ndt_1925-2026

Non Destructive Testing (NDT) 2025 - 2026

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Authors: Mohammadebrahim Bajgholi, Gilles Rousseau, Denis Thibault, Simon Francoeur. This study evaluates advanced Non-Destructive Testing (NDT) techniques for detecting flaws in welded repairs of martensitic stainless steel plates, focusing on Francis turbine runners. Phased Array Ultrasonic Testing (PAUT), Total Focusing Method (TFM), and Eddy Current Array (ECA) technologies were examined to assess repair integrity and identify flaws such as lack of fusion (LOF). The findings highlight the necessity of integrating advanced NDT techniques to ensure the structural integrity of critical components, particularly in high-stress applications such as turbine runners.

06 - Advanced Non-Destructive Testing Techniques for Welded Joint Repairs: A Case Study on Improving Inspection Reliability and Structural Integrity Assessment

Authors: Luca Paterlini, Andrea Marinelli, Andrea Brenna and Marco Ormellese This document proposes a risk map to manage phenomena linked to the fact that carbon steel structures that transport hydrocarbons have systems to prevent corrosion, including cathodic protection and insulating coatings, and that electrical interference, both AC and DC, can cause corrosion even with protection applied.

07 - Protection Criteria of Cathodically Protected Pipelines Under AC Interference.

08 - Natural van der Waals canalization lens for non-destructive nanoelectronic circuit imaging and inspection

Authors: Qingdong Ou, Shuwen Xue, Weiliang Ma, Jiong Yang, Guangyuan Si , Lu Liu, Gang Zhong, Jingying Liu, Zongyuan Xie, Ying Xiao, Kourosh Kalantar-Zadeh, Xiang Qi, Peining Li, Zhigao Dai, Huanyang Chen, Qiaoliang Bao Optical inspection is important in semiconductor wafer manufacturing for analyzing surfaces and defects. Traditional methods struggle with resolution and detecting buried structures. The authors suggest a new approach using a van der Waals canalization lens from a- MoO 3 crystals, achieving 15 nm resolution for both surface and buried imaging. This method offers better imaging without the issues faced by other lenses, enabling high-resolution inspection of buried nanoscale circuits, crucial for future semiconductor manufacturing.

Authors: Andrzej Katunin, Krzysztof Dragan, Marko Nagode, Krzysztof Lis, Kamil Joszko, Adam Cholewa, Jernej Klemenc, Simon Oman, Paweł Zak, Piotr Synaszko Hidden corrosion, if not found quickly, can greatly impact the strength and safety of aircraft. Enhancing non-destructive testing (NDT) methods is crucial for safe aircraft operation. The D-Sight technique is commonly used for detecting this corrosion, valued in the aerospace industry for its fast and low-cost inspections. One of the drawbacks

09 - Towards quantification of hidden corrosion using D-Sight non destructive testing technique

Authors: Yomi Femi, Ademola Romke Smart pipeline monitoring systems, integrating advanced technologies like sensors and AI, offer continuous, proactive monitoring of pipeline systems. These systems enable early detection of potential issues, enhance operational efficiency, and minimize environmental risks. They reduce downtime and repair costs, improve safety, and offer predictive maintenance, transforming pipeline infrastructure management in a sustainable and cost- effective manner.

01 - Smart Pipeline Monitoring System: Revolutionizing Pipeline Management

Authors: Maxime Fauconnier, Bhuvaneshwari Karunakaran, Alex Drago-González, William S. Y. Wong, Robin H. A. Ras, & Heikki J. Nieminen This study introduces waves propagating on an underwater superhydrophobic surface, allowing for the stabilization of a microscale gas layer called a plastron. The acoustic radiation force triggers kHz "plastronic waves" with a high propagation speed of up to 45 times faster than conventional deep water capillary waves. These waves are useful for non-destructive monitoring of plastron stability and spontaneous air diffusion in gas- undersaturated or supersaturated water conditions.

05 - Fast capillary waves on an underwater superhydrophobic surface

Authors: Qian Liu, Bin Xu, Xinhai Zhu, Ronglin Chen, and Hanbin Ge This study validated an interface debonding defect detection and localization approach for scaled underwater grouting jacket connections (GJCs) using surface wave measurements with piezoelectric lead zirconate titanate (PZT) actuation and sensing technology. The approach was tested and showed that the mimicked interface debonding defect was successfully detected, and the region of debonding was determined by intersecting abnormal wave propagation paths, providing a novel non-destructive debonding defect detection method for offshore wind turbine structures.

13 - Experimental Study on Interface Debonding Defect Detection and Localization in Underwater Grouting Jacket Connections with Surface Wave Measurements

Authors: Xianfeng Zeng, Wenji Ai, Zongchao Liu, and Xianling Wang This paper introduces an unsupervised enhancement framework for restoring underwater images with structural cracks. It combines physical modeling of underwater light transmission with a deep image translation architecture. The approach includes a multi- scale feature integration module and region-focused discriminator. Validated on a real-world dataset, it shows substantial improvements in visual quality and crack boundaries preservation.

14 - Unsupervised Restoration of Underwater Structural Crack Images via Physics-Constrained Image Translation and Multi- Scale Feature Retention

Authors: Renan Favarão da Silva, Edilson Gabriel Veruz, Alécio Julio Silva, Miguel Angelo de Carvalho Michalski, Gilberto Francisco Martha de Souza, Anderson Takehiro Oshiro This paper proposes a method for planning underwater inspections based on reliability and decision-making techniques. It identifies inspection needs, determines when to inspect, and selects the appropriate method. The method is demonstrated in a case study on a Brazilian FPSO platform, supporting maintenance planning and reliability.

15 - Underwater Inspection Planning Based on Reliability and Decision-Making techniques: An FPSO Platform Case Study.

10 - Helium Leak Testing Requirements in Fabrication of Cryogenic Storage Tanks

Authors: Kamal Dhandha, Bharat Joshi, Kamlesh Mistry, Manish Mishra, and Bhavesh Limbada The article discusses the significance and technological considerations surrounding the use of liquefied gases in various industrial applications. It emphasizes the importance of thermal insulation and specialized equipment for transporting and storing cryogenic liquids at extremely low temperatures. The article outlines the design, operation, and maintenance of cryogenic storage tanks, highlighting non-destructive testing methods, particularly helium leak detection, as crucial for ensuring the integrity and safety of cryogenic systems.

Authors: Li Jiaqing, Song Fei, Xiao Zidong, Zhu Longji, Chen Lan, Wei Zheliang This article discusses the growing concern of fatigue damage in steel bridge decks due to increased traffic loads and extended service life. Traditional detection methods are often ineffective, leading to the development of a novel approach that combines K-singular value decomposition (K-SVD) dictionary learning with convolutional neural networks (CNN) for better acoustic emission (AE) signal processing. The K-SVD algorithm filters out noise and improves signal clarity, allowing CNNs to classify AE signals with high accuracy (up to 95.32%). The integration of these methods provides a scalable and real-time solution for accurate crack detection.

11 - Investigation into fatigue micro-crack identification of steel bridge decks based on acoustic emission detection technology

12 - LBA-YOLO: A novel lightweight approach for detecting micro- cracks in building structures

Authors: Wenhao Ren, & Zuowei Zhong This article presents a new algorithm designed for the detection of building cracks, specifically targeting micro- cracks, which are vital for maintaining structural integrity and safety. The model utilizes YOLOv8n and introduces two key components, AC-LayeringNetV2 and RAK-Conv, aimed at enhancing feature extraction and improving performance in complex backgrounds. Experimental results show notable improvements in precision, recall, and mean average precision while also reducing the model’s size and computational complexity, underscoring the algorithm’s effectiveness in real-time crack detection.

16 - A Review on the Advances in Underwater Inspection of Subsea Infrastructure: Tools, Technologies, and Applications

Authors: Dulo Chukwemeka Wegner The article discusses the growing importance of subsea infrastructure for various sectors, including energy, telecommunications, and environmental monitoring. It highlights the increasing number of offshore installations and the need for effective inspection methods. The limitations of traditional diver-based inspection methods are contrasted with modern approaches such as Remotely Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs), and semi-autonomous ROVs. The paper further explores advanced inspection tools and technologies, analyzing their principles, applications, effectiveness, and the challenges posed by underwater environments.

19 - Design of Control System for Underwater Inspection Robot in Hydropower Dam Structures

Authors: Bing Zhao, Shuo Li, Xiangbin Wang, Mingyu Yang, Xin Yu, Zhaoxu Meng, and Gang Wan This article presents a comprehensive robotic control system designed for the inspection of underwater structural surfaces of hydropower dams. By addressing the challenges posed by harsh hydraulic conditions, the system aims to enhance operational efficiency and safety through innovative hardware and software components. Key findings from field tests validate the system’s precision and stability in real-world scenarios, significantly improving inspection capabilities for critical infrastructure

20 - Edge-Enhanced CrackNet for Underwater Crack Detection in Concrete Dams

Authors: Xiaobian Wu, Weibo Zhang, Guangze Shen, and Jinbao Sheng This article presents a novel dual-stage underwater crack detection method, termed Edge-Enhanced Underwater CrackNet (E²UCN), designed to address the challenges of detecting cracks in dam structures submerged underwater. Traditional crack detection approaches struggle due to environmental factors like water disturbances and poor visibility. The proposed method employs an advanced Cycle-GAN for image enhancement, focusing on retaining edge clarity and high-frequency textures specific to cracks, followed by a YOLOv11 model for precise object detection. The results indicate a significant improvement in detection accuracy compared to conventional methods.

21 - Deep Learning for Underwater Crack Detection: Integrating Physical Models and Uncertainty-Aware Semantic Segmentation

Authors: Wenji Ai, Zongchao Liu, Shuai Teng, Shaodi Wang, and Yinghou He This article discusses the significance of underwater crack detection for the safety of submerged infrastructures and introduces a novel deep learning framework designed to overcome existing challenges such as image degradation, limited data, and model generalization. The proposed framework enhances detection accuracy by integrating physics-based image enhancement, a dual-branch segmentation network for precise crack outline delineation, and an uncertainty-aware Transformer module to assess prediction confidence. Experimental results indicate that this approach outperforms current methods in turbid water and uneven lighting conditions, achieving notable performance metrics.

22 - Deep Learning Approaches for Fault Detection in Subsea Oil and Gas Pipelines: A Focus on Leak Detection Using Visual Data

Authors: Viviane F. da Silva, Theodoro A. Netto, and Bessie A. Ribeiro The article presents a study focused on enhancing leak detection methods in subsea oil and gas pipelines through a deep learning-based framework. By addressing the shortcomings of traditional leak detection methods, the study exploits a dataset generated from controlled experiments that simulate underwater leak conditions. Different Convolutional Neural Network architectures were analyzed for their efficiency in detecting gas and liquid leaks, achieving impressive performance metrics. The work notably contributes a novel dataset for the research community, promoting reproducibility and further advancements in automated subsea inspection technologies.

23 - Next-Gen Nondestructive Testing for Marine Concrete: AI-Enabled Inspection, Prognostics, and Digital Twins

Authors: Taehwi Lee, and Min Ook Kim The article reviews recent developments in Artificial Intelligence-integrated Non-Destructive Testing (AI-NDT) technologies for marine concrete structures, emphasizing their enhanced performance and practical applicability. It highlights the limitations of traditional NDT methods in detecting early damage due to challenging marine environments and noise interference. A systematic comparison between vision-based and signal-based AI- NDT techniques reveals significant improvements in detection accuracy, showcasing the effectiveness of AI in mitigating environmental distortions. The study concludes that AI-NDT not only automates data analysis but also supports the transition from traditional maintenance strategies to more proactive, data-driven approaches.

24 - Dynamic Illumination and Visual Enhancement of Surface Inspection Images of Turbid Underwater Concrete Structures

Authors: Xiaoyan Xu, Jie Yang, Lin Cheng, Chunhui Ma, Fei Tong, Mingzhe Gao, and Xiangyu Cao This article introduces a novel underwater image enhancement algorithm called DIVE (Dynamic Illumination and Vision Enhancement), which utilizes a framework for decoupling illumination and scattering effects. The DIVE algorithm effectively corrects luminance unevenness and color deviations, allowing for real-time processing of high-resolution images. Performance tests demonstrate significant improvements in image quality, particularly in environments with elevated sediment concentrations.

25 - Visual Crack Detection in Steel Structures with Convolutional Neural Networks

Authors: Andrii Kompanets, Davide Leonetti, (Bert) Snijder As many bridges near the end of their design lifespan, accurate and efficient inspections are vital. This article explores the integration of drones, crawling robots, and image processing techniques to automate bridge inspections. The focus is on automated crack segmentation using Convolutional Neural Networks (CNN), specifically combining the ConvNext network with an encoder-decoder network for improved segmentation. The study tests the methodology on a dataset of steel bridge images with pixel-wise crack annotations, revealing that false positives are prevalent due to complex backgrounds in the images.

26 - Enabling scalable inspection of offshore mooring systems using cost-effective autonomous underwater drones

Authors: Dong Trong Nguyen, Christian Lindahl Elseth, Jakob Rude Øvstaas, Nikolai Arntzen, Geir Hamre, and Dag-Børre Lillestøl The article discusses the pressing need for improved and cost-effective inspection methods in aquaculture as it expands to meet global food demands. It proposes an autonomous inspection framework utilizing a modular, vision-based pipeline built on the open-source Robot Operating System 2 (ROS 2) and implemented on a Blueye X3 underwater drone. This framework enhances safety, scalability, and regulatory compliance by integrating real-time image enhancement, object detection, and visual servoing for mooring inspection, while significantly reducing inspection costs compared to traditional methods.

27 - Optimization of the Number of Accelerometer Placements for Dynamic Identifi cation of a Historical Masonry Bridge

Authors: Cristiano Giuseppe Coviello, Fabio Rizzo, and Maria Francesca Sabbà This article discusses the optimization of accelerometer placement for dynamic identification of a historical three- arch masonry bridge in Bitonto, Italy. Through operational modal analysis (OMA), the study demonstrates that fewer accelerometers can effectively capture the bridge’s dynamic characteristics. This approach not only streamlines monitoring processes but also helps reduce costs and logistical challenges.

Authors: Xing Du, Yongfu Sun, Yupeng Song, Wanqing Chi, Zongxiang Xiu, Xiaolong Zhao, & Dong Wang This study addresses the challenge of maintaining submarine pipeline stability under dynamic conditions like waves and seismic events, focusing on buried pipelines vulnerable to sediment liquefaction. By combining finite element analysis with machine learning, it enables rapid and accurate prediction of liquefaction risks from wave- current interactions. Findings reveal that pipelines influence sediment liquefaction direction and extent, with sides being more susceptible than tops or bottoms, while also stabilizing surrounding seabed sediments. The integrated model enhances assessment speed without sacrificing precision.

04 - A novel strategy for fast liquefaction detection around marine pipelines: a finite element-machine learning approach

28 - Enhancing Structural Integrity Assessment Through Non- Destructive Evaluation

Authors: Wael Zatar, Felipe Mota Ruiz, and Hien Nghiem This study develops a non-destructive testing method using ground-penetrating radar (GPR) to estimate reinforcement bar diameter in concrete structures. The key innovation is the normalized amplitude-diameter- depth (NADD) relationship, which uses an exponential attenuation model to connect reflected electromagnetic wave amplitude to rebar diameter and cover depth. Normalization eliminates variations in signal energy and antenna coupling, enabling reliable amplitude comparisons across different depths.

29 - Real-Time Flange Bolt Loosening Detection with Improved YOLOv8 and Robust Angle Estimation

Authors: Yingning Gao, Sizhu Zhou, and Meiqiu Li This paper proposes a lightweight, integrated deep learning framework for flange bolt loosening detection and rotation angle estimation, critical for safety in civil, mechanical, and aerospace infrastructure. To overcome limitations of conventional methods (e.g., missed detections, poor feature representation, weak multi-scale fusion), it employs a Mobile Vision Transformer (MobileViT) backbone for balanced local-global feature extraction. Key innovations include large separable convolutions with channel-spatial attention in spatial pyramid pooling, content-aware upsampling, and bidirectional multi-scale fusion, enabling accurate, real- time detection of small, low-contrast bolts.

34 - A lightweight underwater image and video enhancement method based on multi-scale feature fusion

Authors: Gaosheng Luo, Haiyang Li, Huanhuan Wang, Hengshou Sui, Xuewen Zhang, Rongjun Zhang, Bocheng Chen, and Zhe Jiang Underwater image degradation significantly impacts autonomous underwater vehicles (AUVs), subsea inspections, and marine monitoring. This paper introduces UIVE, a lightweight enhancement algorithm that addresses these issues. UIVE's innovation lies in a dynamic multi-scale jump connection that combines downsampling and upsampling features, preserving multi- scale details. By replacing batch normalization layers with residual blocks, UIVE reduces computational complexity while improving feature migration. An adaptive brightness correction module compensates for brightness reduction after nonlinear mapping.

35 - Mixed reality based in-pipe inspection

Authors: Almat Dalabekov, Dias Akimbay, Tolegen Akhmetov, Darkhan Zholtayev, and Azamat Yeshmukhametov Pipelines are vital for resource transport, but their expansion presents inspection and maintenance challenges. This study introduces a novel pre-maintenance approach using Meta Quest Pro mixed reality (MR) technology. This method allows for detailed visualization and analysis of in-pipe defects before physical intervention, improving maintenance planning by providing engineers with comprehensive defect insights and enabling them to select the most appropriate tools and procedures.

Author: V Nolting Conventional eddy current testing is limited to detecting surface defects due to shallow penetration depth. The paper introduces motion-induced eddy current testing, which uses relative motion between a permanent magnet and the test object to generate Lorentz forces and measure electron flow disturbances caused by defects. This method achieves penetration depths two orders of magnitude greater than conventional techniques, enabling detection of deep-lying defects and significantly improving non-destructive evaluation.

02 - A Theoretical Investigation of the Penetration Depth in Motion Induced Eddy Current Testing

Authors: Jiuhao Ge, Fei Xu, Jia Chen, Weiyu Jiang, and Zhiyong Deng Nuclear fuel claddings act as the primary barrier against fission products, with eddy current testing used to measure chromium/oxide layer thickness. This paper develops a validated analytical model based on second- order vector potential for coils outside multilayer pipes, analyzes probe parameters on nuclear cladding models, and provides a design flowchart using resonance frequency and quality factor. The new probe shows superior sensitivity.

03 - Design of eddy current testing probe for thickness evaluation of multilayers on nuclear fuel claddings

17 - Comparative Evaluation of Ultrasonic, Radiographic, and Eddy Current Testing for Defect Detection in Pressure Vessels

Authors: Ayuns luz, Stephen Eteng, Jayson Paul This research quantitatively compares UT, RT, and ECT for assessing pressure vessel integrity, aiming to identify optimal techniques for planar flaws, volumetric inclusions, and wall thinning to support RBI strategies. Using simulated PoD curves for advanced methods, findings show UT excels for planar defects like cracks, RT for volumetric flaws like porosity, and PECT as the sole non- intrusive option for CUI and thinning. No single modality is universally superior; effective management requires a complementary multimodal NDT protocol tailored to material, environment, and defect type.

18 - Non-Destructive Eddy Current Testing System Based on Discrete Wavelet Transform

Authors: Zhengtao Xia, and Jia Jia This paper introduces an eddy current testing (ECT) system using the Discrete Wavelet Transform (DWT) to detect surface micro-damage in metal components, addressing limitations of conventional frequency-domain methods. The system features a DDS-generated 1 MHz excitation signal, an IQ demodulator, and a 24-bit ADC for precise signal acquisition. Haar wavelet-based single-level decomposition isolates defect responses in high-frequency detail coefficients. Experimental results show a 3.64 dB improvement in SNR for a 1 mm notch, outperforming time-domain processing by 0.36 dB.

30 - Editorial: Artificial intelligence in visual inspection

Authors: Yanlong Cao, Zhijie Xu, Wenhan Zeng, and Yuanping Xu AI is revolutionizing visual inspection by surpassing human limitations in accuracy, efficiency, and scalability across fields like manufacturing, medicine, and public safety. This research explores solutions to key challenges, robustness in harsh conditions, real-world applicability, and precision in critical domains like healthcare. Two highlighted studies demonstrate this progress: Liu et al. developed a parallel joint encoding network that enhances low-light drone imagery while detecting objects, improving performance in dark environments for surveillance and rescue missions; Deshpande et al. created an automated rider helmet violation detection system using a hybrid pipeline, achieving high accuracy.

31 - Next-Gen Nondestructive Testing for Marine Concrete: AI-Enabled Inspection, Prognostics, and Digital Twins

Authors: Taehwi Lee, and Min Ook Kim This study reviews recent advances in Artificial Intelligence- integrated Non-Destructive Testing (AI-NDT) for marine concrete, highlighting improvements in detection accuracy and practical applicability. AI integration improves detection accuracy by 17–25% compared to traditional methods and compensates for environmental distortions and standardizes data interpretation. AI-NDT automates data interpretation and supports predictive maintenance frameworks by linking data acquisition, digital twin-based prediction, and lifecycle monitoring, transforming reactive maintenance strategies into sustainable, intelligence-driven management for marine infrastructure.

32 - Pipeline External Defect Detection Based on Magnetically Focused Eddy Current Testing System

Authors: Zihao Gao, and Huaguang Zhang Eddy current testing is widely used for pipeline inspection but faces challenges with ferromagnetic materials due to reduced penetration depth and noise interference. This paper proposes a magnetically focused eddy current testing (MFECT) system that improves signal-to-noise ratio, mitigates the skin effect, and increases penetration depth in ferromagnetic pipes. The study includes comparative analyses of penetration depth with and without magnetic focusing, determines optimal excitation frequencies through simulation, and validates the method experimentally.

33 - Invariant in eddy current testing via dimentional analysis

Authors: Vincento Mottola, Alessandro Sardellitti, Philippo Milano, Luigi Ferrigno, Marco Larascca, Antonello Tamburrino The Buckingham's π theorem has been applied in Non- destructive Testing & Evaluation (NdT&E) to develop methods for multi-parameter estimation using dimensional analysis. This approach reduces the number of parameters affecting measurements by forming dimensionless π- groups. The focus is often on estimating a subset of variables, such as thickness, electrical conductivity, and lift- off, while isolating signals from uncertain parameters. The paper introduces a systematic method to derive invariant transformations for frequency domain Eddy Current Testing data, which is validated through experiments.

36 - Enhanced Defect Classification for Steel Plates via Magneto- Optical Imaging with Deep Feature Fusion

Authors: Qunfeng Niu, Changtong Zhao, Yixin Cao, Di Chai, Lingchao Li, Kun Xu, and Yunqing Tang Steel plate integrity is vital for infrastructure, but defects can cause failures. Traditional nondestructive testing methods have limitations, such as ultrasonic testing being affected by surface roughness and eddy current testing missing deep flaws. Magnetic optical imaging (MOI) is promising but faces challenges like noisy images and ineffective feature extraction. This work presents a new real-time MOI defect detection system using deep feature fusion. It achieved high accuracy and speed, making it a strong solution for inspecting steel plate defects in industry.

37 - Computational efficient methods for the uncertainty quantification of eddy current testing problems and their practical applications

Authors: M. Koll, D. Wöckinger, G. Bramerdorfer, S. Schuster, S. Scheiblhofer, N. Gstöttenbauer, and J. Reisinger Eddy current testing evaluates electrically conductive materials non-destructively. Accurate material parameter estimation depends on signal sensitivity and noise resistance. A key challenge is that multiple parameters can affect signals similarly, complicating unique identification. This paper presents a computational framework using stochastic, deterministic, and finite element models to assess parameter estimability and identifiability in eddy current testing of metallic coated steel sheets.

38 - Modelling Eddy Current Testing of Gaps in Carbon Fibre Structures Based on Spline Approximation

Authors: Till Schulze, Maren Rake, Dirk Hofmann, Johannes Mersch, Martin Schulze, Chokri Cherif, and Henning Heuer Defects like gaps and fibre misalignment impair carbon fibre composites, risking failure. Eddy current testing detects them early but needs validated EM models. This paper proposes novel FEM using spline approximation for fibre heterogeneity, adaptive mesh refinement, gap modeling, and inter-laminar currents from resistance measurements. Validation compares virtual scans with real eddy current scans of dry fabric with/without gaps.

39 - A Thickness Determination Method Based on Energy-Dispersive X-Ray Fluorescence and Application in Zirconium Alloy Coatings

Authors: Liu Yongli, Zhang Jian, Hou Cheng, and Ma Hailiang Accurate coating thickness measurement is vital for zirconium alloy nuclear fuel claddings, but conventional methods like SEM are slow, destructive, and require complex sample preparation. This study introduces a non- destructive, rapid, and accurate fundamental parameter method based on energy-dispersive X-ray fluorescence (EDXRF). It calculates theoretical X-ray fluorescence intensity, including absorption and enhancement effects, and iteratively determines coating thickness by matching it to measured intensity. Validated on chromium coatings on zirconium substrates, the method achieves ≤±5% relative error versus cross-sectional SEM, confirming its reliability.

40 - Revisiting the MV Estonia Accident Using Numerical Simulations with a Statistical Approach

Authors: Shinwoong Kim, and Petri Valanto Since the 1994 sinking of the MV Estonia, the root cause of the accident has been studied, but a new ROV video from 2020 revealed unknown damage on the starboard side, prompting the Estonian Safety Investigation Bureau (ESIB) to reassess. The Hamburg Ship Model Basin (HSVA) aims to determine if this damage occurred while the ship was afloat or during the sinking. Clarifying this is crucial, as it may challenge previous conclusions on the accident's cause. Numerical simulations and statistical analyses are used to explore various damage scenarios in sea conditions.

41 - Development of Pulsed Eddy Current Nondestructive Testing: A Review

Authors: Qian Huang, Ruilin Wang, Jingxi Hu, Hao Jiao, Chi Zhang, Zhitao Hou, Chenxi Duan, Xueyuan Long, and Liangchen Lv Pulsed Eddy Current Testing (PECT) is a nondestructive testing method used for finding defects and characterizing materials in industries like petrochemicals, new energy, and aerospace. The rise in new energy sources has led to a higher need for inspecting non- ferromagnetic materials, such as austenitic stainless steel. However, challenges like electromagnetic leakage and lift- off effects require improvements in PECT technology. This paper reviews recent advances in PECT, including sensors, modeling, signal processing, and applications.

42 - NDT-Based Condition Assessment and Structural Safety Evaluation of a Reinforced Cement Concrete Water Tank in a Coastal Region: A Case Study

Authors: Marakkath Nidhi, Praveena Jagatheesan, and Shimol Philip This study assesses the structural integrity of a 10 lakh L RCC water tank in Thirunallar, Karaikal, under severe coastal exposure. Methods included visual inspection, NDT (Schmidt rebound hammer and UPV tests) for compressive strength and quality, plus STAAD Pro modeling using PWD drawings. Results showed in situ strength of 12-43 MPa exceeding induced stresses of 2.42- 10.59 MPa, confirming adequate safety margins. Deterioration observed was due to environmental durability issues, not structural deficiency. Suitable repair and rehabilitation methods are recommended based on NDT and analysis outputs.

43 - Free-induction-decay magnetometer for pulsed eddy current testing

Authors: Zixuan Xiao, Kaiwen Yi, Zejia Lin, and Xiang Peng The Buckingham's π theorem has been applied in Non- Pulsed-eddy-current testing (PECT) enables nondestructive evaluation of conductors, mineral exploration, and UXO detection. Replacing pickup coils with a free-induction- decay (FID) atomic magnetometer improves sensitivity to weak low-frequency signals. Using enhanced optical pumping and instantaneous phase retrieval, the device reaches 0.45 pT/√Hz noise and 4.2 kHz bandwidth. The synchronized pumping amplifies spin polarization while inducing eddy-current fields, from which the characteristic time constant τ is extracted. Validation on copper, aluminum, zinc, and lead demonstrates applicability to magnetic induction tomography and remote-field eddy- current testing.

44 - Integrated Qualification Workflow for AINSI 316 and 304L Stainless Steels Using Destructive and Eddy Current Non- Destructive Testing

Authors: Jude Emele, Ales Sliva, Mahalingam Nainaragaram Ramasamy, Silvie Brozova, and Ján Dižo This study creates a qualification process that combines mechanical testing, microstructural analysis, and eddy current testing on the same materials. It involves forged 316 and rolled 304L stainless steels that were treated at 700°C. Mechanical tests included tensile and Charpy impact tests at different temperatures, along with microscopy for grain size and fracture analysis. The results showed stable mechanical properties and no significant ECT indications.

45 - Eddy current testing in the quantitative assessment of the degradation state in X10CrMoVNb9 – 1 (P91) power engineering steel

Authors: Grzegorz Tytko, Karolina Piotrowska, Jun Tu, Mateusz Kopec Ferritic–martensitic P91 steel is commonly used in high- temperature power plants for its good resistance to creep and stability. However, long-term use and poor heat treatment can harm its microstructure, weakening the steel and reducing its lifespan. This study looked at how controlled heat treatments from 200°C to 600°C affected the eddy current response of P91 steel. The findings showed a strong link between the electromagnetic response and changes in microstructure, proving that eddy current testing is effective for monitoring P91 steel's condition.

46 - An Intelligent Wireless Ultrasound Platform for Nondestructive Testing (NDT) and Structural Monitoring (SM)

Author: Gaofeng Sha Recent advancements in IoT, software, and AI have made these technologies attractive for NDT and SM, driving a trend called NDE 4.0. While research demonstrates their potential, most solutions remain academic due to high complexity, limiting broader adoption. This study addresses this gap by introducing an affordable, user- friendly wireless ultrasound platform for NDT and SM. Demonstrated through corrosion monitoring and defect inspection, the platform integrates AI models—including an autoencoder for unsupervised learning and a CNN for supervised defect detection—offering a unified, intelligent solution for inspection and monitoring challenges.

47 - Crack Suppression and Performance Analysis of Novel Ni60 Alloy Hardbanding on Drillpipes via Laser Cladding

Author: Lilan Liu, Shen Wang, Yingkai Qin, Boyu Guo, Ziying Wu, and Feiyan Han To address severe wear and corrosion in deep/ultra-deep well drillpipes, this study developed a crack-free Ni60 alloy hardbanding via laser cladding. Direct Ni60 deposition on 4137H steel caused cracking due to thermal expansion mismatch; introducing a 316L interlayer relieved interfacial thermal stress, enabling a dense, metallurgically bonded 316L-Ni60 gradient coating. Characterization (SEM, XRD, microhardness) confirmed a homogeneous microstructure, microhardness 2.2× higher than 4137H steel, ~50% lower wear rate, and superior corrosion resistance, demonstrating strong potential for Ni60-based hardbanding in demanding drilling applications.

48 - Non-Contact Ultrasonic Assessment of Corrosion in Steel Specimens

Author: Lukas Peterson, Andrei Zagrai, ThankGod Nwokocha, and T. David Burleigh Ultrasonic thickness resonance using Electro-Magnetic Acoustic Transducers (EMATs) detects and measures corrosion in thin-walled steel structures like nuclear waste containers. EMATs operate without contact, but need high-power equipment due to low efficiency. Tests showed frequency shifts linked to corrosion levels, with notable effects on resonant peak characteristics. This method is effective for monitoring corrosion.

49 - AI-driven Prediction of Structural Steel Mechanical Properties Using Ultrasound and Eddy Current Testing

Author: Zaki Bin Muhammad Sham, Shagea Alqawzai, Haris Sufyan Bin Mohamad, Thi Qui Nguyen, Mingshan Zhao, and Zhang Linyun Reusing structural steel can lower embodied carbon but requires accurate knowledge of reclaimed members' mechanical properties. Conventional destructive tensile tests are impractical due to high costs and time demands. This study explores ultrasound testing (UT) and eddy current testing (ECT) combined with data-driven models (e.g., Random Forest) to predict mechanical properties and classify strength grades of salvaged steel. ECT achieved 92% classification accuracy, while UT reached 82%. UT-based yield strength prediction had a 5.86% mean absolute error, enabling reasonably accurate estimations without prior steel grade knowledge.