Search Results (31)

Detecting underwater structural defects is vital for hydraulic engineering safety. Diverse patterns of underwater structural defects, i.e., the morphology and scale characteristics, pose difficulties on feature representability during detection. Any single feature morphology is insufficient to fully characterize diverse types of underwater defect patterns. This paper proposes a novel hybrid neural network to enhance feature representation of underwater structural multi-defects, which in turn improves the accuracy and adaptability of underwater detection. Three types of convolution operations are combined to build Hybrid Aggregation Network (HanNet), enhancing the morphological representation for diverse defects. Considering the scale difference of diverse defects, the Multi-Scale Shared Feature Pyramid (MSFP) is proposed, facilitating adaptive representation for diverse sizes of structural defects. The defect detection module leverages an Adaptive Spatial-Aware Attention (ASAA) at the backend, enabling selective enhancement of salient defect features. For model training and evaluation, we, for the first time, build an underwater structural multi-defects sonar image dataset containing a wide range of typical defect types. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods, significantly improving defect detection accuracy, and provides an effective solution for detecting diverse structural defects in complex underwater environments. Full article

Aiming at the problem of image quality degradation caused by turbid water, non-uniform illumination, and scattering effect in the surface defect detection of underwater concrete structures, firstly, the concrete surface images under different shooting distances, different sediment concentrations, and different illumination conditions were collected through laboratory experiments to simulate the concrete surface images of a reservoir dam with higher sediment concentration and deeper water depth. On this basis, an underwater image enhancement algorithm named DIVE (Dynamic Illumination and Vision Enhancement) is proposed. DIVE solves the problems of luminance unevenness and color deviation in stages through the illumination–scattering decoupling processing framework, and combines efficient computing optimization to achieve real-time processing. The lighting correction of Gaussian distributions (dynamic illumination module) was processed in stages with suspended particle scattering correction (visual enhancement module), and the bright and dark areas were balanced and color offset was corrected by local gamma correction in Lab space and dynamic decision-making of G/B channel. Through thread pool parallelization, vectorization and other technologies, the real-time requirement can be achieved at the resolution of 1920 × 1080. Tests show that DIVE significantly improves image quality in water bodies with sediment concentration up to 500 g/m³, and is suitable for complex scenes such as reservoirs, oceans, and sediment tanks. Full article

To detect damage in underwater pile foundations, we propose a new method based on the diffusion probability model and improved PointMLP. First, PCA-ICP registration is carried out for the point cloud data from different stations using a sonar system. A variety of filtering algorithms and the Random Sample Consensus (RANSAC) method are employed to obtain a complete point cloud of the pile foundation. The pile foundation defect point cloud is generated and enhanced based on the diffusion probability model. The feature attention mechanism is added to the PointMLP, and then the improved PointMLP is trained to identify the defect of the pile foundation. In our study, the point cloud of a wharf pile foundation was collected, and the experimental results effectively identified the damage to the pile foundation. Up to 95% accuracy was achieved for the calculated volume. The volume error of the damage was 0.0756 m³, with an accuracy of 95.238%. Thus, this method can provide technical support for detecting underwater pile foundation defects and avoiding the occurrence of major accidents. Full article

As critical infrastructure, hydropower dams require efficient and accurate detection of underwater structural surface defects to ensure their safety. This paper presents the design and implementation of a robotic control system specifically developed for underwater dam inspection in hydropower stations, aiming to enhance the robot’s operational capability under harsh hydraulic conditions. The study includes the hardware design of the control system and the development of a surface human–machine interface unit. At the software level, a modular architecture is adopted to ensure real-time performance and reliability. The solution employs a hierarchical architecture comprising hardware sensing, real-time interaction protocols, and an adaptive controller, and the integrated algorithm combining a fixed-time disturbance observer with adaptive super-twisting controller compensates for complex hydrodynamic forces. To validate the system’s effectiveness, field tests were conducted at the Baihetan Hydropower Station. Experimental results demonstrate that the proposed control system enables stable and precise dam inspection, with standard deviations of multi-degree-of-freedom automatic control below 0.5 and hovering control below 0.1. These findings confirm the system’s feasibility and superiority in performing high-precision, high-stability inspection tasks in complex underwater environments of real hydropower dams. The developed system provides reliable technical support for intelligent underwater dam inspection and holds significant practical value for improving the safety and maintenance of major hydraulic infrastructure. Full article

Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid environments. To address these limitations, we propose a compact image enhancement framework called Wavelet Fusion with Sobel-based Weighting (WWSF). This method first corrects global color and luminance distributions using multiscale Retinex and gamma mapping, followed by local contrast enhancement via CLAHE in the L channel of the CIELAB color space. Two preliminarily corrected images are decomposed using discrete wavelet transform (DWT); low-frequency bands are fused based on maximum energy, while high-frequency bands are adaptively weighted by Sobel edge energy to highlight structural features and suppress background noise. The enhanced image is reconstructed via inverse DWT. Experiments on real-world sluice gate datasets demonstrate that WWSF outperforms six state-of-the-art methods, achieving the highest scores on UIQM and AG while remaining competitive on entropy (EN). Moreover, the method retains strong robustness under high turbidity conditions (T ≥ 35 NTU), producing sharper edges, more faithful color representation, and improved texture clarity. These results indicate that WWSF is an effective preprocessing tool for downstream tasks such as segmentation, defect classification, and condition assessment of hydraulic infrastructure in complex underwater environments. Full article

Interface debonding between high-strength grouting materials and the inner surfaces of steel tubes in grouting jacket connections (GJCs), which have been widely employed in offshore wind turbine support structures, negatively affects their mechanical behavior. In this study, an interface debonding defect detection and localization approach for scaled underwater GJC specimens using surface wave measurements with piezoelectric lead zirconate titanate (PZT) actuation and sensing technology was validated experimentally. Firstly, GJC specimens with artificially mimicked interface debonding defects of varying dimensions were designed and fabricated in the lab, and the specimens were immersed in water to replicate the actual underwater working environment of GJCs in offshore wind turbine structures. Secondly, to verify the feasibility of the proposed interface debonding detection approach using surface wave measurements, the influence of the height and circumferential dimension of the debonding defects on the output voltage signal of PZT sensors was systematically studied experimentally using a one pitch and one catch (OPOC) configuration. Thirdly, a one pitch and multiple catch (OPMC) configuration was further employed to localize and visualize the debonding defect regions. An abnormal value analysis was carried out on the amplitude of the output voltage signals from PZT sensors with identical wave traveling paths, and the corresponding abnormal surface wave propagation paths were identified. Finally, based on the influence of interface debonding on the surface wave measurements mentioned above, the mimicked interface debonding defect was detected successfully and the region of debonding was determined with the intersection of the identified abnormal wave travelling paths. The results showed that the mimicked debonding defect can be visualized. The feasibility of this method for interface debonding defect detection in underwater GJCs was confirmed experimentally. The proposed approach provides a novel non-destructive debonding defect detection approach for the GJCs in offshore wind turbine structures. Full article

Interface debonding between the steel tube and grouting materials in grouting jacket connections (GJCs) of offshore wind turbine supporting structures leads to negative effects on the load-carrying capacity and safety concerns. In this paper, an interface debonding defect detection and localization approach for scale underwater GJC specimens using surface wave measurement is proposed and validated numerically. A multi-physics finite element model (FEM) of underwater GJCs with mimicked interface debonding defects, surrounded by water, and coupled with surface-mounted piezoelectric lead zirconate titanate (PZT) patches is established. Under the excitation of a five-cycle modulated signal, the surface stress wave propagation, including transmission, diffraction, and reflection, within the outer steel tube, grouting material, and inner steel tube is simulated. The influence of mimicked interface debonding defects of varying dimensions on stress wave propagation is systematically analyzed through stress wave field distributions at distinct time intervals. Additionally, the response of surface-mounted PZT sensors in the underwater GJC model under a one-pitch-one-catch (OPOC) configuration is analyzed. Numerical results demonstrate that the wavelet packet energy (WPE) of the surface wave measurement from the PZT sensors corresponding to the traveling path with a mimicked interface debonding defect is larger than that without a defect. To further localize the debonding region, a one pitch and multiple catch (OPMC) configuration is employed, and an abnormal value analysis is conducted on the WPEs of PZT sensor measurements with identical and comparable wave traveling patches. The identified debonding regions correspond to the simulated defects in the models. Full article

An innovative design of a hydrophone based on a piezoelectric composite film of $AlN/S{c}_{0.2}A{l}_{0.8}N$ is presented. By designing a non-uniform composite sensitive layer, the dielectric loss and defect density are significantly reduced, while the high-voltage electrical characteristics of scandium-doped aluminum nitride are retained. X-ray diffraction analysis shows that the sensitive films have excellent crystal quality (FWHM is 0.34°). According to the standard underwater acoustic calibration test, the device exhibits full directivity with a minimum deviation of ±0.5 dB at 1 kHz frequency, sound pressure sensitivity of −162.9 dB (re: 1 V/$\mathsf{\mu }$Pa) and equivalent noise density of 46.1 dB (re: 1 $\mathsf{\mu }$Pa/√Hz). The experimental results show that the comprehensive performance of the piezoelectric heterostructure hydrophone meets the standard of commercial high-end hydrophones while maintaining mechanical stability, and provides a new solution for underwater acoustic sensing. Full article

Hydropower stations and dams play a crucial role in water management, ecology, and energy. To meet the requirements of underwater dam defect detection, this study develops a streamlined underwater vehicle design and operational framework inspired by bionic principles. A parametric modeling approach was employed to propose the vehicle’s streamlined configuration. Using CFD simulations, hydrodynamic coefficients were calculated and validated through towing experiments in a pool. The hydrodynamic stability of the vehicle was assessed and verified through these analyses. Additionally, various configurations were generated using a free deformation method. An optimization function was established with resistance and stability as the objectives, and the optimal result was derived based on the function’s calculation outcomes. The study designed a high-metacentric underwater vehicle, inspired by the seahorse’s shape, and introduced a novel stability evaluation method. Simulations were conducted to analyze the vehicle’s variable attack angle, drift angle, pitching, and rotational motion at a forward three-throttle speed. The results demonstrate that the vehicle achieves static stability in both the horizontal and vertical planes, as well as dynamic stability in the vertical plane, but exhibits limited dynamic stability in the horizontal plane. After optimizing the original configuration, the forward resistance was reduced by 2.15%, while the horizontal plane dynamic stability criterion C_H was improved by 35.29%. Full article

This study proposes a concrete dam underwater apparent defect detection algorithm named YOLOv8s-UEC for intelligent identification of underwater defects. Due to the scarcity of existing images of underwater concrete defects, this study establishes a dataset of underwater defect images by manually constructing defective concrete walls for the training of defect detection networks. For the defect feature ambiguity that exists in underwater defects, the ConvNeXt Block module and Efficient-RepGFPN structure are introduced to enhance the feature extraction capability of the network, and the P2 detection layer is fused to enhance the detection capability of small-size defects such as cracks. The results show that the mean average precision (mAP_0.5 and mAP_0.5:0.95) of the improved algorithm are increased by 1.4% and 5.8%, and it exhibits good robustness and considerable detection effect for underwater defects. Full article

Digital twin technology, as a data-driven and model-driven innovation means, plays a crucial role in the process of digital transformation and intelligent upgrading of the marine industry, helping the industry to move towards a new stage of more intelligent and efficient development. In order to solve the defects of the Autonomous Underwater Vehicle (AUV) underwater support platform structure deformation field, digital twin technology and model reduction technology are applied to an AUV underwater support platform, and a five-dimensional digital twin model of the AUV underwater support platform is studied, including five dimensions: physical world, digital world, twin data center, service application, and data connection. The digital twin of the subsea support platform is established by using the digital twin modeling technology. The POD method is used to calculate the deformation field matrix of the support structure of the subsea support platform under the 0–5 sea state, and the corresponding eigenvalues and eigenvectors are obtained. By intercepting the eigenvectors corresponding to the eigenvalues of the high energy proportion, the low-order equation is constructed, and the reduced-order model under each sea state can be quickly solved. The experimental results show that the model reduction technology can greatly shorten the model solving time, and the calculated results are highly consistent with the simulation results of the finite element full-order model, which can realize the rapid analysis of the deformation response of the subsea support platform structure, and provide a theoretical basis and technical support for the subsequent simulation, state evaluation, visual monitoring, and predictive maintenance. Full article

The quality of underwater bridge piers significantly impacts bridge safety and long-term usability. To address limitations in conventional inspection methods, this paper presents a sonar-based technique for the three-dimensional (3D) reconstruction and visualization of underwater bridge piers. Advanced MS1000 scanning sonar is employed to detect and image bridge piers. Automated image preprocessing, including filtering, denoising, binarization, filling, and morphological operations, introduces an enhanced wavelet denoising method to accurately extract the foundation contour coordinates of bridge piers from sonar images. Using these coordinates, along with undamaged pier dimensions and sonar distances, a model-driven approach for a 3D pier reconstruction algorithm is developed. This algorithm leverages multiple sonar data points to reconstruct damaged piers through multiplication. The Visualization Toolkit (VTK) and surface contour methodology are utilized for 3D visualization, enabling interactive manipulation for enhanced observation and analysis. Experimental results indicate a relative error of 13.56% for the hole volume and 10.65% for the spalling volume, demonstrating accurate replication of bridge pier defect volumes by the reconstructed models. Experimental validation confirms the method’s accuracy and effectiveness in reconstructing underwater bridge piers in three dimensions, providing robust support for safety assessments and contributing significantly to bridge stability and long-term safety assurance. Full article

Perovskite quantum dots (QDs), emerging with excellent bright-green photoluminescence (PL) and a large absorption coefficient, are of great potential for the fabrication of light sources in underwater optical wireless communication systems. However, the instability caused by low formation energy and abundant surface traps is still a major concern for perovskite-based light sources in underwater conditions. Herein, we propose ultra-stable zero dimensional–two dimensional (0D–2D) CsPbBr₃ QD/1,4-bis(4-methylstyryl)benzene (p-MSB) nanoplate (NP) heterostructures synthesized via a facile approach at room temperature in air. CsPbBr₃ QDs can naturally nucleate on the p-MSB NP toluene solution, and the radiative combination is drastically intensified owing to the electron transfer within the typical type-II heterostructures, leading to a sharply increased PLQY of the heterostructure thin films up to 200% compared with the pristine sample. The passivation of defects within CsPbBr₃ QDs can be effectively realized with the existence of p-MSB NPs, and thus the obviously improved PL is steadily witnessed in an ambient atmosphere and thermal environment. Meanwhile, the enhanced humidity stability and a peak EQE of 9.67% suggests a synergetic strategy for concurrently addressing the knotty problems on unsatisfied luminous efficiency and stability of perovskites for high-performance green-emitting optoelectronic devices in underwater applications. Full article

Background: Swimming and, specifically, front crawl, can be included among the “overhead” sports. Overhead sports are a risk factor for some problems of the musculoskeletal system, especially the shoulder. The aim of this study was to assess the incidence of shoulder and neck pain in a Masters Swimming Team and its correlation with the crawl stroke. Methods: This is an observational study through video-analysis of the stroke and a questionnaire. The participants selected for the present study were 61 athletes of a Masters team, whose prevailing training stroke was the front crawl. Their stroke was analyzed during training using a go-pro camera mounted on a sliding trolley on a track, evaluating their technical defects with their trainer. A questionnaire about frequency of shoulder and neck pain during the last five years was administered to all the participants at the study. Results: From the questionnaire, 45 and 55 out of 61 athletes had suffered from shoulder pain and cervical pain, respectively. Both types of pain were correlated with the weekly swimming volume. The swimmers with hyperflexion of the wrist and prolonged internal rotation in the pulling phase had shoulder problems. Those who suffered from current shoulder pain reduced the underwater time. The four swimmers with an excessive body roll during breathing and those who kept their heads extended, reported cervical pain. Conclusions: Shoulder and neck pain could be prevented with the correction of specific technical errors in crawl stroke. Full article

Due to the strong growth of offshore renewable energies, research and engineering in this field is constantly expanding. One of the centerpieces of these technologies is the high-voltage electrical cable, generally made of copper, to transport the energy produced from the offshore farm to the onshore station. The critical nature of these cables lies in the proven resistance that they must demonstrate during stays underwater for several years, even decades, in difficult environmental conditions, which begin at the handling, shipping and underground burial stage. The marine environment can lead to deformation of the copper wires well beyond the limit of proportionality and, consequently, to breakage. Copper, although being an exceptional electrical conductor, has very poor mechanical properties. The plasticity generated by the excessive deformation of copper wires affects all of the physical properties of copper. When plasticity develops, electrical transport is affected and the heat within copper increases, but care should be given to not exceed 90 °C, as this would result in the shutdown of the cable with dramatic economic consequences. The work carried out in this article, which is part of the National Project EMODI as well as the European Project FLOW-CAM, aims at studying the mechanical behavior of the phase in order to correlate the deformation levels reached to the phase geometry as well as operating mechanisms of damage which reflect the proliferation of microstructural defects within the conductor. To do this, we propose a numerical model using Abaqus. Correct description of the effects of several parameters (geometry of the phase) and plasticity development on the performance of the phase were simulated and discussed. Full article