Search Results (98)

Environmentally assisted cracking (EAC) can be an aggressive degradation mechanism for materials in safety-critical applications across a variety of industries, particularly when combined with cyclic mechanical loading. Corrosion fatigue, a prominent form of EAC, often affects tubular components such as piping, heat exchangers, and boiler tubes in chemical, refining, and power generation industries. This study presents the design and validation of a low-cost, automated test system for evaluating EAC under controlled laboratory conditions. The system integrates electromechanical loading, force measurement, and closed-loop control of temperature and pH. Crack growth is monitored using a compliance-based method calibrated using finite element analysis. Environmental control loops were validated for stability and responsiveness. Performance was demonstrated through tests on carbon steel specimens in acidic chloride solution and polymethylmethacrylate (PMMA) specimens in xylene solvents. The system demonstrated accurate load control, environmental stability, and sensitivity to crack extension. The test system also enabled detection of crack closure behavior in carbon steel specimens resulting from corrosion product buildup during immersion in acidic chloride solution. Additionally, the system effectively distinguished varying impacts of environmental severity in PMMA testing (100% xylene vs. 50% xylene–50% ethanol), confirming its suitability for comparative studies. This test platform enables efficient, repeatable evaluation of EAC fatigue performance across a range of materials and environments. Full article

Anomaly detection is a pressing issue, particularly in industrial images. Detecting weld defects in radiographic images is a challenge due to the small signal-to-noise ratio (SNR) and the limited availability of data. In this paper, we propose an automated weld defect detection method using Normalizing Flows (NFs). We employed various state-of-the-art NF architectures with different feature extractors to detect defects in radiographic images of welds, comprehensively comparing the results with radiographic images of welded steel pipes collected from industrial sites. The results show that the combination of CFlow-AD with a wide residual network-50-2 (WRN-50-2) outperformed the other methods, indicating its effectiveness in anomaly detection. Full article

To reduce welding deformation during the automated welding of thick-walled pipes in shipbuilding and thereby improve welding quality, a segmented multi-layer multi-pass welding sequence optimization and process improvement strategy is proposed. Firstly, based on a welding model for thick-walled pipes, a multi-layer multi-pass welding trajectory equation is established. A double-ellipsoidal moving heat source is adopted to design a circular multi-layer multi-pass double-ellipsoidal heat source model. Secondly, three circular pipe workpieces with different wall thicknesses are selected, and four segmented welding sequences are simulated using welding finite element analysis (FEA). Finally, based on the optimal segmented welding sequence, the welding process is improved, and optimal welding process parameters are determined based on deformation and residual stress analysis. The results of the segmented multi-layer multi-pass welding sequence optimization show that the skip-symmetric welding method yields the best results for thick-walled circular pipes. Compared to other welding sequences, it reduces welding deformation by an average of 6.50% and welding stress by an average of 5.37%. In addition, process improvement tests under the optimal welding sequence indicate that the best welding quality is achieved under the following conditions: for 10 mm thick pipes—200 A current, 24 V voltage, and 11.5 mm/s welding speed; for 15 mm thick pipes—215 A, 24.6 V, and 10 mm/s; and for 20 mm thick pipes—225 A, 25 V, and 11 mm/s. Full article

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to the risk involved. This work presents and experimentally validates an autonomous, dual-I-AUV (Intervention–Autonomous Underwater Vehicle) system capable of assembling rigid pipeline segments through coordinated actions in a confined underwater workspace. The first I-AUV is a Girona 500 (4-DoF vehicle motion, pitch and roll stable) fitted with multiple payload cameras and a 6-DoF Reach Bravo 7 arm, giving the vehicle 10 total DoF. The second I-AUV is a BlueROV2 Heavy equipped with a Reach Alpha 5 arm, likewise yielding 10 DoF. The workflow comprises (i) detection and grasping of a coupler pipe section, (ii) synchronized teleoperation to an assembly start pose, and (iii) assembly using a kinematic controller that exploits the Girona 500’s full 10 DoF, while the BlueROV2 holds position and orientation to stabilize the workspace. Validation took place in a 12 m × 8 m × 5 m water tank. Results show that the paired I-AUVs can autonomously perform precision pipeline assembly in real water conditions, representing a significant step toward fully automated subsea construction and maintenance. Full article

In the Architecture, Engineering, and Construction (AEC) industry, traditional labor efficiency evaluation methods have limitations, while computer vision technology shows great potential. This study aims to develop a potential automated construction efficiency evaluation framework. We propose a method that integrates keypoint processing and extraction using the BlazePose model from MediaPipe, action classification with a Long Short-Term Memory (LSTM) network, and construction object recognition with the YOLO algorithm. A new model framework for action recognition and work hour statistics is introduced, and a specific construction scene dataset is developed under controlled experimental conditions. The experimental results on this dataset show that the worker action recognition accuracy can reach 82.23%, and the average accuracy of the classification model based on the confusion matrix is 81.67%. This research makes contributions in terms of innovative methodology, a new model framework, and a comprehensive dataset, which may have potential implications for enhancing construction efficiency, supporting cost-saving strategies, and providing decision support in the future. However, this study represents an initial validation under limited conditions, and it also has limitations such as its dependence on well-lit environments and high computational requirements. Future research should focus on addressing these limitations and further validating the approach in diverse and practical construction scenarios. Full article

Heating, ventilation, and air-conditioning systems account for 40–60% of the energy consumed in commercial buildings, and much of this load originates from sub-optimal piping layouts in chiller-plant rooms. This study presents an automated routing framework that couples Building Information Modeling (BIM) with an enhanced A* search to produce collision-free, low-resistance pipelines while simultaneously guiding component selection. The algorithm embeds protective buffer zones around equipment, reserves maintenance corridors through an attention-based cost term, and prioritizes 135° elbows to cut local losses. Generated paths are exported as Industry Foundation Classes (IFC) objects for validation in a BIM digital twin, where hydraulic feedback drives iterative reselection of high-efficiency devices—including magnetic-bearing chillers, cartridge filters and tilted-disc valves—until global pressure drop and life-cycle cost are minimized. In a full-scale shopping-mall retrofit, the method significantly reduces pipeline resistance and operating costs, confirming its effectiveness and replicability for sustainable chiller-plant design. Full article

Cleaning operations in narrow pipelines are often hindered by limited maneuverability and low efficiency, necessitating the development of a high-performance and highly adaptable robotic solution. To address this challenge, this study proposes a pipeline-cleaning robot specifically designed for the heat-exchange tubes of industrial heat exchangers. The robot features a dual-wheel cross-drive configuration to enhance motion stability and integrates a gear–rack-based alignment mechanism with a cam-based propulsion system to enable autonomous deployment and cleaning via a flexible arm. The robot adopts a modular architecture with a separated body and cleaning arm, allowing for rapid assembly and maintenance through bolted connections. A vision-guided control system is implemented to support accurate positioning and task scheduling within the primary pipeline. Experimental results demonstrate that the robot can stably execute automatic navigation and sub-pipe cleaning, achieving pipe-switching times of less than 30 s. The system operates reliably and significantly improves cleaning efficiency. The proposed robotic system exhibits strong adaptability and generalizability, offering an effective solution for automated cleaning in confined pipeline environments. Full article

Fiber-reinforced polymer (FRP) pipes have emerged as a preferred alternative to conventional metallic piping systems in various industries, including chemical processing, marine, and oil and gas industries, owing to their superior corrosion resistance, high strength-to-weight ratio, and extended service life. However, ensuring the long-term reliability and structural integrity of FRP pipes presents significant challenges, primarily because of their anisotropic and heterogeneous nature, which complicates defect detection and characterization. Traditional non-destructive testing (NDT) methods, which are widely applied, often fail to address these complexities, necessitating the adoption of advanced digital techniques. This review systematically examines recent advancements in digital NDT approaches with a particular focus on their application to composite materials. Drawing from 140 peer-reviewed articles published between 2016 and 2024, this review highlights the role of numerical modeling, simulation, machine learning (ML), and deep learning (DL) in enhancing defect detection sensitivity, automating data interpretation, and supporting predictive maintenance strategies. Numerical techniques, such as the finite element method (FEM) and Monte Carlo simulations, have been shown to improve inspection reliability through virtual defect modeling and parameter optimization. Meanwhile, ML and DL algorithms demonstrate transformative capabilities in automating defect classification, segmentation, and severity assessment, significantly reducing the inspection time and human dependency. Despite these promising developments, this review identifies a critical gap in the field: the limited translation of advanced digital methods into field-deployable solutions specifically tailored for FRP piping systems. The unique structural complexities and operational demands of FRP pipes require dedicated research for the development of validated digital models, application-specific datasets, and industry-aligned evaluation protocols. This review provides strategic insights and future research directions aimed at bridging the gap and promoting the integration of digital NDT technologies into real-world FRP pipe inspection and lifecycle management frameworks. Full article

The integration of Building Information Modeling (BIM) with intelligent optimization techniques can significantly enhance the design efficiency of water distribution networks (WDNs). Despite this, the dynamic interoperability between BIM platforms and hydraulic simulation tools remains limited. This study introduces DyEHS (Dynamo–EPANET–Harmony Search), a novel workflow integrating Autodesk Civil 3D, EPANET, and Harmony Search via Dynamo, to address this gap. DyEHS enables the automated optimization of pipe diameters and network layouts, aiming to minimize capital costs while satisfying hydraulic constraints. In a real-world case study, DyEHS achieved a 15% reduction in the total pipe network costs compared to traditional uniform-diameter designs, while ensuring that all nodes maintained a minimum pressure of 25 m. This quantifiable improvement highlights the tool’s potential for practical engineering applications, offering a robust, adaptable, and fully integrated BIM-based solution for WDN design. Full article

The adoption of artificial intelligence (AI) in sports training has the potential to revolutionize skill development, yet cost-effective solutions remain scarce, particularly in table tennis. To bridge this gap, we present an intelligent training system leveraging computer vision and machine learning for real-time performance analysis. The system integrates YOLOv5 for high-precision ball detection (98% accuracy) and MediaPipe for athlete posture evaluation. A dynamic time-wrapping algorithm further assesses stroke effectiveness, demonstrating statistically significant discrimination between beginner and intermediate players (p = 0.004 and Cohen’s d = 0.86) in a cohort of 50 participants. By automating feedback and reducing reliance on expert observation, this system offers a scalable tool for coaching, self-training, and sports analysis. Its modular design also allows adaptation to other racket sports, highlighting broader utility in athletic training and entertainment applications. Full article

The accurate installation position of corrugated pipes is critical for ensuring the quality of prestressed concrete box girders. Given that these pipes can span up to 30 m and are deeply embedded within rebars, manual measurement is both labor-intensive and prone to errors. Meanwhile, automated recognition and measurement methods are hindered by high equipment costs and accuracy issues caused by rebar occlusion. To balance cost effectiveness and measurement accuracy, this paper proposes a method that utilizes an RGB-D camera and deep learning. Firstly, an optimal registration scheme is selected to generate complete point cloud data of pipes from segmented data captured by an RGB-D camera. Next, semantic segmentation is applied to extract the characteristic features of the pipes. Finally, the center points from cross-sectional slices are extracted and curve-fitting is performed to recognize and measure the pipes. A test was conducted in a simulated precast factory environment to validate the proposed method. The results show that under the optimal fitting scheme (BP neural network with circle fitting constraint), the average measurement errors for the three pipes are 2.2 mm, 1.4 mm, and 1.6 mm, with Maximum Errors of −5.8 mm, −4.2 mm, and −5.7 mm, respectively, meeting the standard requirements. The proposed method can accurately locate the pipes, offering a new technical pathway for the automated recognition and measurement of pipes in prefabricated construction. Full article

This paper presents a comparative study of binarization techniques for automated defect detection in dye penetrant testing (DPT) images. We evaluate established methods, including global, adaptive, and histogram-based thresholding, against three novel machine learning-assisted approaches, Soft Binarization (SoBin), Delta Binarization (DeBin), and Convolutional Autoencoder Binarization (AutoBin), using a real-world dataset from an automated DPT system inspecting stainless steel pipes. Performance is assessed with both pixel-level and region-level metrics, with particular emphasis on the influence of defect saturation. Defect saturation is quantified as the mean saturation value of all pixels belonging to a given defect, and defects are grouped into ten categories spanning from low (60–68) to high (132–140) mean saturation. Our results demonstrate that for lower mean defect saturation values, methods such as AutoBin_Triangle, HSV_global_70, and SoBin achieve superior Intersection over Union (IoU) and high true positive rates. In contrast, methods based primarily on global thresholding of the saturation channel tend to perform competitively on images with higher defect saturation levels, reflecting their sensitivity to stronger color signals. Moreover, depending on the method, nearly perfect region-level true positive rates (${\mathrm{TPR}}_{\mathrm{region}}$) or minimal false positive rates (${\mathrm{FPR}}_{\mathrm{region}}$) can be attained, emphasizing the trade-off that different models offer distinct strengths and weaknesses, which necessitates selecting the optimal method based on the specific quality control requirements and risk tolerances of the industrial process. These findings underscore the critical importance of defect saturation as a cue for both human and computer vision systems and provide valuable insights for developing robust automated quality control and predictive quality algorithms. Full article

This study seeks to automate the Rapid Entire Body Assessment (REBA) in dentistry with Artificial Intelligence (AI) technologies, notably MediaPipe, to improve accuracy and obviate the necessity for expert judgment. This research utilizes time-synchronized videos and averages across frames to mitigate mistakes resulting from visual occlusion and over- or underestimation, respectively. The REBA scores of the observed dentists were evaluated and compared with the conventional single image-based method. Among the evaluated dentists, 83% of dentists are at high risk, and the other 17% of dentists are at very high risk, requiring solutions to lower their REBA scores and prevent musculoskeletal disorders (MSDs). The individual REBA point profiles differed, necessitating a collective study through response surface methodology (RSM) utilizing Design Expert software. The RSM model exhibited substantial results, as indicated by R² = 0.9055 and p = < 0.0001 values. A linear regression equation was established, and contour graphs depicted the relative variation of REBA points. The optimized REBA score profile establishes a maximum attainable threshold for dentists, directing them towards the lower scores. This streamlined contour functions as a design restriction for creating ergonomic solutions in dental practice. Full article

The colonization of pressurized irrigation networks by zebra mussels (Dreissena polymorpha) poses a serious risk to water delivery, reducing pipeline capacity and potentially causing complete blockages. Despite the critical need for early detection and effective management, existing methods often rely on costly, time-consuming field inspections or indirect indicators with limited accuracy. To address this gap, we present SIMZEBRA, a cloud-based toolkit that assesses invasions using hydraulic monitoring and simulation. The tool employs the Normalized Pressure Method, comparing real-time pressure data from transducers with EPANET simulations of a mussel-free network. An optimization process adjusts friction coefficients in network segments until simulated and measured pressures align, enabling the generation of infestation maps over user-defined time periods. Compared to conventional approaches, SIMZEBRA enhances detection accuracy, reduces the reliance on physical inspections, and provides a scalable, automated solution for continuous monitoring. The tool also integrates experimental data to establish relationships between mussel density, pipeline diameter, and roughness. In the presented case study, roughness increases of up to 10 mm were detected in affected pipes, while local head losses at hydrants ranged between 9 and 11 m, depending on flow conditions. Developed in R with CPU parallelization, the toolkit operates remotely on a cloud server, ensuring fast, efficient, and cost-effective detection and management of zebra mussel infestations. This approach improves early warning capabilities and supports proactive invasive species management in pressurized irrigation networks. Full article

Small and mid-sized water utilities face persistent challenges due to limited technical expertise and financial resources, impeding effective management and decision making. This study presents an enhanced version of the MACS Water Smart application, which integrates artificial intelligence and EPANET-based hydraulic modelling with GIS (geographical information system) functionalities to optimize water supply networks. The methodology was applied to the potable water system of Khelvachauri, Georgia, which experiences significant pressure deficits, particularly in its southern area during peak consumption time. By employing machine learning algorithms, the WS tool automates tasks such as pipe diameter optimization and pressure recovery, gradually eliminating the total need for expert intervention. The AI-powered optimization achieved pressure increases above 25 m, reduced flow velocities below 1.5 m/s, improved pumping efficiency by 15%, and lowered leakage rates by 8%. Additionally, computational time was reduced by 35% compared with traditional methods. These findings validate the performance of AI-based hydraulic simulation and its ability to replicate engineering decisions. Furthermore, the tool provides a scalable solution for planning future network expansions. This work highlights the practicality of combining AI and hydraulic modelling for sustainable water management in resource-constrained settings, emphasizing its cost-effectiveness and potential for widespread adoption in small and mid-sized utilities. Full article