Search Results (2,442)

Perforation, a common well completion method in oil and gas exploitation, introduces structural defects in casings that alter their mechanical properties. Based on engineering specifications, this study calculates critical loads (i.e., collapse pressure and yield pressure) and the triaxial equivalent stress for casings. Four load cases were selected for analysis: uniform external pressure, uniform internal pressure, external pressure with axial compression, and internal pressure with axial tension. The equivalent stresses around circular, elliptical, pentagonal, and hexagonal perforation defects were computed. A self-defined perforation influence coefficient was used to evaluate changes in mechanical performance. Results show that circular defects have the least effect on the mechanical properties of the casing. Maximum equivalent stress occurs along the hole centerline parallel to the casing axis and increases with greater disparity between ellipse axes or smaller polygon angles. High shot density (>24 holes/m) and large phase angle (60°) generally enhance safety, but an optimal combination exists. Under tensile loads near cracked defects, crack propagation may lead to fracture. For elliptical defects with cracks, the Mode I stress intensity factor grows faster with greater axis disparity, accelerating crack tip stress and deformation, and raising fracture risk. Cracks perpendicular to tensile stress influence the stress intensity factor more significantly than parallel ones. Full article

The purpose of this parametric study was to develop a numerical simulation model calibrated with experimental data to predict the flexural behavior of prestressed concrete (PSC) girders subjected to long-term prestress losses. The model is capable of accurately simulating the flexural behavior of PSC girders using commercial finite-element (FE) software in the ABAQUS/Explicit program. The accuracy of the model was validated by comparing its results with flexural response test data from three post-tensioned girders, with the tendons ultimately having tensile strength capacities of 1860 MPa, 2160 MPa, and 2400 MPa. The comparison demonstrated generally excellent agreement between numerical and experimental results in terms of the load–deflection response and crack propagation behavior, from the onset of first cracking through the maximum load and into the ductile response range. Subsequently, a parametric study was conducted to evaluate the effects of tendon ultimate strength, amount of long-term prestress loss, grouting defects, degradation-induced reductions in concrete strength, and reductions in tendon cross-sectional area on girder flexural behavior. Through this parametric investigation, the study identified key factors with respect to long-term prestress loss that may influence the flexural behavior of aging PSC structures. Full article

Concrete structures are vulnerable to sulfate attacks during their service life, as sulfate ions react with cement hydration products to form expansive phases, generating internal stresses that cause mechanical degradation. In this study, a cementitious capillary crystalline waterproofing material (CCCW) was incorporated into concrete to mitigate sulfate ingress and enhance sulfate resistance. The evolution of compressive strength, ultrasonic pulse velocity, dynamic elastic modulus, and the microstructure of concrete was investigated in sulfate-exposed concretes with varying CCCW dosages and strength grades; the sulfate ion concentration profiles were also analyzed. The results indicate that the enhancement effect of CCCW on sulfate resistance declines progressively with increasing concrete strength. The formation of calcium silicate hydrate and calcium carbonate fills the pores of concrete, hindering the intrusion of sulfate solution. Moreover, the self-healing effect of concrete further inhibits the diffusion of sulfate ions through cracks, improving the sulfate resistance of concrete. These findings provide critical insights and practical guidance for improving concrete resistance to sulfate-induced deterioration. Full article

To address the greenhouse effect and environmental pollution stemming from fossil fuels, the development of new energy sources is widely regarded as a critical pathway toward achieving carbon neutrality. Microalgae, as a feedstock for third-generation biofuels, have emerged as a research hotspot for producing biojet fuel due to their high photosynthetic efficiency, non-competition with food crops, and potential for carbon reduction. This paper provides a systematic review of technological advancements in the catalytic hydrogenation of microalgal oil for biojet fuel production. It specifically focuses on the reaction mechanisms and catalyst design involved in the hydrogenation–deoxygenation and cracking/isomerization processes within the Oil-to-Jet (OTJ) pathway. Furthermore, the paper compares the performance differences among various catalyst support materials and between precious and non-precious metal catalysts. Finally, it outlines the current landscape of policy support and progress in industrialization projects globally. Full article

Automated Road defect detection using Unmanned Aerial Vehicles (UAVs) has emerged as an efficient and safe solution for large-scale infrastructure inspection. However, object detection in aerial imagery poses unique challenges, including the prevalence of extremely small targets, complex backgrounds, and significant scale variations. Mainstream deep learning-based detection models often struggle with these issues, exhibiting limitations in detecting small cracks, high computational demands, and insufficient generalization ability for UAV perspectives. To address these challenges, this paper proposes a novel comprehensive network, RoadNet, specifically designed for high-precision road defect detection in UAV-captured imagery. RoadNet innovatively integrates Transformer modules with a convolutional neural network backbone and detection head. This design not only significantly enhances the global feature modeling capability crucial for understanding complex aerial contexts but also maintains the computational efficiency necessary for potential real-time applications. The model was trained and evaluated on a self-collected UAV road defect dataset (UAV-RDD). In comparative experiments, RoadNet achieved an outstanding mAP@0.5 score of 0.9128 while maintaining a fast-processing speed of 210.01 ms per image, outperforming other state-of-the-art models. The experimental results demonstrate that RoadNet possesses superior detection performance for road defects in complex aerial scenarios captured by drones. Full article

Many scholars believe that the Yi Jing 易經 (the Book of Changes) and traditional Chinese medicine share common mathematical principles, which are both predicated on the ontological of qi 氣 and the cosmological of correlative between nature and human. Traditional Chinese medicine emphasizes the systemic organization of organs, meridians, qi, and blood as central components by incorporating the mathematical principles, including the theory of “Chaos-Crack”, the infinite classification methods of yinyang 陰陽, the generative and restrictive interactions of wuxing 五行, and the metaphysical significance of special numbers such as one, two, three, etc. Traditional Chinese medicine also formulates many theories and methodologies by integrating these mathematical principles with the schemata of luoshu 洛書 and jiugong 九宮, as well as the special combination numbers such as tianliu diwu 天六地五. This research tries to explain the mathematical principles and applications from the body dimension in traditional Chinese medicine. Full article

Visual inspection remains a cornerstone of quality control in welded structures, yet manual evaluations are inherently constrained by subjectivity, inconsistency, and limited scalability. This study presents WeldVGG, a deep learning-based visual inspection model designed to automate weld defect classification using radiographic imagery. The proposed model is trained on the RIAWELC dataset, a publicly available collection of X-ray weld images acquired in real manufacturing environments and annotated across four defect conditions: cracking, porosity, lack of penetration, and no defect. RIAWELC offers high-resolution imagery and standardized class labels, making it a valuable benchmark for defect classification under realistic conditions. To improve trust and explainability, Grad-CAM++ is employed to generate class-discriminative saliency maps, enabling visual validation of predictions. The model is rigorously evaluated through stratified cross-validation and benchmarked against traditional machine learning baselines, including SVC, Random Forest, and a state-of-the-art architecture, MobileNetV3. The proposed model achieves high classification accuracy and interpretability, offering a practical and scalable solution for intelligent weld inspection. Furthermore, to prove the model’s ability to generalize, a test on the GDXray was performed, yielding positive results. Additionally, a Wilcoxon signed-rank test was conducted separately to assess statistical significance between model performances. Full article

This study presents a thermodynamic equilibrium analysis of hydrogen-rich syngas production via biomass–methane co-pyrolysis, employing the Gibbs free energy minimization method. A critical temperature threshold at 700 °C is identified, below which methanation and carbon deposition are thermodynamically favored, and above which cracking and reforming reactions dominate, enabling high-purity syngas generation. Methane addition shifts the reaction pathway towards increased reduction, significantly enhancing carbon and H₂ yields while limiting CO and CO₂ emissions. At 1200 °C and a 1:1 methane-to-biomass ratio, cellulose produces 50.84 mol C/kg, 119.69 mol H₂/kg, and 30.65 mol CO/kg; lignin yields 78.16 mol C/kg, 117.69 mol H₂/kg, and 19.14 mol CO/kg. The H₂/CO ratio rises to 3.90 for cellulose and 6.15 for lignin, with energy contents reaching 43.16 MJ/kg and 52.91 MJ/kg, respectively. Notably, biomass enhances methane conversion from 25% to over 53% while sustaining a 67% H₂ selectivity. These findings demonstrate that syngas composition and energy content can be precisely controlled via methane co-feeding ratio and temperature, offering a promising approach for sustainable, tunable syngas production. Full article

Stable adhesion on non-magnetic, steep, and irregular underwater surfaces (e.g., concrete dams with cracks or biofilms) remains a challenge for inspection robots. This study develops a novel adsorption mechanism based on the synergistic operation of a Venturi-ejector and a composite suction cup. The mechanism utilizes the Venturi effect to generate stable negative pressure via hydrodynamic entrainment and innovatively adopts a composite suction cup—comprising a rigid base and a dual-layer EPDM sponge (closed-cell + open-cell)—to achieve adaptive sealing, thereby reliably applying the efficient negative-pressure generation capability to rough underwater surfaces. Theoretical modeling established the quantitative relationship between adsorption force (F) and key parameters (nozzle/throat diameters, suction cup radius). CFD simulations revealed optimal adsorption at a nozzle diameter of 4.4 mm and throat diameter of 5.8 mm, achieving a peak simulated F of 520 N. Experiments demonstrated a maximum F of 417.9 N at 88.9 W power. The composite seal significantly reduced leakage on high-roughness surfaces (Ra ≥ 6 mm) compared to single-layer designs. Integrated into an inspection robot, the system provided stable adhesion (>600 N per single adsorption device) on vertical walls and reliable operation under real-world conditions at Balnetan Dam, enabling mechanical-arm-assisted maintenance. Full article

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article

Polymer-based product usage in modern society is increasing day by day. Following usage, these inert products and hydrophobic materials contribute to environmental pollution, often accumulating as litter in ecosystems and contaminating water bodies. The rapid socio-economic development in the Kingdom of Saudi Arabia (KSA) has resulted in a significant increase in waste generation. This study was conducted on the utilization of recycled plastic waste (RPW) polymer along with commercial polymer (CP) for the modification of the local binder. The hot environmental conditions and increased traffic loading are the major reasons for the permanent deformation and thermal cracks on the pavements, which require improved and modified road performance materials. The Ministry of Transport and Logistical Support (MOTLS) in Saudi Arabia, along with other related agencies, spends a substantial amount of money each year on importing modifiers, including chemicals, hydrocarbons, and polymers, for modification purposes. This research was conducted to investigate and utilize available local recycled plastic materials. Comprehensive laboratory experiments were designed and carried out to enhance recycled plastic waste, including low-density polyethylene (rLDPE), high-density polyethylene (rHDPE), and polypropylene (rPP), combined with varying percentages of commercially available polymers such as Styrene-Butadiene-Styrene (SBS) and Polybilt (PB). The results indicated that incorporating recycled plastic waste expanded the binder’s susceptible temperature range from 64 °C to 70 °C, 76 °C, and 82 °C. The resistance to rutting was shown to have significantly improved by the dynamic shear rheometer (DSR) examination. Achieving the objectives of this research, combined with the intangible environmental benefits of utilizing plastic waste, provides a sustainable pavement development option that is also environmentally beneficial. Full article

Crack monitoring in concrete structures is essential to maintaining structural integrity. Therefore, this paper proposes a mobile ground robot equipped with a 2-DoF manipulator and stereo vision sensors for autonomous crack monitoring and mapping. To facilitate crack detection over large areas, a 2-DoF motorized manipulator providing linear and rotational motions, with a stereo vision sensor mounted on the end effector, was deployed. In combination with a manual rotation plate, this configuration enhances accessibility and expands the field of view for crack monitoring. Another stereo vision sensor, mounted at the front of the robot, was used to acquire point cloud data of the surrounding environment, enabling tasks such as SLAM (simultaneous localization and mapping), path planning and following, and obstacle avoidance. Cracks are detected and segmented using the deep learning algorithms YOLO (You Only Look Once) v6-s and SFNet (Semantic Flow Network), respectively. To enhance the performance of crack segmentation, synthetic image generation and preprocessing techniques, including cropping and scaling, were applied. The dimensions of cracks are calculated using point clouds filtered with the median absolute deviation method. To validate the performance of the proposed crack-monitoring and mapping method with the robot system, indoor experimental tests were performed. The experimental results confirmed that, in cases of divided imaging, the crack propagation direction was predicted, enabling robotic manipulation and division-point calculation. Subsequently, total crack length and width were calculated by combining reconstructed 3D point clouds from multiple frames, with a maximum relative error of 1%. Full article

The Ground Penetrating Radar (GPR) method can image dielectric discontinuities in subsurface structures, which cause the reflection of electromagnetic (EM) waves. These discontinuities are imaged as reflectors in GPR sections, often distorted by diffracted energy. To focus the diffracted energy within the GPR sections, migration is commonly used. The migration velocity of GPR data is a low-wavenumber attribute crucial for effective migration. Obtaining a migration velocity model, typically close to a Root Mean Square (RMS) model, from zero-offset (ZO) data requires analysis of the available diffractions, whose density and (x, t) coverage are random. Thus, the accuracy and efficiency of such a velocity model, whether for migration or interval velocity model estimation, are not guaranteed. An alternative is the multipath summation method, which involves the weighted stacking of constant velocity migrated sections. Each stacked section contributes to the final stack, weighted by a scalar value dependent on the constant velocity value used and its relation to its estimated mean velocity of the section. This method effectively focuses the GPR diffractions in the presence of low heterogeneity. However, when the EM velocity varies dramatically, 2D weights are needed. In this study, with the aid of an Artificial Intelligence (AI) algorithm that detects diffractions and uses their kinematic information, we generate a diffraction velocity model. This model is then used to assign 2D weights for the weighted multipath summation, aiming to focus the scattered energy within the GPR section. We describe this methodology and demonstrate its application in enhancing the lateral continuity of reflections. We compare it with the 1D multipath summation using simulated data and present its application on marble assessment GPR data for imaging cracks and discontinuities in the subsurface structure. Full article

Wet–dry cycles are a key factor aggravating the durability degradation of foam concrete. To address this issue, this study prepared bentonite slurry–steel slag foam concrete (with steel slag and cement as main raw materials, and bentonite slurry as admixture) using the physical foaming method. Based on 7-day unconfined compressive strength tests with different mix proportions, the optimal mix proportion was determined as follows: mass ratio of bentonite to water 1:15, steel slag content 10%, and mass fraction of bentonite slurry 5%. Based on this optimal mix proportion, dry–wet cycle tests were carried out in both water and salt solution environments to systematically analyze the improvement effect of steel slag and bentonite slurry on the durability of foam concrete. The results show the following: steel slag can act as fine aggregate to play a skeleton role; after fully mixing with cement paste, it wraps the outer wall of foam, which not only reduces foam breakage but also inhibits the formation of large pores inside the specimen; bentonite slurry can densify the interface transition zone, improve the toughness of foam concrete, and inhibit the initiation and propagation of matrix cracks during the dry–wet cycle process; the composite addition of the two can significantly enhance the water erosion resistance and salt solution erosion resistance of foam concrete. The dry–wet cycle in the salt solution environment causes more severe erosion damage to foam concrete. The main reason is that, after chloride ions invade the cement matrix, they erode hydration products and generate expansive substances, thereby aggravating the matrix damage. Scanning Electron Microscopy (SEM) analysis shows that, whether in water environment or salt solution environment, the fractal dimension of foam concrete decreased slightly with an increasing number of wet–dry cycle times. Based on fractal theory, this study established a compressive strength–porosity prediction model and a dense concrete compressive strength–dry–wet cycle times prediction model, and both models were validated against experimental data from other researchers. The research results can provide technical support for the development of durable foam concrete in harsh environments and the high-value utilization of steel slag solid waste, and are applicable to civil engineering lightweight porous material application scenarios requiring resistance to dry–wet cycle erosion, such as wall bodies and subgrade filling. Full article

Manual pixel-level annotation remains a major bottleneck in deploying deep learning models for dense prediction and semantic segmentation tasks across domains. This challenge is especially pronounced in applications involving fine-scale structures, such as cracks in infrastructure or lesions in medical imaging, where annotations are time-consuming, expensive, and subject to inter-observer variability. To address these challenges, this work proposes a weakly supervised and annotation-efficient segmentation framework that integrates sparse bounding-box annotations with a limited subset of strong (pixel-level) labels to train robust segmentation models. The fundamental element of the framework is a lightweight Bounding Box Encoder that converts weak annotations into multi-scale attention maps. These maps guide a ConvNeXt-Base encoder, and a lightweight U-Net–style convolutional neural network (CNN) decoder—using nearest-neighbor upsampling and skip connections—reconstructs the final segmentation mask. This design enables the model to focus on semantically relevant regions without relying on full supervision, drastically reducing annotation cost while maintaining high accuracy. We validate our framework on two distinct domains, road crack detection and skin cancer segmentation, demonstrating that it achieves performance comparable to fully supervised segmentation models using only 10–20% of strong annotations. Given the ability of the proposed framework to generalize across varied visual contexts, it has strong potential as a general annotation-efficient segmentation tool for domains where strong labeling is costly or infeasible. Full article