Search Results (44)

High-density polyethylene (HDPE) pipes are widely used in urban natural gas pipeline systems due to their excellent mechanical and chemical properties. However, welding joints are critical weak points in these pipelines, and defects, such as cold welding—caused by reduced temperature or/and insufficient pressure—pose significant safety risks. Traditional non-destructive testing (NDT) methods face challenges in detecting cold welding defects due to the polymer’s complex structure and characteristics. This study presents a microwave-based NDT system for detecting cold welding defects in thermal fusion welds of HDPE pipes. The system uses a focusing antenna with a resonant cavity, connected to a vector network analyzer (VNA), to measure changes in microwave parameters caused by cold welding defects in thermal fusion welds. Experiments conducted on HDPE pipes welded at different temperatures demonstrated the system’s effectiveness in identifying areas with a lack of fusion. Mechanical and microstructural analyses, including tensile tests and scanning electron microscopy (SEM), confirmed that cold welding defects lead to reduced mechanical properties and lower material density. The proposed microwave NDT method offers a sensitive, efficient approach for detecting cold welds in HDPE pipelines, enhancing pipeline integrity and safety. Full article

Background: In the context of rapid urbanization, the need for building safety and durability assessment is becoming increasingly prominent. Objective: The aim of this paper is to review the strengths and weaknesses of the main non-destructive testing (NDT) techniques in construction engineering, with a focus on the application of deep learning in image-based NDT. Design: We surveyed more than 80 papers published within the last decade to assess the role of deep learning techniques combined with NDT in automated inspection in construction. Results: Deep learning significantly enhances defect detection accuracy and efficiency in construction NDT, particularly in image-based techniques such as infrared thermography, ground-penetrating radar, and ultrasonic inspection. Multi-technology fusion and data integration effectively address the limitations of single methods. However, challenges remain, including data complexity, resolution limitations, and insufficient sample sizes in NDT images, which hinder deep learning model training and optimization. Conclusions: This paper not only summarizes the existing research results, but also discusses the future optimization direction of the target detection network for NDT defect data, aiming to promote intelligent development in the field of non-destructive testing of buildings, and to provide more efficient and accurate solutions for building maintenance. Full article

In this study, we propose an enhanced LiDAR-based mapping and localization system that utilizes a camera-based YOLO (You Only Look Once) algorithm to detect and remove dynamic objects, such as vehicles, from the mapping process. GPS, while commonly used for localization, often fails in urban environments due to signal blockages. To address this limitation, our system integrates YOLOv4 with LiDAR, enabling the removal of dynamic objects to improve map accuracy and localization in high-traffic areas. Existing methods using LiDAR segmentation for map matching often suffer from missed detections and false positives, degrading performance. Our approach leverages YOLOv4’s robust object detection capabilities to eliminate potentially dynamic objects while retaining static environmental features, such as buildings, to enhance map accuracy and reliability. The proposed system was validated using a mid-size SUV equipped with LiDAR and camera sensors. The experimental results demonstrate significant improvements in map-matching and localization performance, particularly in urban environments. The system achieved RMSE (Root Mean Square Error) reductions compared to conventional methods, with RMSE values decreasing from 0.9870 to 0.9724 in open areas and from 1.3874 to 1.1217 in urban areas. These findings highlight the ability of the Vision + LiDAR + NDT method to enhance localization performance in both simple and complex environments. By addressing the challenges of dynamic obstacles, the proposed system effectively improves the accuracy and robustness of autonomous navigation in high-traffic settings without relying on GPS. Full article

Spatially referenced and geometrically accurate laser scanning is essential for the safety monitoring of an underground mine. However, the spatial inconsistency of point clouds collected by heterogeneous platforms presents challenges in achieving seamless fusion. In our study, the terrestrial and handheld laser scanning (TLS and HLS) point cloud registration method based on the coarse-to-fine strategy is proposed. Firstly, the point features (e.g., target spheres) are extracted from TLS and HLS point clouds to provide the coarse transform parameters. Then, the fine registration algorithm based on identical area extraction and improved 3D normal distribution transform (3D-NDT) is adopted, which achieves the datum unification of the TLS and HLS point cloud. Finally, the roughness is calculated to downsample the fusion point cloud. The proposed method has been successfully tested on two cases (simulated and real coal mine point cloud). Experimental results showed that the registration accuracy of the TLS and HLS point cloud is 4.3 cm for the simulated mine, which demonstrates the method can capture accurate and complete spatial information about underground mines. Full article

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article

Girth welds are weak points in pipelines, and failures occur frequently. In a gas transmission pipeline, a girth weld experienced cracking, prompting a failure analysis using experimental methods and finite element analysis (FEA). Experimental results showed that X-ray non-destructive testing (NDT) revealed cracks, porosity, and lack of fusion in the girth weld. However, the hardness and microstructure of the material showed no abnormalities. During operation, the pipeline experienced an increase in soil cover and was subjected to ground subsidence and vehicle loads. Finite element analysis was conducted on the defective girth weld under different conditions, including varying soil cover depths, different levels of subsidence, and varying vehicle loads, to examine the pipeline’s stress response. The results indicated that the combination of soil cover, subsidence, and vehicle loads led to pipeline failure, whereas none of these factors alone was sufficient to cause girth weld failure. To prevent such failures from occurring again, the following measures are recommended: strengthen on-site welding quality control of girth welds, conduct inspections for defects in girth welds of in-service pipelines, and promptly address any defects that exceed acceptable limits. Full article

Additive manufacturing (AM) defects present significant challenges in fiber-reinforced thermoplastic composites (FRTPCs), directly impacting both their structural and non-structural performance. In structures produced through material extrusion-based AM, specifically fused filament fabrication (FFF), the layer-by-layer deposition can introduce defects such as porosity (up to 10–15% in some cases), delamination, voids, fiber misalignment, and incomplete fusion between layers. These defects compromise mechanical properties, leading to reduction of up to 30% in tensile strength and, in some cases, up to 20% in fatigue life, severely diminishing the composite’s overall performance and structural integrity. Conventional non-destructive testing (NDT) techniques often struggle to detect such multi-scale defects efficiently, especially when resolution, penetration depth, or material heterogeneity pose challenges. This review critically examines manufacturing defects in FRTPCs, classifying FFF-induced defects based on morphology, location, and size. Advanced NDT techniques, such as micro-computed tomography (micro-CT), which is capable of detecting voids smaller than 10 µm, and structural health monitoring (SHM) systems integrated with self-sensing fibers, are discussed. The role of machine-learning (ML) algorithms in enhancing the sensitivity and reliability of NDT methods is also highlighted, showing that ML integration can improve defect detection by up to 25–30% compared to traditional NDT techniques. Finally, the potential of self-reporting FRTPCs, equipped with continuous fibers for real-time defect detection and in situ SHM, is investigated. By integrating ML-enhanced NDT with self-reporting FRTPCs, the accuracy and efficiency of defect detection can be significantly improved, fostering broader adoption of AM in aerospace applications by enabling the production of more reliable, defect-minimized FRTPC components. Full article

In multi-source fusion deformation-monitoring methods that utilize fiber Bragg grating (FBG) data and other data types, the lack of FBG constraint points in edge regions often results in inaccuracies in fusion results, thereby impacting the overall deformation-monitoring accuracy. This study proposes a multi-source fusion deformation-monitoring calibration method and develops a calibration model that integrates vision and FBG multi-source fusion data. The core of this model is a normal distribution transform (NDT)–convolutional neural network (CNN)–self-attention (SA) calibration network. This network enhances continuity between points in point clouds using the NDT module, thereby reducing outliers at the edges of the fusion results. Experimental validation shows that this method reduces the absolute error to below 0.2 mm between multi-source fusion calibration results and high-precision measured point clouds, with a confidence interval of 99%. The NDT-CNN-SA network offers significant advantages, with a performance improvement of 36.57% over the CNN network, 14.39% over the CNN–gated recurrent unit (GRU)–convolutional block attention module (CBAM) network, and 9.54% over the CNN–long short term memory (LSTM)–SA network, thereby demonstrating its superior generalization, accuracy, and robustness. This calibration method provides smoother and accurate structural deformation data, supports real-time deformation monitoring, and reduces the impact of assembly deviation on product quality and performance. Full article

This study aims to analyze the performance of dissimilar titanium alloy Ti-5Al-2.5 Sn and stainless-steel SS 304 joints using three non-destructive testing (NDT) methods such as radiographic testing, visual and microstructural evaluation. Gas tungsten arc welding (GTAW) was performed to join the base metals by incorporating the multi-interlayer of Cu-Nb. The performance of dissimilar joints was evaluated in terms of quality and strength at a welding current of 40 and 60 amperes, and a fixed gas flow rate and welding speed of 20 lit/min and 150 mm/min, respectively. Radiography and visual results indicated severe cracks, voids and incomplete fusion in the specimen welded at a higher current and no such flaws in the specimen welded at a low current. Microstructural results revealed that a dendritic structure was achieved in the fusion zone at a low current that enhanced the ultimate tensile strength (UTS) to 248 MPa while brittle cracks were observed at the Ti-Cu side at higher currents, which reduced the strength to 160 MPa. Full article

To optimally preserve and manage our civil structures, we need to have accurate information about their (1) geometry and dimensions, (2) boundary conditions, (3) material properties, and (4) structural conditions. The objective of this article is to show how imaging and image fusion using non-destructive testing (NDT) measurements can support structural engineers in performing accurate structural evaluations. The proposed methodology involves imaging using synthetic aperture focusing technique (SAFT)-based image reconstruction from ground penetrating radar (GPR) as well as ultrasonic echo array (UEA) measurements taken on multiple surfaces of a structural member. The created images can be combined using image fusion to produce a digital cross-section of the member. The feasibility of this approach is demonstrated using a case study of a prestressed concrete bridge that required a bridge load rating (BLR) but where no as-built plans were available. Imaging and image fusion enabled the creation of a detailed cross-section, allowing for confirmation of the number and location of prestressing strands and the location and size of internal voids. This information allowed the structural engineer of record (SER) to perform a traditional bridge load rating (BLR), ultimately avoiding load restrictions being imposed on the bridge. The proposed methodology not only provides useful information for structural evaluations, but also represents a basis upon which the digitalization of our infrastructure can be achieved. Full article

High-precision positioning is a fundamental requirement for autonomous vehicles. However, the accuracy of single-sensor positioning technology can be compromised in complex scenarios due to inherent limitations. To address this issue, we propose an adaptive multi-sensor fusion localization method based on the error-state Kalman filter. By incorporating a tightly coupled laser inertial odometer that utilizes the Normal Distribution Transform (NDT), we constructed a multi-level fuzzy evaluation model for posture transformation states. This model assesses the reliability of Global Navigation Satellite System (GNSS) data and the laser inertial odometer when GNSS signals are disrupted, prioritizing data with higher reliability for posture updates. Real vehicle tests demonstrate that our proposed positioning method satisfactorily meets the positioning accuracy and robustness requirements for autonomous driving vehicles in complex environments. Full article

Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions. Subsequently, the aligned inspection images were merged using complementary techniques, including maximum, weighted-averaging, depth-driven combination (DDC), and wavelet decomposition. The results indicated that although individual inspections may have lower mean absolute error (MAE) ratings than fused images, the use of complementary fusion improved defect identification in the total number of detections across numerous layers of the structure. Detection errors are analyzed, and a tendency to overestimate defect sizes is revealed with individual inspection methods. This study concludes that complementary fusion provides a more comprehensive understanding of overall defect detection throughout the thickness, highlighting the importance of leveraging multiple modalities for improved inspection outcomes in structural analysis. Full article

Automated ultrasonic testing (AUT) is a nondestructive testing (NDT) method widely employed in industries that hold substantial economic importance. To ensure accurate inspections of exclusive AUT data, expert operators invest considerable effort and time. While artificial intelligence (AI)-assisted tools, utilizing deep learning models trained on extensive in-laboratory B-scan images, whether they are augmented or synthetically generated, have demonstrated promising performance for automated ultrasonic interpretation, ongoing efforts are needed to enhance their accuracy and applicability. This is possible through the evaluation of their performance with experimental ultrasonic data. In this study, we introduced a real-world ultrasonic B-scan image dataset generated from proprietary recorded AUT data during industrial automated girth weld inspection in oil and gas pipelines. The goal of inspection in our dataset was detecting a common type of defect called lack of fusion (LOF). We experimentally evaluated deep learning models for automatic weld defect detection using this dataset. Our assessment covers the baseline performance of state-of-the-art (SOTA) models, including transformer-based models (DETR and Deformable DETR) and YOLOv8. Their flaw detection performance in ultrasonic B-scan images has not been reported before. The results show that, without heavy augmentations or architecture customization, YOLOv8 outperforms the other models with an F1 score of 0.814 on our test set. Full article

Corrosion is one of the critical factors leading to the failure of steel structures. Ultrasonic C-scans are widely used to identify corrosion damage. Limited by the range of C-scans, multiple C-scans are usually required to cover the whole component. Thus, stitching multiple C-scans into a panoramic image of the area under detection is necessary for interpreting non-destructive testing (NDT) data. In this paper, an image mosaic method for ultrasonic C-scan based on scale invariant feature transform (SIFT) is proposed. Firstly, to improve the success rate of registration, the difference in the probe starting position in two scans is used to filter the matching pairs of feature points obtained by SIFT. Secondly, dynamic programming methods are used to search for the optimal seam path. Finally, the pixels in the overlapping area are fused by fade-in and fade-out fusion along the seam line. The improved method has a higher success rate of registration and lower image distortion than the conventional method in the mosaic of ultrasonic C-scan images. Experimental results show that the proposed method can stitch multiple C-scan images of a testing block containing artificial defects into a panorama image effectively. Full article

Laser-based powder bed fusion of metals (PBF-LB/M) is a widely applied additive manufacturing technique. Thus, PBF-LB/M represents a potential candidate for the processing of quenched and tempered (Q&T) steels such as 42CrMo4 (AISI 4140), as these steels are often considered as the material of choice for complex components, e.g., in the toolmaking industry. However, due to the presence of process-induced defects, achieving a high quality of the resulting parts remains challenging in PBF-LB/M. Therefore, an extensive quality inspection, e.g., using process monitoring systems or downstream by destructive or non-destructive testing (NDT) methods, is essential. Since conventionally used downstream methods, e.g., X-ray computed tomography, are time-consuming and cost-intensive, micromagnetic NDT measurements represent an alternative for ferromagnetic materials such as 42CrMo4. In this context, 42CrMo4 samples were manufactured by PBF-LB/M with different process parameters and analyzed using a widely established micromagnetic measurement system in order to investigate potential relations between micromagnetic properties and porosity. Using multiple regression modeling, relations between the PBF-LB/M process parameters and six selected micromagnetic variables and relations between the process parameters and the porosity were assessed. The results presented reveal first insights into the use of micromagnetic NDT measurements for porosity assessment and process parameter optimization in PBF-LB/M-processed components. Full article