Inspection Of Structures Research Articles

Non-destructive evaluation and Structural Health Monitoring of impact induced damage in composite structures using ultrasonic approaches and shearography

Composite structures are massively used thanks to the high strength-to-weight ratio, making them ideal for lightweight and durable structures in aviation. However, Impact may arise at any time during lifetime on composite aerostructures and ensuring their structural integrity via fast inspection is essential to meet airworthiness criteria. Multimodal NDT (Non-Destructive Testing) inspection and characterization of composite structures is a critical process to verify the quality of the composite material and identify any defects. In this context, the paper shows a comparison of several techniques as nondestructive methods: shearography, laser ultrasonics and phased array ultrasonics. In particular, using shearography and laser ultrasonics as non-contact approaches can increase the inspection speed and reliability compared to the state of the art phased array ultrasonics, which is the standard approach used in the aerospace field. For this reason, a sample of interest to the aerospace industry is subject to different low velocity impacts which are characterized by different energies leading to barely visible damage. The results show the capabilities of both approaches in identifying small emerging defects along with the advantage and drawbacks of both methodologies through a critical review of performance thereof evaluating the parameters of their use. In addition, the same approaches are adopted on a stiffener composite plates and compared with continuous health monitoring approaches. In this regard, NDT approaches are demonstrated useful to enhance the SHM warnings.

SHM System Architecture based on Ultrasonic Guided Waves for Aircraft Application Scenarios

Structural health monitoring (SHM) based on ultrasonic guided waves is a novel technology using permanently attached actuator and sensor networks, data acquisition and data evaluation systems to enable in-service inspection of aircraft structures. A modular and decentralized SHM system is presented that can be adapted to various aircraft application scenarios. The central element of the SHM system is a robust sensor array based on piezocomposite technology that can be efficiently integrated into the manufacturing process of CFRP structures (via co-bonding). The sensor array consists of integrated piezoceramic transducers, temperature sensors, electrical cables and integrated electronic components. The decentralized system architecture of the individual electronic components reduces the installation and cabling effort during the assembly of aircraft structures as well as the system weight. Integration and installation aspects as well as various system architectures are discussed using an Airbus A350 door surround structure. Furthermore, an outlook of damage detection under representative flight loads is given.

Sensor platform for periodic integrity inspection of subsea structures with a permanently installed measuring system

Offshore structures are inspected with high costs and risks. In order to reduce both, the Fraunhofer IKTS has developed a sensor platform for automated periodic inspection by underwater vehicles like Remotely Operated Vehicles (ROV) or Autonomous Underwater Vehicles (AUV). The monitoring tasks are numerous. Temperature or pressure measurements are widely needed to ensure safe operation. Welded seams are a critical element of offshore infrastructure. If they fail due to high wave loads, extraordinary ice collision or due to fatigue behaviour, the entire structure becomes critical regarding the stability and safety. The Fraunhofer IKTS developed a technique for the permanent installation on subsea structures in order to enable a periodic inspection. The communication and energy supply are realized wireless by WiFi and inductive energy supply. The sensors used differ from application to application. For the welded seam inspection, shear wave laminable transducers ensure a flat design to prevent damage due to current or waves. Shear waves give a relative independency from fouling to make the interpretation of the data more comprehensive. The device has been tested in maritime environment for 2,5 years to ensure reliability. Furthermore, a modular setup with integrated transducers into the circuit board provide market relevant potential low prices on the long term. The connection unit has been designed in a way that also small ROV/AUV can operate the light-weight unit to ensure a broad applicability by almost any vehicle on the market.

Improving the identification of dynamic parameters of bridges by UAV: approach for SHM

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.

I-SUM: Integrated Smart Ultrasonic Monitoring

iSUM (Integrated Smart Ultrasonic Monitoring) System is an integrated embedded ultrasonic guided-waves-based system consisting of methodologies and algorithms performed over data obtained by iSUM_ES devices to obtain damage detection, positioning, and characterization . i_SUM_ES device ruled by ISUM_SW performs tests and transmits the collected data to the controller device where it is processed . Through algorithms and AI models developed “ad-hoc” for each field of application, iSUM_APP provides clear, concise, and easily interpretable information of the current state of structures. iSUM System allows structural integrity inspection tests to be carried out by a minimally trained operator. It can also run scheduled tests autonomously and deliver alerts, reports, data raw on-demand or in the case of structural damage detection . This system aims to provide useful information about the most typical damages on engineering structures, reducing the time of inspection, and avoiding systems of complex installation and operation. Moreover, their use is intended to reduce inspection costs during manufacturing and operation, increasing the quality, safety, and durability of the systems composed of the target structures. This paper presents the industry needs that motivate this development, the system architecture, and the applications for which it has been conceived.

Installation of portals in the sections of the column demolition during the re-equipment of Tagmet JSC workshops

Introduction. The article presents the materials on the activities, carried out in the pipe rolling shops of the Tagmet JSC during the modernization period, starting from 2007. The Research Institute of Building Constructions named after V.A. Koucherenko, conducted prolonged surveys of plant workshop structures in order to issue conclusions on their industrial safety and recommendations for the repair and strengthening of damaged elements in building structures for their further safe operation, including workshops, where the technological process has been changed due to the installation of new equipment.Aim. To demonstrate one of the ways to reconstruct industrial buildings using the example of workshops, located in the territory of the Tagmet JSC, based on a survey of combined structures, calculations and analyzes of their bearing capacity margins.Materials. The article provides the drawings of original metal structures and photographs of steel structures, already built to reinforce them and change the existing parameters of the building frame to suitable ones for new operating conditions.Results. Recommendations for structural strengthening, applicable for similar activities, are given. Due to the scientific and technical support for the design and construction of three modernized Tagmet workshops, the plant continuously increased its productivity in subsequent years.Conclusions. No defects and damage, which exceed the values, established by the current regulatory documents, and affect the operational suitability of buildings, were revealed during the inspection and operational safety examination of Tagmet plant structures. The results of the calculations, performed to the portal structures at the column demolition sections of the plant workshop building frames, proved the assurance of their strength and stability. The design solutions were successfully implemented during the reconstruction of workshops at the Tagmet JSC.

Domain-Specific Few-Shot Table Prompt Question Answering via Contrastive Exemplar Selection

As a crucial task in natural language processing, table question answering has garnered significant attention from both the academic and industrial communities. It enables intelligent querying and question answering over structured data by translating natural language into corresponding SQL statements. Recently, there have been notable advancements in the general domain table question answering task, achieved through prompt learning with large language models. However, in specific domains, where tables often have a higher number of columns and questions tend to be more complex, large language models are prone to generating invalid SQL or NoSQL statements. To address the above issue, this paper proposes a novel few-shot table prompt question answering approach. Specifically, we design a prompt template construction strategy for structured SQL generation. It utilizes prompt templates to restructure the input for each test data and standardizes the model output, which can enhance the integrity and validity of generated SQL. Furthermore, this paper introduces a contrastive exemplar selection approach based on the question patterns and formats in domain-specific contexts. This enables the model to quickly retrieve the relevant exemplars and learn characteristics about given question. Experimental results on the two datasets in the domains of electric energy and structural inspection show that the proposed approach outperforms the baseline models across all comparison settings.

Advanced image stitching method and evaluation for underwater structure inspection utilizing planar array cameras

The inspection of underwater structural health is crucial for comprehensive bridge health assessments. In underwater structure imaging, traditional methods include single-camera and binocular camera inspection. However, due to water turbidity and long working distances with a small field-of-view, obtaining clear and high-quality detection images with these methods takes much work. To address this problem, this paper presents a method for planar array image stitching based on Harris corner point extraction, utilizing the advantages of planar array cameras characterized by short working distances and wide field-of-view. The core contribution of this paper is the introduction of an innovative image sequence stitching algorithm utilizing Harris corner point extraction and the combination of the first proposed planar array cameras with the image sequence stitching algorithm, which solves the problem of long distance and small field-of-view during the underwater inspection. The image stitching method involves calibrating camera parameters with a checkerboard and stitching underwater images from planar array cameras to reveal underwater structural features. Furthermore, five quantitative evaluation metrics and the method for calculating the field-of-view loss rate are presented to evaluate and analyze the stitched images. A series of experiments were performed on concrete surfaces, aquatic and underwater, with a total field-of-view of the underwater image after stitching of 358.86 mm × 319.24 mm at a working distance of 160 mm. Five evaluation methods were used to quantitatively evaluate the quality of the stitched images and calculate the field-of-view loss rate of the images. The results indicate that the proposed method improves the ability to inspect underwater. The stitched images achieve notable metrics: an entropy of approximately 6.7, an average gradient of about 1.7, a spatial frequency of around 3.5, an edge strength of about 17, mutual information of approximately 1.2, and a field-of-view loss rate of <0.1, facilitating more effective underwater structure inspection.

General reinforcement learning control for AUV manoeuvring in turbulent flows

Turbulence induces unsteady loads on autonomous underwater vehicles (AUVs) and may present a significant navigation challenge. This leads to elevated risks of mission failure or vehicle damage in proximity to obstacles. A scenario of particular interest is inspection of offshore structures that needs to be carried out at short range inside a turbulent wake. This work presents a control strategy based on reinforcement learning (RL) that has been designed to handle such a complex manoeuvring scenario. Training and evaluation is carried out using computational fluid dynamics (CFD) simulations of a simplified 2D geometry of similar manoeuvring characteristics to that of an AUV moving in the horizontal plane. Due to the high cost of the simulations, substantial emphasis has been placed on improving sampling efficiency of RL training using experience transfer from a computationally less demanding environment and quicker filling of the replay buffer by applying geometric transformations to the observations. The agent can navigate not only in the training environment, but also in a previously unseen flow generated by a large circular cylinder immersed in a current. The developed control strategy has also been interfaced with a path-following algorithm that allowed the controlled vehicle to carry out an inspection task.

Remote collaborative framework for real-time structural condition assessment using Augmented Reality

Civil structures worldwide are confronted with a growing threat of structural deterioration, aggravated by various factors such as climate change, population growth, and increased traffic. The latent nature of these issues often leads to undetected vulnerabilities until a catastrophic failure occurs, resulting in substantial losses. To address this challenge, there is a critical need for improved structural monitoring, condition assessment, and maintenance practices. Traditional inspection methods, relying on visual estimation and heavy equipment for inaccessible areas, present formidable obstacles to inspectors. These methods impede the safe and fast examination of structural damage, complicating tracking of structural deterioration, and hindering efficient condition assessment. Recognizing these challenges, this paper proposes a remote collaborative framework to enhance the efficiency of structural inspections by leveraging the capabilities of Augmented Reality (AR), QR code, and 5G network. The proposed framework centers on real-time remote collaboration among on-site and off-site inspectors, aiming to elevate safety, accessibility, and overall inspection efficacy. The integration of real-time data sharing and collaboration facilitates immediate decision-making, enabling inspectors to proactively address structural vulnerabilities and prevent potential failures. This study concludes that the proposed framework effectively facilitates real-time structural condition assessment for on-site AR users. Simultaneously, off-site web users can instantly track the progression of data over time through the utilization of 5G technology. The proposed advanced AR framework effectively demonstrates real-time structural condition assessment through a lab-scale experimental beam and a full-scale bridge.

Automatic crack detection and structural inspection of cultural heritage buildings using UAV photogrammetry and digital twin technology

Current methods for rapidly inspecting structures are difficult to document and subjective. Automating the process with innovative technologies is necessary to overcome these limitations. Precisely monitoring cracking in cultural heritage buildings is vital as they pose a significant risk to their integrity. Recent advancements in artificial intelligence, including imaging equipment and computer vision, offer significant advantages for automating the detection of structural cracks. This study conducted tests to investigate utilizing unmanned aerial vehicle photogrammetry, a modern surveying method, for monitoring such inspections. Initially, a level of detail 3 digital model of the structure was generated. Finally, the metric accuracy of the detected cracks was analyzed. The cracks measured by the traditional method were considered as reference data and compared with the automatically detected cracks. As a result, 83 % of the cracks were detected by the modern techniques and the RMSE value was 0.802 cm because of the metric comparison.

Distributed collaborative inspections through smart infrastructure metaverse

We propose a real-time distributed collaborative system where on- and off-site inspectors perform synchronous structural inspections; called Smart Infrastructure Metaverse. Mixed Reality (MR) headsets are equipped with a holographic display that allows on-site inspectors to visualize digitized information overlaid on the structure. On the other hand, Virtual Reality (VR) headsets allow remote inspectors to visualize and interact with a pre-built 3D point cloud map. The MR inspectors execute inspection tasks such as adding annotations or taking measurements via MR interfaces, while the VR inspectors also perform the same tasks in a virtual environment, while the scene of the structure is viewed through 360°panoramic images aligned with the pre-built map using a proposed multi-shot localization algorithm. An experimental study has demonstrated the feasibility of SIM on a railroad bridge where two on-site MR inspectors and one remote VR inspector perform a visual inspection.

Lightweight network for millimeter-level concrete crack detection with dense feature connection and dual attention

Development of lightweight deep learning crack detection method is essential for the future deployment of mobile device-based structure inspection. The primary challenge involves the analysis and extraction of features from narrow cracks, typically 3–6 pixels wide, which are often obscured by noise such as water stains and shadows. The lightweight model should also maintain high accuracy while ensuring low computational complexity and a minimal number of parameters. To this end, this paper proposes YOLO v5-DE (Dense Feature Enhancement Connection, Efficient and Fast Convolution), a lightweight network based on the YOLO v5 architecture tailored to address these challenges, and constructs crack datasets captured at different heights to investigate the impact of different shooting distances on network performance. The network utilizes efficient convolutions and dense feature connections, with strategic reuse of filtered features from shallow layers, to significantly enhance the model's fine-grained feature information and gradient flow. The experimental results demonstrate that YOLO v5-DE achieves a detection accuracy of 96% for cracks in concrete structures. Compared to the improved YOLO v5 with EfficientViT as the backbone network, YOLO v5-DE achieves 4.7% increase on accuracy while requiring fewer computational resources, with only 1.4 million parameters and 3.6 Giga Floating point Operations Per Second (GFLOPS). Additionally, YOLO v5-DE reduces the inference time to 3.38 ms and increases the frame rate to 295.8 FPS. Moreover, the proposed lightweight network exhibits better detection performance when facing complex backgrounds and real-world environments.

Load Identification in Steel Structural Systems Using Machine Learning Elements: Uniform Length Loads and Point Forces

Actual load identification is a most important task solved in the course of (1) engineering inspections of steel structures, (2) the design of systems rising or restoring the bearing capacity of damaged structural frames, and (3) structural health monitoring. Actual load values are used to determine the stress–strain state (SSS) of a structure and accomplish various engineering objectives. Load identification can involve some uncertainty and require soft computing techniques. Towards this end, the article presents an integrated method combining basic provisions of structural mechanics, machine learning, and artificial neural networks. This method involves decomposing structures into primitives, using machine learning data to make projections, and assembling structures to make final projections for steel frame structures subjected to elastic strain. Final projections serve to identify parameters of point forces and loads distributed along the length of rods. The process of identification means checking the difference between (1) weight coefficient matrices applied to unit loads and (2) actual loads standardized using maximum load values. Cases of neural network training and parameters identification are provided for simple beams. The aim of this research is to enhance the reliability and durability of steel structures by predicting consequences of unfavorable load, including emergency impacts. The novelty of this study lies in the co-use of artificial intelligence elements and structural mechanics methods to predict load parameters using actual displacement curves of structures. This novel approach will enable engineering inspection teams to predict unfavorable load peaks, prevent emergency situations, and identify actual causes of emergencies triggered by excessive loading.

Introduction Remote-operated Underwater Vehicle for Inspection of Underwater Structure

Since many dams and bridges constructed in last century are facing serious safety issue resulting from leakage, erosion, and crack, it is increasingly urgent to develop more inspection and repair technologies that can be used for the maintenance operation. This essay introduces the practical application of underwater structure inspection conducted by remote-operated underwater vehicle. The applications include Mullaperiyar dam, Amagase dam, Aqua bridege, and Shorenji dam. And the application skills are summarized in inspecting small leaks of dam in clean water, inspection in turbid water, conducting survey in easy-entangled environment, and operation in flowing water. The advantages of remote-operated underwater vehicle compared with traditional divers are accumulated, and the remaining problem of existing remote-operated underwater vehicle are also summarized. This paper further provides experience of its operation in low-visibility water and stabilizing its position in turbulence, as well as solving the difficulties of getting the cables entangled in easy-winding water. Besides, an outlook is provided for remote-operated underwater vehicle’s development tendency in the field of underwater detection and reinforcement, with the purpose of addressing more critical problem in maintaining underwater structure in the future.

Preliminary trials of trackerless augmented reality in endoscopic endonasal surgery.

We present a novel method for augmented reality in endoscopic endonasal surgery. Our method does not require the use of external tracking devices and can show hidden anatomical structures relevant to the surgical intervention. Our method registers a preoperative 3D model of the nasal cavity to an intraoperative 3D model by estimating a scaled-rigid transformation. Registration is based on a two-stage ICP approach on the reconstructed nasal cavity. The hidden structures are then transferred from the preoperative 3D model to the intraoperative one using the estimated transformation, projected and overlaid into the endoscopic images to obtain the augmented reality. We performed qualitative and quantitative validation of our method on 12 clinical cases. Qualitative results were obtained from an ENT surgeon from visual inspection of the hidden structures in the augmented images. Quantitative results were obtained by measuring a target registration error using a novel transillumination-based approach. The results show that the hidden structures of interest are augmented at the expected locations in most cases. Our method was able to augment the endoscopic images in a sufficiently precise manner when the intraoperative nasal cavity did not deform considerably with respect to its preoperative state. This is a promising step towards trackerless augmented reality in endonasal surgery.

Physiological changes and the Mallampati classification: Tracheal intubation complications

Introduction: Difficulty in tracheal intubation is a significant factor in morbidity and mortality. Mallampati has therefore made it possible to predict it in advance. Its classification is based on the visualization of pharyngeal structures and is a simple anesthetic assessment. In practice, some circumstances can modify Mallampati's classification, making intubation more complex, and knowledge of these is of medical importance. Objectives: To review the literature on circumstances that alter Mallampati's classification. Methods: A literature review was carried out on possible physiological alterations responsible for changes in the Mallampati classification. We searched the Medline, Scielo and CRD databases for the terms: “Mallampati modification” and “Mallampati score” associated with “anaphylaxis” and “labor” and the articles were submitted to critical analysis. Results: Among the interferences in the Mallampati score is anaphylaxis, which results in upper airway edema, affecting the classification. In addition, pregnancy and labor can influence the score. Some factors such as breast growth, weight gain and pharyngeal edema increase the index in pregnant women. In addition, there is airway edema, explained by exertion and increased venous pressure in the upper body. In addition, fluid overload and the antidiuretic effect of oxytocin cause changes in the airways. Another situation is that of pediatric patients, where 13% of respiratory problems during anesthesia are related to intubation difficulties. In children between 4 and 8 years old without malformations, the index has proved applicable. However, it is impossible to use it in children under 4 years of age. Conclusions: The Mallampati score complements the judgment of a general examination that includes anamnesis and physical inspection of the craniofacial structure to identify risk factors, taking into account other situations that may interfere with the classification. Therefore, an understanding of its limitations is necessary for its correct application.

Optimal Duration of Observations During Seismic Inspection of Buildings

Studying the nature of the occurrence and propagation of microseismic tremors has not lost its relevance over the past few decades. Currently, the analysis of microseisms is the basis of some engineering and geological studies, including those aimed at the inspection of structures of various purposes. The procedure for preparing and conducting surveys is governed by a system of regulatory documents. However, the current codes and specifications represent a general guide for assessing the operational properties of building structures. Therefore, specific survey methods need to be clarified and detailed. Describes the experiment of examining the building regarding the dynamics of frequency characteristics within 24 hours. The observation system was implemented in the form of 16 points, evenly distributed over the volume of the building. Spectral analysis based on FFT was carried out to identify the time intervals within the 24-hour period with a pronounced maximum and minimum level of man-induced impact on the studied subject. During the hours of maximum exposure, the spectra were correlated according to records of different duration in terms of the correspondence of frequency components. The necessary and sufficient duration of registration of microseismic vibrations was derived to determine the frequency of natural vibration of a building when the observation points are located on the lower and upper floors.

Innovation, structural inspection for new mixed complexes: DNA binding, biomedical applications and molecular docking approaches

2-Guanidinobenzimidazole (BIG) and Imidiazole (I) ligands were utilized to synthesize Cu(II), VO(II), Ag(I), and Pd(II) as mixed ligand complexes. All studied molecules were characterized through various spectral, analytical and computational studies to find out their chemical structure. TGA was applied to identify the occurrence of H2O molecules besides the mono-nuclear property of isolated complexes. These complexes were proved through DFT study to confirm the coordinating site that was proposed and displays the optimal three-dimensional structures of the studied compounds. The binding affinity of the tested complexes with CT-DNA has been tested through agarose gel, electronic spectroscopy and viscosity measurements. Furthermore, the studied molecules might bind to CT-DNA electrostatically through exterior contact, replacement, intercalation and groove surface binding with good affinity. In-vitro anti-bacterial, anti-fungi, cytotoxic and antioxidant activities are performed for all studied compounds. MOE-docking simulation results indicate promising inhibitory features of BIGIPd complexes, in agreement with in-vitro results.

Synthesis, Biological and Molecular Docking Studies of Thiazole-Thiadiazole derivatives as potential Anti-Tuberculosis Agents.

Tuberculosis remains a global health threat, with increasing infection rates and mortality despite existing anti-TB drugs. The present work focuses on the research findings regarding the development and evaluation of thiadiazole-linked thiazole derivatives as potential anti-tuberculosis agents. We present the synthesis data and confirm the compound structures using spectroscopic techniques. The current study reports twelve thiazole-thiadiazole compounds (5 a-5 l) for their anti-tuberculosis and related bioactivities. This paper emphasizes compounds 5 g, 5 i, and 5 l, which exhibited promising MIC values, leading to further in silico and interaction analysis. Pharmacophore mapping data included in the present analysis identified tubercular ThyX as potential drug targets. The compounds were evaluated for anti-tubercular activity using standard methods, revealing significant MIC values, particularly compound 5 l, with the best MIC value of 7.1285 μg/ml. Compounds 5 g and 5 i also demonstrated moderate to good MIC values against M. tuberculosis (H37Ra). Structural inspection of the docked poses revealed interactions such as hydrogen bonds, halogen bonds, and interactions containing Pi electron cloud, shedding light on conserved interactions with residues like Arg 95, Cys 43, His 69, and Arg 87 from the tubercular ThyX enzyme.