nibelungen Bridge as a Pilot Project
Authors:   Dresden University of Technology (TU Dresden), Kang Chongjie ,   Dresden University of Technology (TU Dresden), Bartels Jan-Hauke ,   MKP GmbH, Voigt Chris ,   Dresden University of Technology (TU Dresden), Marx Steffen
Published: June 2024

The construction industry is a significant consumer of natural resources and a major contributor to CO2 emissions in Germany, accounting for an annual waste volume of 54%. Therefore, preserving existing structures can play a crucial role in environmental protection and resource conservation. To achieve this goal, effective monitoring and maintenance concepts are essential to extend the service life of structures while ensuring their stability and functionality. In 2022, the German Research Foundation launched a priority program called 'SPP 100+' with the objective of fundamentally redefining the current maintenance strategy for infrastructure structures through intelligent digitization. This paper focuses on the Nibelungen Bridge in Worms, which serves as a validation case in this program. The paper begins by presenting the Nibelungen Bridge and its maintenance history. It then outlines key aspects, including geometric modeling, structural health monitoring (SHM), digital twinning, level 4 recalculation considering complex nonlinearities, and the assessment of detected structural damages. In the end, conclusions and outlooks are given.
The use of piezoelectric coupling in structural monitoring as a feature robust to EOV
Authors:   University of Genova, Berardengo Marta ,   Politecnico di Milano, Manzoni Stefano ,   Politecnico di Milano, Brambilla Matteo ,   University of Parma, Vanali Marcello ,   Politecnico di Milano, Codina Andrea
Published: June 2024

The use of piezoelectric materials is becoming more and more attractive in many different fields such as mechanical, civil, aerospace, etc. Indeed, their special features and light weight allow introducing them in structures and systems without causing significant load effects and allowing their use to the purpose of many different tasks, such as vibration control, structural monitoring, energy harvesting, etc.

The present paper analyses a new approach for exploiting piezoelectric elements for structural monitoring. The main idea is to use an estimate of the coupling between the transducer and the structure. This coupling represents the efficiency of the energy conversion between the hosting structure/system and the piezoelectric element (either bender or stack). Its estimate can be obtained by differential measurements of modal parameters.

Due to its estimation procedure, the coupling is shown being a feature robust to possible environmental and operating condition variations, without any need of training a detection algorithm with all the possible environmental and operational conditions.

The paper explains at first why the piezoelectric coupling is able to evidence the presence of a structural damage or alteration. Then, it is shown that the proposed damage feature is robust to environmental and operational variability, which constitutes one of its main merits.

Finally, the results of an experimental campaign aimed at confirming the main theoretical points presented in the paper are discussed.
Guideline for the strategic application of monitoring of road bridges in Germany
Authors:   MKP GmbH, Wedel Frederik ,   MKP GmbH, Pitters Sarah ,   BAM Federal Institute for Materials Research and Testing, Herrmann Ralf ,   BAM Federal Institute for Materials Research and Testing, Schneider Ronald ,   BAM Federal Institute for Materials Research and Testing, Hille Falk ,   Bundesanstalt für Straßenwesen (BASt) / Federal Highway Research Institute, Hindersmann Iris
Published: June 2024

Engineering structures are an important part of our transport infrastructure. Their failure is associated with high safety risks and economic damage. Ensuring the availability of these constructions and guaranteeing their operational safety are thus important tasks. Currently, maintenance of engineering structures is a reactive process and therefore not yet a predictive process. Every 3 to 6 years, the structures are visually inspected, all damages are documented and a condition grade is assigned to the structure based on the detected damages. Continuous data-providing methods such as monitoring are used only very occasionally. With the help of monitoring, condition data about the structures are continuously collected, which can be used for a better assessment of the structures. On the one hand, this would increase safety of engineering structures, and on the other hand, it makes the shift towards predictive maintenance management based on real-time data and predictions, where the onset of damage is detected before it occurs, possible in the first place. Monitoring plays therefore an important role in the management of the infrastructure. It is a crucial a step towards the digitalisation of our infrastructure and existing processes.

Although there are many use cases for monitoring that have already been successfully implemented, monitoring is not yet widely used by German road authorities due to several challenges that were also identified within the scope of the project.

Therefore, a guide has been developed in which the current obstacles related to monitoring are identified and possibilities for integrating monitoring into existing processes are offered. To ensure optimal alignment with the needs of road authorities, both an online survey and a workshop have been conducted. The results are summarised in a brochure that is made available to authorities to improve the use of monitoring in Germany. This paper presents the guideline and the brochure.
Sensitivity analysis of model parameters in a nonlinear model-updating approach for prestressed concrete bridges
Authors:   University of Duisburg-Essen, Sprenger Bjarne ,   University of Duisburg-Essen, Schnellenbach-Held Martina
Published: June 2024

Automated monitoring systems in structural health monitoring (SHM) using finite element models can measure system responses of the structure and provide information about behavior and damage scenarios. To describe the reactions and damages of the physical structure, the parameters of the FE-model have to be adjusted accordingly. A common approach therefore is model-updating, an iterative modification of unknown parameters of a finite element model by adapting the response of the model to the measurement results of the physical bridge. The adapted model can be used for assessments and predictions of the structure and provides accurate structural properties to a digital twin. The proposed monitoring concept includes damage detection, based on nonlinear model-updating using Artificial Intelligence (AI) methods and is applied to a prestressed, reinforced concrete bridge based on the observation of static response. For a practical application in long-term SHM, system properties and load values are identified simultaneously by using optimization methods based on Evolutionary Algorithms (EA). Due to the high dimensionality and complexity of the optimization results, Principal Component Analysis (PCA) including autoencoder networks for dimensionality reduction are required to evaluate and visualize the identified structural properties for their reliability. Based on physical 3D nonlinear finite element sensitivity analysis, the influence of damage scenarios and load situations on the bearing behavior of the bridge will be analyzed. The generated data and the results are presented and serve as the basis for the reliability analysis and evaluating the proposed concept. Furthermore, by comparing the measurements of the parameterized investigated models, conclusions about the behavior of the structure and the sensor placement can be obtained.
SHM to detect and characterize impact events in metallic aircraft structure
Authors:   National Research Council of Canada (NRC), Pant Shashank ,   National Research Council of Canada (NRC), Wooldridge Jack ,   National Research Council of Canada (NRC), Seabrook Emma ,   National Research Council of Canada (NRC), Valdes Julio ,   National Research Council of Canada (NRC), Genest Marc
Published: June 2024

Aircraft structures are susceptible to foreign object impacts, which may occur during manufacturing, maintenance and in-service. Sizes of these impacted objects especially during in-service can range from a small rock to a large bird. Common ways to detect such impacts are based on flight / ground crew observations and reports leading to close examinations of structures using non-destructive evaluation (NDE) techniques. If undetected these impact damages can grow during service loading and may be detrimental to flight safety. Therefore, timely detection of any signs of impact damages are critical such that proper maintenance actions can be taken. The aim is to develop methodologies using Structural Health Monitoring (SHM) techniques to detect and characterize foreign object impact events.

In this experiment, a cut-out of an aluminum panel measuring 31 x 26 inches from an out-of-service aircraft was used. The panel was instrumented with four Lead Zirconate Titanate (PZT) sensors from Acellent Inc., as well as, four Acoustic Emission (AE) sensors from Mistras Inc. Impedance and susceptance measurements were acquired to assess the proper functionality of the PZT sensors before and after the impact events. Both the PZTs and the AE sensors were directly connected to a digital oscilloscope, without any amplification and / or filtering for acquiring raw data during the impact events. An instrumented multi-use tapper was designed and developed to calibrate the system, as well as, to record the impulse (impact force and time) during the impact events. The acquired data were processed using physics-based and machine learning techniques to detect and characterize the impact events.
ArchIMEDES: Computer Vision Tracking of the Inherent Changes to Structural Stability of Masonry Arch Bridges Resulting from Increased Bed Scour.
Authors:   Queen's University Belfast, Millar Ben ,   Queen's University Belfast, Hamill Gerrard ,   Queen's University Belfast, Taylor Su ,   Queen's University Belfast, Robinson Des
Published: June 2024

Scour-induced failure of masonry arch bridges is a common cause of bridge collapse, especially during extreme flood events. Over recent years there has been a significant increase in the numbers of documented scour induced failures of this bridge stock across Europe and the USA. The extent of the scour can erode the supporting soil or sediment around the bridge piers or abutments, leading to a loss of support for the structure. As the foundation becomes unstable, it can result in settlement or tilting of the bridge, potentially causing structural damage, while the ongoing nature of scour can result in the progressive deterioration of the bridge’s ability to safely transfer load. Traditional hydraulic adjustments, such as streamlining abutments with wing walls and using cutwaters on piers, do not significantly reduce scour depth for masonry arch bridges. This paper presents initial findings of an investigation that links the changes of response in load carrying capacity to the easement of support at the bridge foundation due to scour. Scale experiments on the FlexiArchTM under loading with realistic scour conditions is contributing to the development of an algorithm based on the relationship between the structural response of the FlexiArchTM and prevailing scour conditions. The computer vision-based monitoring package ArchIMEDES, developed as part of the research, will use this algorithm to predict current bridge scour conditions at any given time. This will allow for potential scour development to be identified and investigated before the bridge collapses, ensuring the security and safety of these vital pieces of infrastructure.
A deep-learning-based approach towards identifying combined faults in structural health monitoring
Authors:   Technische Universität Hamburg-Harburg (TUHH), Al-Nasser Heba ,   Technische Universität Hamburg-Harburg (TUHH), Al-Zuriqat Thamer ,   Technische Universität Hamburg-Harburg (TUHH), Dragos Kosmas ,   Technische Universität Hamburg-Harburg (TUHH), Chillón Geck Carlos ,   Technische Universität Hamburg-Harburg (TUHH), Smarsly Kay
Published: June 2024

Fault diagnosis, encompassing fault detection, isolation, identification and accommodation, is critical for reliable structural health monitoring (SHM) systems, ensuring timely correction of sensor faults that can corrupt or lose data. While sensor fault identification has received scarce attention within fault diagnosis of SHM systems, recent methods have considered fault identification, assuming the presence of a single fault type in a faulty sensor, such as bias, drift, gain, precision degradation, or complete failure. Nonetheless, real-world SHM systems are prone to facing combined faults, i.e. different faults that affect individual sensors simultaneously. This paper presents a methodology for identifying combined faults, referred to as ICF methodology, aiming to identify sensor faults occurring simultaneously in individual sensors using time-series data, thereby improving the quality of fault diagnosis in SHM systems. Leveraging a recurrent neural network, specifically long short-term memory, a specialized classification algorithm, representing the core of the ICF methodology, is implemented using temperature time-series data. The ICF methodology is then validated with acceleration response data recorded from a real-world SHM system of a bridge. The acceleration response data is recorded from faulty sensors, containing natural faults, and non-faulty sensors injected with artificial, combined faults, to improve the validation of the methodology. The results demonstrate the effectiveness of the ICF methodology in enhancing the quality of fault diagnosis by comprehending the causes leading to sensor faults in real-world SHM systems.

Keywords: Identification of combined faults (ICF), sensor faults, fault diagnosis (FD), structural health monitoring (SHM), classification algorithms, long-short term memory (LSTM) networks.
Toward Indirect Real-Time Prediction of Bridge Vibration Responses Under Traffic Flow Through a Population of Connected Sensing Vehicles
Authors:   University of Alberta, Talebi-Kalaleh Mohammad ,   University of Alberta, Mei Qipei
Published: June 2024

Condition monitoring of bridge structures as the lifelines of smart cities is high of importance. While indirect vehicle scanning techniques have shown promising results and have less cost compared to mounting fixed sensors on the bridge, they have limitations in predicting response under traffic flow since the crossing time of one vehicle is very short. This paper presents a novel crowsensing-based framework for predicting bridge acceleration responses and identifying the modal characteristics. This method utilizes smartphone data from a diverse population of sensing vehicles as they traverse the bridge to predict bridge acceleration response at various virtual sensing locations. Subsequently, the predicted acceleration is used to identify the mode shapes and natural frequencies of the bridge. The principal innovation in this practical and cost-effective monitoring solution is the utilization of a randomly selected set of sensing vehicles at each timestamp. These selections may differ from one timestamp to the next, reflecting the real-world conditions where certain vehicles may intermittently lose or disconnect their internet connectivity. By consistently updating the set of sensing agents while other vehicles cross the bridge, the proposed framework overcomes the data length limitations of conventional vehicle-based methods by leveraging multiple vehicles and continuous data collection. Comprehensive numerical studies are conducted to evaluate the performance of the method. In the numerical investigations, a three-span bridge is subjected to the continuous passing of a large number of half-car vehicle models with random speeds and initial locations. Vehicle-bridge interaction is considered in the analysis. Utilizing a single randomly selected sensing agent at each timestamp, the results demonstrate the effectiveness of the framework in predicting bridge acceleration response with a relative error of less than 5%. Additionally, the method achieves an accuracy level of 95% in identifying the bridge's initial three mode shapes.
A short-training method for monitoring axially-loaded beams in presence of unknown and large thermal variations
Authors:   University of Genova, Berardengo Marta ,   Politecnico di Milano, Lucà Francescantonio ,   University of Parma, Vanali Marcello ,   University of Parma, Annesi Gianvito ,   Politecnico di Milano, Manzoni Stefano
Published: June 2024

Tie-rods are axially loaded beams often used in building to balance the lateral forces in vaults and arches and their good state of health is therefore essential for the integrity of the entire structure in which they are inserted. In the literature, there are few works treating the damage identification in tie-rods, which is nonetheless a difficult task because of the large natural environmental changes the tie-rods are subject to. Indeed, often, these axially loaded beams are placed outside from buildings and could experience more than 40 °C of thermal variation in a year. Such a temperature variation does not only change the mechanical and geometrical features of the tie-rod, as occurs for most of the structures, but also implies a large change of axial load, in turn causing a significant change of beam dynamics able to mask the effects of a possible damage.

This paper aims at presenting a new approach for monitoring the health condition of tie-rods using a single accelerometer to measure beam dynamics and a short training in order to avoid the need of collecting a large amount of data to describe all the possible temperature conditions. The method requires to identify few beam eigenfrequencies through operational modal analysis and processes them by means of principal component analysis and Mahalanobis squared distance. This allows building a proper damage index with reliable confidence intervals.

The effectiveness of the proposed method in finding damages with a short training period is demonstrated at first with numerically generated data and, then, using signals coming from a laboratory set-up. In both the cases, the proposed method is compared to a more traditional approach.
Non-Contact Acoustic Emission Monitoring of Corrosion in Mooring Chain Links
Authors:   Delft University of Technology (TU Delft), Riccioli Filippo ,   CORROSION Laboratory, Tyto Aldrich ,   Delft University of Technology (TU Delft), Pahlavan Lotfollah
Published: June 2024

Mooring chains are key parts of floating energy production units, such as floating wind turbines, photovoltaic islands, and floating production-storage-offloading units (FPSOs). These structures are predominantly subject to corrosion and fatigue. Detailed integrity assessment of mooring chains can be challenging due to difficult access, complex geometry, surface conditions (often with the presence of corrosion pits), and weather conditions. Additionally, the presence of marine growth on the surface of the chain links often requires the need of surface cleaning to obtain a detailed assessment of the structural integrity. This paper highlights an experimental investigation of monitoring of corrosion-induced damage in mooring chain links by means of non-contact Acoustic Emission (AE) technique. Accelerated corrosion experiments were performed on a large-scale mooring chain sample recovered from the field. A dedicated experimental set-up was designed to apply accelerated corrosion on two chain links submerged in natural sea-water. An immersion tank, i.e. an instrumented 1000x1000x1200mm3 water tank, was used to submerge the chain links in natural sea-water. The corrosion process was accelerated by means of anodic polarization. Ultrasound signals were continuously measured using two arrays of underwater AE transducers (in the frequency range between 50-450 kHz) placed on two perpendicular planes at a fixed distance from the chain links. Corrosion-induced AE sources were localized on the 3D geometry of the tested links. The variation and evolution of AE parameters extracted from the measured signals were analyzed as a function of testing time. The results of the accelerated corrosion experiment suggest that corrosion-induced ultrasound signals can be detected and monitored by means of non-contact AE. The results of this study may be used for characterization of corrosion-induced AE signals in mooring chain links.
Machine learning-based improvement of crack localization in concrete structures
Authors:   Ege University, Inderyas Omair ,   Ege University, Tayfur Sena ,   Hacettepe University, Kaya Aydın ,   Ege University, Alver Ninel
Published: June 2024

Acoustic emission (AE) method is useful for damage detection in structures. However, processing the massive amount of AE data with traditional analysis methods is time-consuming. Therefore, machine learning (ML) integration provides a more rational and efficient solution by accelerating the data analysis and improving the damage detection accuracy.
This study aims to improve source localization, which provides to identify cracking patterns by signal-based analysis, by training the ML model with the AE dataset for accurate damage diagnosis of concrete structures. Analyzes were conducted on the ML model using AE data collected on a concrete member under laboratory conditions. In this way, the ML model that learned signals with multi-variable wave characteristics and parameters was integrated into the source localization algorithm and thus, AE source location results were enhanced. Furthermore, due to its ability to identify damaged areas, this approach is essential for structural safety by providing early damage detection and accurate localization of cracks. This integration of the machine learning model with AE data will be considered as an advanced tool for the effective monitoring of damages in structures.
Development of a real-time ultrasonic monitoring tool for high-cycle thermal fatigue
Authors:   Imperial College London, Clarkson Laurence ,   Imperial College London, Zhang Yifeng ,   Imperial College London, Cegla Frederic
Published: June 2024

Pipe networks within nuclear power plants (NPPs) are susceptible to high-cycle thermal fatigue

(HCTF) failures especially in so-called mixing zones where fluids with different thermal and hydraulic properties interact. The 1998 incident at Civaux 1 NPP is a good example of such a failure and its potential consequences. Given the critical impact on safety of these NPP components, a non-invasive method for HCTF progression monitoring in real time is expected to be of great use.

Previous ultrasonic monitoring work showed that it is possible to predict the through-thickness temperature distribution and its temporal evolution of a mild steel block to within ±2 °C, relative to a resistance temperature device. These predictions were achieved using time of flight measurements from an ultrasonic transducer placed on the accessible surface with the so-called inverse thermal model (ITM) to invert the data. However, experiments to date have been limited to slow (10 minute) thermal transients at low temperatures (T < 100 °C). Given that HCTF is driven by thermal gradients, the ITM method seems promising to monitor its progression. In this work the performance of the ITM method was investigated under more realistic conditions: faster (1 minute) thermal transients at higher temperatures (≈ 250-300 °C). A special high temperature electromagnetic acoustic transducer (EMAT) and a fast acquisition and inversion methodology were created to collect the data. The measurement setup and the collected data will be presented in this paper.
Reduction of the model prediction error in vibration-based SHM applications
Authors:   National Technical University of Athens (NTUA), Silionis Nicholas E. ,   National Technical University of Athens (NTUA), Panagiotopoulos Sotiris ,   National Technical University of Athens (NTUA), Anyfantis Konstantinos N. ,   Structural Vibrations Solutions - SVIBS, Andersen Palle ,   Waveguide Instruments, Nilsson Carl-Magnus
Published: June 2024

The accuracy of an SHM scheme is partly controlled by the accuracy of the predictive models involved within the design process of the detector that associates sensor data with a corresponding health state. On the other hand, the accuracy of the predictive model, which is quantified by the error between predictions and observations, is partly controlled by its parameter magnitudes.
This work provides a unified framework that allows for reducing the model prediction error by updating the model parameters in the light of experimental observations. The problem is cast in a probabilistic setting and tackled through inferential statistics. Forward and inverse uncertainty quantification approaches are employed in a sequential manner. The framework is able to yield point estimates in accordance to a Maximum Likelihood Estimator (MLE) and is also able to construct credible intervals through computational Bayesian updating. The former is treated by genetic algorithms whereas the later through Markov Chain Monte Carlo computational statistics.
The framework is showcased on a vibration problem of the simplified benchmark case named as “Jim Beam”. The first ten modal parameters are considered as the discriminant features of interest . A surrogate of the finite element model of the considered specimen serves as the predictive model and the eigenfrequencies are extracted from a linear modal analysis. The parameters of inferential interest are the Young’s modulus and an artificial stiffness related parameter that is introduced so as to model the flexural rigidity of the specimen’s bolted connections. Experimental Modal Analysis and Operational Modal Analysis are the two approaches that were employed for experimentally identifying the modes and their corresponding frequencies. Results are provided from both the MLE and the Bayesian updating process and the effectiveness of each approach is discussed.