Search Results (41)

Cross-plate joints between panels are commonly used in diaphragm wall construction to ensure structural integrity. However, research on the mechanical behaviour of these joints remains limited, and they are often disregarded in numerical modelling due to their complexity. This paper fabricated two types of specimens with cross-plate joints, which were subsequently employed in bending and shear tests, respectively. The load–displacement curves and the joint openings were experimentally measured. It was found that the load–displacement curves exhibited approximately four linear stages in the bending tests and two in the shear tests. Based on the test results, a multi-linear spring model was proposed to simplify the mechanical behaviour of the joints, and the stiffness of each linear stage was determined through back-analysis of the tested data. The calculated load–displacement curves ultimately agreed well with those obtained from the tests, with average errors of 3.6% in the bending test and 2.6% in the shear test. The proposed model was then applied to a devised case study, thereby demonstrating its capacity to capture joint opening phenomena and revealing the spatial variability of joint opening within the excavation depth. Compared with conventional 2D and 3D models, the proposed model yields displacement results that better reflect the actual deformation of the diaphragm wall. Furthermore, the precise modelling calculation for joints, which is time-consuming, is also avoided, and the calculation time of the proposed model is only 1.52 times that of the conventional 3D model. Full article

In the steel manufacturing process, defect classification is a critical step to ensure product performance and safety. However, due to the complexity of defect types and their multi-scale distribution characteristics, surface defect classification for steel plates remains a significant challenge. To address this issue, this paper proposes a deep learning model based on the ConvNeXt architecture, FAX-Net, which is designed to further improve the accuracy of steel surface defect classification. The FAX-Net architecture incorporates a Symmetric Dual-dimensional Attention Module (SDAM), which employs structurally symmetric and parallel modeling paths to effectively enhance the model’s responsiveness to critical defect regions. In addition, a Transformer-Fused Feature Pyramid Network (TF-FPN) is designed by integrating a lightweight Transformer to improve information interaction and integration across features of different scales, thereby enhancing the model’s discriminative capability in multi-scale scenarios. Experimental results demonstrate that the proposed FAX-Net model offers significant advantages in steel surface defect classification tasks. On the NEU-CLS dataset, FAX-Net achieves a classification accuracy of 97.78%, outperforming existing mainstream methods. These findings validate that FAX-Net possesses superior classification capabilities and is well-suited to handle a wide variety of defect types and scales effectively. Full article

Shock-to-detonation transition (SDT) is the detonation of explosive charge triggered by the shock pressure from a nearby detonated explosive or an impact at high speed. A good prediction of SDT is a key in the design of explosives’ use, storage, and transportation. Typically, SDT simulation must use designated commercial software; therefore, a high license cost is necessary. This paper presents a simulation of SDT by a cost-effective hydrodynamic code developed on an open-source code framework, OpenFOAM. The code adopted the multi-material Eulerian method, Ignition and Growth reaction rate model, and Riemann solver to solve the shock-induced detonation phenomenon. The code was verified by a Pop plot calculation and a sympathetic detonation simulation. In the Pop plot calculation, the distance-of-run to the detonation of Composition B depending on the initial shock pressure was simulated. The reactant and product phases of Composition B were modeled by the Jone–Wilkins–Lee (JWL) equation of state (EOS). The aluminum plate used to create the initial shock pressure was modeled by shock Mie–Gruneisen (MG) EOS. The predicted distance-of-run against the initial shock pressure was in good agreement with an empirical correlation and experimental data. In the sympathetic detonation simulation, the charge explosive and nearby explosive were Composition B and were modeled by JWL EOS as in the Pop plot calculation and the plexiglass gap was modeled by MG EOS. The simulated critical gap for the sympathetic detonation was well predicted as in the other published data. This implies that the code is valid for SDT simulation. In addition, it is a cost-effective simulation, since the code was developed on open-source code, so massive computation can then be run without license costs. Full article

Fracture toughness is an important index related to the service safety of marine risers, and weld is an essential component of the steel catenary risers. In this paper, microscopic structure characterization methods such as scanning electron microscopy (SEM) and electron back scatter diffraction (EBSD), as well as mechanical experiments like crack tip opening displacement (CTOD) and nanoindentation, were employed to conduct a detailed study on the influence of the microstructure characteristics of multi-wire submerged arc welded seams of steel catenary riser pipes on CTOD fracture toughness. The influence mechanisms of each microstructure characteristic on fracture toughness were clarified. The results show that the main structure in the weld of the steel catenary riser is acicular ferrite (AF), but there is also often side lath plate ferrite (FSP) and grain boundary ferrite (GBF). With the increase in the proportion of FSP and GBF in the weld microstructure, the CTOD fracture toughness of the weld decreases gradually. The weld AF is a braided cross arrangement structure, and most of the grain boundary orientation difference is higher than 45°. The effective grain size refinement of AF can effectively prevent crack propagation and significantly improve fracture toughness. GBF is distributed along proto-austenitic grain boundaries PAGB, and the large hardness difference between the GBF and the AF matrix weakens the grain boundary. Cracks can easy be initiated at the interface position of the two phases and can propagate along the GBF grain boundary, resulting in the deterioration of toughness. Although the hardness of FSP is between that of GBF and AF, it destroys the continuity of the overall weld microstructure and is also unfavorable to toughness. Full article

Previous research on composite dampers has rarely addressed the issue of large deformations of structures under limit state. However, the proposed damper in this paper takes this issue into account and could provide yielding reserve stiffness for structures, ensuring structural resilience. A composite damper with yielding reserve stiffness (YRSD), consisting of a friction unit and a metal yield unit, was proposed. Low cyclic loading tests with different energy-dissipating steel plate thicknesses and bolt preloads were carried out and experimental results were compared with that of numerical simulation. This paper focuses on the synergistic energy dissipation mechanism of the proposed damper and the effects of various factors on its hysteretic performance, including the bolt preload and thickness of X-shaped steel plates. The results show that the synergistic energy dissipation mechanism of the proposed damper is well, exhibiting the behavior of hardening post-yielding stiffness and multi-stage energy dissipation characteristics, which could provide yielding reserve stiffness for the structure. The experimental hysteresis curve of YRSD is full, indicating its strong energy dissipation capacity, and the skeleton curve of experiment is consistent with that of the theoretical model. The envelope area of the rectangular hysteresis curve of YRSD increases by 107.3% with the preload increased by 100%. When the thickness of the X-shaped steel plates is increased by 2 mm, the resistance of YRSD increases by 26.2% and the post-yield stiffness increases by 37.9%. The stiffness degradation trend of all specimens initially decreases and then increases. The energy dissipation capacity of the friction unit increases by 53.8% as the preload is doubled. The capacity of the metal yield unit increases by 31.7% as the thickness of the X-shaped steel plates is increased by 2 mm. When the energy dissipation capacities of the friction unit and the metal yield unit are close to equal, the optimal energy dissipation capacity of the proposed damper is achieved. The error of results between the numerical analysis and experimentation is less than 10%, providing a basis for the parametric analysis of similar composite damper with yielding reserve stiffness. Full article

This paper presents a comparative study on license plate detection and recognition algorithms in unconstrained environments, which include varying illuminations, nonstandard plate templates, and different English language fonts. A prime objective of this study is to assess how well these models handle such challenges. These problems are common in developing countries like Pakistan where diverse license plates, styles, and abrupt changes in illuminations make license plates detection and recognition a challenging task. To analyze the license plate detection problem Faster-RCNN and end-to-end (E2E) methods are implemented. For the license plate recognition task, deep neural network and the CA-CenterNet-based methods are compared. Detailed simulations were performed on authors’ own collected dataset of Pakistani license plates, which contains substantially different multi-styled license plates. Our study concludes that, for the task of license plate detection, Faster-RCNN yields a detection accuracy of 98.35%, while the E2E method delivers 98.48% accuracy. Both detection algorithms yielded a mean detection accuracy of 98.41%. For license plate recognition task, the DNN-based method yielded a recognition accuracy of 98.90%, while the CA-CenterNet-based method delivered a high accuracy of 98.96%. In addition, a detailed computational complexity comparison on various image resolutions revealed that E2E and the CA-CenterNet are more efficient than their counterparts during detection and recognition tasks, respectively. Full article

Ship grounding onto multiple rocks is one of the scenarios where a ship may suffer severe hull damage, thus leading to some serious consequences, such as casualties, oil spill pollution, and property damage. Ship bottom raking is the most common and severe damage type in grounding caused by sharp rocks moving against the bottom plate. This paper investigates the dynamic responses of ship grounding onto multiple sharp rocks, which has rarely been studied in the literature. Nine ship grounding in-tank model tests were conducted to provide experimental data for ship grounding onto a single rock or multiple rocks. A simplified scaled ship model with replaceable bottom plating was designed and used in the model test. Some artificial cone rock models with a 1 mm tip radius and a 15° semi-apex angle were assumed. The damage modes of the bottom plating and motions during ship grounding onto multiple rocks were obtained and recorded in the model tests, as well as the longitudinal grounding resistances. The effects of the initial relative height of each rock and the size of rock distribution on the structural damage mode and dynamic response of a ship model in multi-rock ship grounding were investigated. In addition, the results obtained from single-rock and multi-rock ship grounding model tests are compared. Full article

With the expanding application of lightweight wooden structures in modern construction, the load-bearing capacity of ordinary triangular single-span wooden trusses limits the applicability of lightweight wooden structures. As a result, triangular multi-span wooden trusses have emerged to replace single-span wooden trusses. In practice, multi-span wooden trusses are composed of multiple single-span lightweight wooden trusses, with connections between members using metal plates, a field that has been relatively well researched. However, connections between spans are primarily made with nails in actual engineering, and there has been little research on the use of wooden pins to connect multi-span wooden trusses. To study the mechanical performance of multi-span wooden trusses connected by wooden pins, this paper innovatively combines existing equipment with a self-designed pulley assembly device to conduct a continuous static full-scale loading test on double-span wooden trusses connected by wooden pins of three different diameters. We comprehensively evaluate which type of wooden pin is more suitable for triangular multi-span wooden trusses. The results indicate that the 16 mm diameter wooden pin provides the best energy dissipation performance for connected beam trusses. The 20 mm diameter wooden pin offers the best performance stability. The 20 mm diameter wooden pin also demonstrates a good load-bearing capacity and resistance to deformation. Overall, the 20 mm diameter wooden pin exhibits the best connection performance in triangular beam trusses. Full article

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model’s Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm³/cm³, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm³/cm³, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture. Full article

This paper presents the application of a hybrid approach for damage localization in concrete plates of arbitrary geometric shapes and a constant thickness. The hybrid algorithm utilizes fast discrete wavelet transformation, energy approach and time of flight criteria for the purpose of the localization of single- and multi-damage problems inside or on the periphery of concrete plates. A brief theoretical background of the hybrid method as well as numerical procedures for modeling the piezoelectric smart aggregate and ultrasonic wave propagation are presented. Experimental and numerical verification of the damage localization were performed on square samples/models with one or two damages and with 16 positions of piezoelectric smart actuator/sensor aggregates. After the verification of the hybrid method, a numerical simulation was performed on models with one or two damages for plates of arbitrary geometric shapes. Based on the obtained results, it was concluded that the proposed method can be applied to damage localization in concrete plates of arbitrary geometric shapes. The presented method and numerical procedure can be further used in research through varying the geometry, number and position of damages as well as the number and position of piezoelectric smart aggregates. Full article

Colony-Forming Unit (CFU) counting is a complex problem without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by researchers. However, U-Net remains the most frequently cited and used deep learning method in these domains. The latter approach provides a segmentation output map and requires an additional counting procedure to calculate unique segmented regions and detect microbial colonies. However, due to pixel-based targets, it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, this paper proposes a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. Firstly, a unique innovation lies in the multi-loss U-Net reformulation. An additional loss term is introduced in the bottleneck U-Net layer, focusing on the delivery of an auxiliary signal that indicates where to look for distinct CFUs. Secondly, the novel localization algorithm automatically incorporates an agar plate and its bezel into the CFU counting techniques. Finally, the proposition is further enhanced by the integration of a fully automated solution, which comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application directly receives images from the camera, processes them, and sends the segmentation results to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the deep learning model. Through extensive experimentation, the authors of this paper have found that all probed multi-loss U-Net architectures incorporated into the proposed hybrid approach consistently outperformed their single-loss counterparts, as well as other comparable models such as self-normalized density maps and YOLOv6, by at least 1% to 3% in mean absolute and symmetric mean absolute percentage errors. Further significant improvements were also reported through the means of the novel localization algorithm. This reaffirms the effectiveness of the proposed hybrid solution in addressing contemporary challenges of precise in vitro CFU counting. Full article

Although 2D in vitro cancer cell cultures have been used for decades as a first line-of-research tool to investigate antitumoral drugs and treatments, their use presents many drawbacks, including the poor resemblance of such cultures to the characteristics of in vivo tumors. To mitigate these drawbacks, 3D culture models have emerged as a more representative alternative. Cancer cells cultured as 3D structures have the advantage of resembling solid tumors in their architecture and in their resistance to chemotherapeutic drugs, in part because of restrained drug penetration. Additionally, these 3D structures create a more physiological environment for the study of immune cell invasion and migration, comparable to solid tumors. In this paper, we describe a fast and cost-effective step-by-step protocol for the generation of 3D spheres using ultra-low-attachment (ULA) multiwell plates, which can be incorporated into the normal workflow of any laboratory. Using this protocol, spheroids of different human cancer cell lines can be obtained and can then be characterized on the basis of their morphology, viability, and expression of specific markers. Full article

Aiming its analysis at the poor hardness and wear-resistance of E690 high-strength steel, and the high hardness and good wear-resistance of AlCrN-coated, combined with the laser impact micro-modeling which can store oil lubrication, this paper carries out research on the synergistic wear reduction mechanism of laser impact micro-modeling AlCrN coated on the surface of E690 high-strength steel. Multi-arc ion plating technology is used to prepare the AlCrN coating on the laser-impact micro-modeling specimen; the micro-modeling AlCrN-coated specimen is subjected to a reciprocating friction test, and the hardness and residual stress of the coated surface are measured by equipment such as a residual stress meter and a microhardness tester. The microstructure and physical elements of the surface wear before and after the preparation of the coating are analyzed by scanning electron microscope (SEM), confocal three-dimensional morphometer and XRD diffractometer, respectively. The results show that the prepared AlCrN-coated materials were well-bonded to the substrate. Compared with the micro-molding-only specimens, the average friction coefficient and wear amount of the micro-molded AlCrN-coated specimens with different micro-molding densities and depths decreased compared with the micro-molded specimens; among them, the average friction coefficient of the specimens with a micro-molding density of 19.6% and a depth of 7.82 µm was 0.0936, which was the lowest. Additionally, the AlCrN coating enhances the stability of the friction process of the specimen and reduces the amount of wear of the specimen. Under the premise of ensuring the anti-wear and stability properties of the material, the best integrated friction performance was achieved at a micro-molding density of 19.6% and a depth of 24.72 µm. A synergistic wear reduction and lubrication model of micro-molding and AlCrN-coating was established. Full article

The cold metal transfer (CMT) process is widely used in thin plate welding because of its characteristics of low heat input and stable arc. In actual production, a larger weld gap, misalignment, or other problems due to assembly error lead to serious welding defects, such as burn-through and a lack of fusion. The arc sound contains a wealth of information related to the quality of the weld. This work analyzes the mechanism of CMT arc sound generation, as well as the correlation between the time–frequency spectrum of the arc sound signal and welding quality. This paper studies the extraction of the multi-channel time–frequency spectrum of an arc sound and inputs it to a custom convolutional neural network for the CMT welding defect identification of thin aluminum alloy plates. The experimental result shows that the average accuracy of the proposed model is 91.49% in the defect identification of a CMT arc-welded aluminum alloy sheet, which is higher than that of the single-channel time–frequency convolutional neural network and other traditional classification models. Full article

Various diffractive, refractive and holographic optical elements, such as diffraction gratings; microlens raster; phase plates; multi-order diffractive optical elements; adaptive mirrors; diffractive and refractive axicons; holographic multiplexes and many others are used to analyze wavefront aberrations. We shortly discuss the features (advantages and disadvantages) of various wavefront aberration sensors in the Introduction. The main part of the paper is devoted to the analysis of the weight coefficients of Zernike polynomials obtained during medical examinations of the cornea in the human eye. Using data obtained by aberrometers, the average values of the Zernike polynomial coefficients for the anterior and posterior surfaces of the healthy eye cornea and a myopic one were calculated. The original wavefront for the anterior and posterior surfaces of the cornea was restored separately, as well as the total wave aberration. For an objective assessment of the quality of vision, the corresponding point spread functions (PSFs) were calculated. We propose to compensate for the aberrations of the myopic eye, taking into account the physical features of the corneal surface. The results of numerical simulation showed that in order to improve the quality of the patient’s vision, it is necessary to take into account high-order aberrations of the anterior surface of the cornea in the form of a coma of the third order and aberrations of the fourth order. Full article