Search Results (243)

The marine and continental transitional facies coal of the Longtan Formation in western Guizhou Province is a key area for coalbed methane (CBM) exploration and development in Southwest China and has numerous resources; however, the exploration and development effects have been average. The development status of the Longtan Formation is such that it presents prominent compatibility issues, challenging production layer combinations, and limited production capacity. On the basis of the “three-step method” of the multicoal seam coproduction combination method and considering the influence of effective gas content and coal seam modification on productivity, a new CBM production seam combination optimization method is developed in which acoustic logging is used to elucidate the coal structure of a coal seam. The combination optimization of production layers in 9 typical wells in the study area was carried out, and the influencing factors and geological insights were discussed. The following conclusions were drawn: Under the multiple influences of the in situ stress mechanism, temperature, and formation pressure, the effective gas content of the coal seam first increases and then decreases, with the highest value occurring at approximately 600 m, and the gas saturation continues to increase. The in situ permeability and transformation permeability first decreased, then increased, and then decreased, and the turning depths were 400 m and 600 m. The productivity potential of the combined mining combination tended to increase with increasing depth. At depths up to 1000 m, a 100 m interval is the optimal combined span, 550–750 m is the enrichment high-permeability window, and it is also the best depth for coal seam joint mining. At a depth of 1000 m, the permeability and effective gas content of the coal seam decreased, whereas the gas saturation and desorption pressure increased, which increased the development potential and research value of the CBM reservoir. Full article

To enhance the seismic performance of existing reinforced concrete (RC) buildings during retrofitting, the study introduces a new type of joint connected by a T-shaped steel plate. Compared with previous similar strengthening methods, this novel structure incorporating a post-installed beam not only effectively improves the mechanical properties of RC columns, but the connectors also further enhance the integrity of the post-installed beam. Low-cycle reversed loading tests on one cast-in-place specimen (RC) and three T-shaped steel plate connection specimens (TRC1–TRC3) were conducted to evaluate failure modes, hysteresis and skeleton curves, and energy dissipation. Results show that the novel joint failure concentrates at beam-end–column steel jacket weld seams and column-side steel plate cracking, while the core-zone concrete remains intact. Compared with RC, the novel joints TRC1–TRC3 exhibit bearing capacity variations of −1.03%~+15.80% and significantly enhanced energy dissipation. The thickness of the beam’s wrapped steel improves the carrying capacity and energy dissipation, whereas the T-shaped connector thickness has limited influence on bearing capacity. ABAQUS parametric analysis indicates that bolt quantity, concrete strength, and connector thickness have limited influence and serve as secondary design factors. These findings provide a theoretical basis for retrofitting existing buildings. Full article

To address the problems of the Electromagnetic Wave Propagation Algorithm (EMWPA)—insufficient initial-population coverage, an imbalance between exploration and exploitation, and a tendency to fall into local optima—in high-dimensional complex optimization problems, this paper proposes a boosted electromagnetic wave propagation optimization algorithm, BEMWPA. First, a cubic chaotic map is introduced in the population-initialization stage to enhance the uniformity of the initial-solution distribution and the search-space coverage. Second, nonlinear phase modulation is applied to the electric- and magnetic-field driving terms, and a differentiated probabilistic switching mechanism is constructed to improve the dynamic coordination between global exploration and local exploitation. Furthermore, a Beta-distribution opposition-based learning strategy is introduced to enhance the algorithm’s ability to escape local optima by generating high-quality opposite candidate solutions. To verify the effectiveness of the proposed algorithm, systematic comparative experiments are conducted on the CEC2017 benchmark function set, and BEMWPA is combined with rapidly-exploring random tree (RRT) and applied to path planning of a welding manipulator in complex multi-workpiece scenarios. For a three-dimensional welding scenario containing 12 workpieces, 12 closed weld seams, and multiple obstacle constraints, BEMWPA-RRT reduces the initial inter-seam transfer path length of RRT from 586.00 mm to 479.11 mm, representing a relative reduction of 18.24%, and the complete end-effector path length is reduced from 2974.00 mm to 2867.11 mm, representing a relative reduction of 3.59%. Meanwhile, the optimized transfer path length is only 1.59 mm longer than the obstacle-free ideal transfer length of 477.52 mm, indicating that the proposed method can approach the geometric lower bound of this scenario while satisfying the obstacle-avoidance constraints. Kinematic verification on a seven-degrees-of-freedom welding manipulator further shows that the optimized Cartesian-space path can be converted into a continuously executable joint-space trajectory, providing an effective method for offline welding path planning of complex multi-workpiece tasks. Full article

Post-process weld quality assurance detects a defect only after the joint has been completed and therefore cannot intervene while an unstable weld is still being produced. The question addressed here is how much of a seam must be observed before its final quality state can be recognized. This study investigates whether the final quality state of a gas metal arc (GMA) weld can be recognized from the initial segment of the seam using only a laser Doppler vibrometer as the diagnostic sensor. Thirty-two bead-on-plate GMA welds were produced under stable and deliberately destabilized conditions. The welding current channel was used only for offline arc segmentation and independent reference, whereas classification used vibration-derived time, frequency, envelope, and wavelet features extracted in 0.10 s windows. Causal prefix features were evaluated with leave-one-weld-out validation and weld-level bootstrap confidence intervals, and a streaming variant updated the probability of a defective weld window by window. The final process-stability state was distinguishable after approximately 5 mm of seam (AUC = 0.94), with high discrimination through the early-to-mid seam. As an online alarm, the aggregate prefix rule produced no false alarms among six acceptable welds, detected 95% of the defective welds, and achieved a median recognition lead of ~3.6 s. However, these specificity and lead-time estimates are coarse because only six acceptable welds were available, and with a single exception, the defective welds were unstable throughout; thus, the results mainly reflect early recognition of an established state. Decision latency was nearly constant in time (0.15–0.25 s), so the spatial decision position and remaining lead time depended mainly on welding speed. The results support non-contact early recognition of unstable GMA welding, while larger balanced datasets with mid-weld transitions are needed to validate true defect-onset anticipation. Full article

To address the difficulty in stably detecting weld seam keypoints under complex industrial interferences, such as intense arc light, spatter, reflection, and partial occlusion, this paper proposes a lightweight weld seam keypoint detection model named StripePoint-YOLO. The proposed method formulates five typical types of weld seams as a unified detection-based keypoint localization task. Built upon YOLO11n, the model introduces a P2 detection head to enhance shallow high-resolution feature representation for keypoints and adopts SPDConv to reduce the loss of local details caused by early-stage downsampling. Meanwhile, the P5 detection output layer is removed, while its deep semantic features are retained for top-down feature fusion. This design reduces the negative influence of redundant coarse-scale predictions on the center localization of tiny keypoints. For optimization and training, WIoU v3 and NWDLoss are adopted as a joint regression loss to improve the stability of small-scale keypoint bounding box regression. In addition, an online physics-driven data augmentation strategy, OPDDA, is designed to simulate welding disturbances such as arc light, spatter, and dynamic occlusion. Experimental results show that StripePoint-YOLO achieves an mAP@50-95 of 80.46%, a Mean Center Error (MCE) of only 2.33 px, a parameter count of 1.86 M, and a computational cost of 19.42 GFLOPs, while reaching an inference speed of 159.80 FPS under the reported hardware configuration. Further MCE visualization and localization error analysis demonstrate that the proposed method maintains stable keypoint center localization across multiple weld seam types and complex noisy scenarios, verifying the effectiveness of StripePoint-YOLO for accurate and efficient weld seam keypoint detection in industrial welding images. Full article

This study introduces a novel prestressed precast hollow-core slab system with integrated polystyrene foam inserts and a spliced design utilizing cast-in-place wet joints, aiming to resolve the inherent trade-off between self-weight reduction and load-bearing capacity in precast floor systems. An experimental investigation was conducted on three individual slabs with varied foam geometries and four jointed slabs with different foam configurations and joint concrete types. All specimens were tested under four-point bending. The experimental program meticulously assessed failure modes, load–displacement characteristics, and load–strain relationships of the proposed slab systems. Results reveal that the hollow-core slabs exhibited failure mechanisms similar to conventional cast-in-place slabs, with cracking initiating in the pure bending region and then propagating along the slab edges. Specifically, specimens with square and circular foam inserts achieved weight reduction rates of 24.57% and 19.78%, respectively. Concurrently, their ultimate loads increased by 32.39% and 46.46% compared to the cast-in-place control. The prestressing tendons remained elastic at ultimate load, confirming that failure was governed by concrete crushing in the compression zone rather than tendon rupture, which represents a ductile failure mode providing sufficient warning prior to collapse. For the jointed specimens, while cracks fully penetrated the foam inserts in the pure bending zone, no cracking occurred at the wet joint interfaces, signifying robust composite action. The load capacity of these jointed specimens surpassed that of the equivalent cast-in-place slab by 19.1% to 50.7%. Based on an evaluation of material cost, structural efficiency, and flexural performance among the tested configurations, the combination of square foam inserts and conventional C40 concrete in the wet joint is recommended. This research provides a critical experimental foundation for the development of lightweight, high-performance precast floor systems in prefabricated concrete construction. Full article

Large-wave corrugated steel utility tunnels have been widely used in underground engineering in recent years due to their excellent deformation adaptability and short construction period. However, research on the longitudinal joint bearing capacity is still insufficient and lacks an accurate calculation method for design guidance. In this study, axial compression tests were carried out on four groups of three-wave corrugated steel-plate high-strength bolted connection specimens with different plate thicknesses and bolt strength grades. Combined with finite element models extended to staggered seams, the effects of plate thickness and waveform on failure modes, bearing capacity, and force transmission characteristics were analysed. The test results indicated that the ductility of shear failure connections was obviously lower than that of bearing failure connections, and the brittleness of the shear failure mode became more pronounced as the difference between bolt shear strength and steel plate bearing strength increased. Numerical analysis revealed that although the bearing capacity of the seam improved with increasing waveform, the bearing capacity per unit width decreased. The ultimate displacement was related to the difference in strengths of the bolt and the corrugated steel plate, and the ultimate displacement in shear failure was much smaller than that in seams of bearing failure. The steel plate yielded progressively from both sides inwards along the corrugation direction. The bolt shear force distribution perpendicular to the loading direction exhibited considerable nonuniformity, with bolts at the staggered positions of the longitudinal seams being the first to fail. Based on comparisons of Chinese and American codes, and incorporating waveform and nonuniform shear effects, this study proposed bearing capacity equations for such connections. These equations accurately predict failure modes, with calculated results at 75–85% of theoretical values—a significant accuracy improvement over existing codes. Full article

Mobile laser scanning (MLS) has become an effective technique for deformation monitoring in subway shield tunnels. Among various deformation characteristics, segment dislocation is an important indicator of tunnel structural health because it reflects the relative deformation between adjacent segments and may affect the mechanical behavior and waterproof performance of segmental joints. However, existing MLS-based methods for dislocation detection still suffer from facility interference, inaccurate seam localization, and limited automation in quantitative analysis. To address these challenges, this study proposes an automated method for shield tunnel segment dislocation detection based on MLS point cloud processing. The proposed framework consists of three main steps. First, a point cloud filtering strategy integrating offset features and semantic segmentation is developed to remove facility-related noise while preserving tunnel wall information. Second, a tunnel segment segmentation method combining bolt hole extraction and moving template matching is introduced to achieve accurate localization of both horizontal and longitudinal seams, where bolt holes are identified using normal vector and distance constraints. Finally, automated segment dislocation analysis is performed based on the filtering and segmentation results. Experimental results demonstrate that the proposed filtering method improves accuracy by 8.7% and 5.6% compared with conventional ellipse fitting and cylinder fitting methods, respectively. Using manually interpreted reference values derived from the same MLS dataset as the evaluation reference, the proposed method achieves less than 2 mm deviation in both seam localization and dislocation analysis, demonstrating high consistency with manual interpretation. Compared with existing automatic approaches, the proposed method provides more accurate and reliable automated dislocation analysis, significantly reducing the need for manual inspection. The proposed method enhances the automation, consistency, and reliability of shield tunnel deformation assessment and provides an effective solution for structural health monitoring. Full article

Determining optimal process parameters for laser transmission welding (LTW) of solid/porous materials remains challenging due to the complexity of influencing factors. In this study, the welding of solid polycarbonate (PC) and porous polyethylene terephthalate (porous-PET) was chosen as an exemplary case and the relationship between parameters and welding quality was established using a Gaussian process regression (GPR) model. First, the experimental dataset, comprising welding power, welding speed, PC thickness, and porous-PET density, is established based on a flexible factor-level design. Then, the optimized GPR model trained based on the full experimental dataset achieved high predictive performance, significantly outperforming that trained with the averaged experimental dataset. Next, using the optimal prediction model as the objective function, three different optimization methods, genetic algorithm (GA), Bayesian optimization (BO), and covariance matrix adaptation evolution strategy (CMA-ES), are employed to optimize the process parameters, and the performance of the different optimization algorithms shows that CMA-ES has demonstrated the fastest convergence and the shortest runtime, while still converging to the same recommended parameters as GA and BO. Experimental validation confirms the accuracy of the recommended parameters, with a low relative error. Morphological analysis confirms that the weld seam is uniformly formed at recommended parameters. The proposed strategy provides an efficient route for achieving high-performance LTW joints and shows strong potential for improving process efficiency and reducing manufacturing cost in solid/porous materials joining. Full article

To reveal the influence of water accumulation in open pits on the stability of boundary coal–rock pillars, this study investigates a boundary coal–rock pillar between an underground coal mine and an adjacent open pit in western China. Coal–rock physical property tests, hydrochemical analysis, permeability tests, and theoretical calculations of water-resisting coal–rock pillars were conducted to examine seepage channel formation, physical property changes, and stability evolution under long-term water accumulation. The results show that the mechanical strength of coal and rock specimens decreases under the saturated state. The uniaxial compressive strength of rock specimens decreases by 8.75–50.64%, while that of No.2⁻² and No.3⁻¹ coal specimens decreases by 17.72% and 25.01%, respectively. The tensile strength decreases by 24.59–59.11%, and the shear strength decreases by 4.36–45.96%. The hydraulic conductivity of intact specimens is mostly 10⁻⁴~10⁻³ m/d, whereas that of fractured specimens increases to 10⁻³~10⁻² m/d. The calculated width of water-resisting coal pillars increases by 19.7~21.9% under long-term water accumulation. Long-term water accumulation in the open pit changes the external hydraulic boundary of the boundary coal–rock pillar, allowing water to migrate inward along bedding planes, joints, primary fractures, mining-induced fractures, and coal seam pores. This process promotes the connection of pre-existing pore–fracture structures and seepage channel formation, weakens particle cementation and structural-plane shear resistance, and reduces the structural integrity, bearing capacity, and water-resisting capacity of the coal–rock pillar. Therefore, the stability deterioration of boundary coal–rock pillars is a continuous process involving channel formation, sustained seepage, strength degradation, enhanced pore–fracture connectivity, permeability enhancement, and further stability reduction. Full article

The curing quality of epoxy resin at wind turbine blade joint seams critically affects blade integrity and service reliability, yet conventional metallic heating systems often suffer from poor temperature uniformity, limited flexibility, and slow thermal response. In this study, a flexible and lightweight heating blanket based on carbon nanotube (CNT) electrothermal film was developed for blade reinforcement and in situ curing applications. The device employs a multilayer composite architecture consisting of a CNT heating layer, a nano-aerogel thermal insulation layer, a thermoplastic polyurethane electrical insulation layer, and a silicone-coated glass fiber protective layer, together with an intelligent temperature control system. The resulting blanket, with a total thickness of 3.85 mm, exhibited rapid and stable heating performance, increasing from 25 to 120 °C within 8 min. Under resin-curing conditions, it achieved an initial heating rate of 7.2 °C min⁻¹ and a temperature uniformity of ±2.6 °C, markedly outperforming a conventional Ni@Cr alloy heating blanket. Accelerated aging tests further demonstrated stable electrothermal performance under the tested condition. Those results indicate that the proposed CNT-based heating blanket provides an efficient and reliable thermal management strategy for large curved composite structures. Full article

Hot-water bottles are widely used domestic heat sources, but seam and neck failures can cause scald injuries, and quantitative evidence supporting replacement guidance remains limited. This study compared the tensile behaviour of unused, dry-stored hot-water bottles manufactured in 2022 and 2024, focusing on vulcanised rubber and plasticised PVC constructions. ISO 37 Type 1 dumb-bell specimens were excised from body panels and seam regions and tested in uniaxial tension at 23 ± 2 °C and 50 ± 5% RH using a grip-separation rate of 500 mm min⁻¹. Stress–strain curves were analysed to determine maximum stress, failure strain, toughness and tangent modulus, with seam and face specimens compared within each material year cohort. PVC specimens were consistently stiffer and stronger than rubber specimens but failed at lower strain. Manufacturing year-associated differences were material-dependent: PVC-2022 generally showed higher maximum stress and toughness than PVC-2024, whereas Rubber-2022 underperformed Rubber-2024 at large strain. Seam–face ordering also depended on material and year, with PVC faces outperforming seams, while rubber showed cohort-specific behaviour. These findings indicate that hot-water bottle durability is influenced by both material system and joint region, supporting the need to consider seam performance alongside bulk material properties in safety assessment and replacement guidance. Full article

This study investigates the mechanical performance of a pulsed laser butt-welded IN792 DS joint and its relationship to its microstructure by means of grid nanoindentation. A new ISE-free (rate-derived) hardness parameter (H_R) has been introduced to account for the local bulk elastoplastic behavior of the material in combination with the stable contribution of residual stress, thus overcoming the limitations of the current standard codes. It allows performance comparability between different welding experiments, materials, and joint configurations. It offers an alternate means to mechanically determine the HAZ width when microscopic and metallurgical methods fail to detect it. Moreover, the spectra of two independent indentation parameters have been utilized as an input within an iterative statistical deconvolution scheme to estimate the composition of the relevant phases present within the fused zone. While one parameter spectrum acted as a predictor in the first stage, the second one served as a corrector for the final estimation of the four detected phases, thereby self-validating the iteration procedure with 5% tolerance. The validity of phase estimation was first determined over the entire FZ and then at three levels of the weald seam (top, neck and bottom) for further validation. The results indicate that the γ-matrix and ultrafine fine/hard second phases in the fused zone amounted to 54% and 43% volume fractions, respectively. The associated deconvoluted mechanical performance, expressed in terms of E_IT, H_IT, and H_R, corresponded to approximately 209 ± 4.5, 6.3 ± 0.2, 4.4 ± 0.1 and 224 ± 7.0, 6.7 ± 0.1, and 4.6 ± 0.1 GPa, respectively. A correlation between the estimated phases and the local mechanical performance via the conventional indentation parameter (H_IT and E_IT) and the new H_R parameter in the three relevant regions of the fused zone was discussed while discerning the effect of cooling rate on precipitate size, heterogeneity, porosity, residual stresses, and grain orientation. Further validation studies on different sample geometries, materials and joint configurations are needed to confirm the generality of the proposed methodology. Full article

The ultra-short-pulse (USP) laser joining of sapphire to iron is investigated by combining electron backscatter diffraction (EBSD) and ruby (Cr³⁺) R₁ fluorescence mapping to resolve the joint microstructure and pressure distributions. Energy-dispersive X-ray spectroscopy (EDS) reveals Al, O, and Fe intermixing within the seam, consistent with the formation of thin Fe–Al–O reaction layers. R₁ fluorescence yields a maximum internal pressure of $(490\pm 80)$ MPa within the modified sapphire region and decays to near-zero within a few micrometres distance from the seam. EBSD data suggest a single-crystal sapphire lattice with localized disorientation adjacent to the joint, whereas the iron foil remains polycrystalline with rolling-induced misorientation without additional weld-induced grain refinement. These results demonstrate that USP joining of sapphire to iron produces localized interfacial reaction zones, with confined pressure predominantly occurring within sapphire. Full article

To address the detection challenges in X-ray weld seam images caused by weak contrast, slender structures, and multi-scale coexistence, we propose MGDR-YOLO, an industrially deployable detector with four coordinated designs. First, a MultiBackbone parallel heterogeneous backbone is designed to perform complementary direction–detail modeling and lightweight context modeling under a shared shallow stem, enhancing the joint representation of fine-grained features and global semantics. Second, Gated Attention Fusion Block (GAFB) is introduced to perform selective in-scale fusion via channel gating and local–global attention mechanisms, thereby suppressing channel redundancy and noise leakage induced by naive concatenation. Third, Directional Feature Convolution (DFConv) decouples standard 2D convolution into horizontal and vertical branches and fuses them using depthwise separable convolution, substantially reducing computational cost while preserving geometric alignment. Finally, Rep Shared Convolutional Detection Head (RSCD) improves detection head consistency and inference throughput through cross-scale shared convolutions and a training-to-deployment re-parameterization scheme. The experimental results show that MGDR-YOLO significantly outperforms YOLOv11n, increasing the mean average precision (mAP) from 92.9% to 95.2%. The performance gain is most pronounced for the LP class (slender and low-contrast defects), with an mAP improvement of 10.1 percentage points. Meanwhile, the proposed model achieves a 39.4% increase in frames per second (FPS) while reducing the number of parameters by 46.2%, demonstrating superior efficiency. These results indicate that MGDR-YOLO consistently improves the accuracy and robustness of X-ray weld defect detection while maintaining real-time performance, making it well suited for resource-constrained industrial online inspection scenarios. Full article