High-precision 3D point cloud data have various analyses and application use cases. This study aimed to achieve a more precise noise reduction of the raw 3D point cloud data of asphalt pavements obtained using 3D laser scanning. Hence, a noise-reduction algorithm integrating improved Gaussian filtering and coefficient of variation was developed. A portable laser scanner was used to collect raw, high-precision 3D point cloud data of surface textures from pavement slab samples prepared with three different types of asphalt mixtures: AC-13, SMA-13, and OGFC-13, as well as asphalt from the test sections of the Yakang Expressway. An improved Gaussian filtering and Gaussian filtering that extracts noise using the coefficient of variation were used to filter out the obvious outlier noise and small-scale burr noise, respectively. Finally, the filtering effect of the proposed algorithm, Gaussian filtering, median filtering, and mean filtering on raw 3D point cloud data of pavement textures was evaluated through subjective visual quality and objective index evaluations. The results showed that the proposed algorithm filters out noise while preserving the micro-texture structure information, outperforming Gaussian filtering, median filtering, and mean filtering.

In recent years, various kinds of civil explosive detonation accidents have occurred frequently around the world, resulting in substantial human casualties and significant property losses. It is generally believed that thermal stimulation plays a critical role in triggering the detonation of explosives; consequently, the study of the thermal hazards of explosives is of great significance to many aspects of safety emergency management practices in the production, transportation, storage, and use of explosives. It is known that the thermal stability of the ammonium perchlorate-aluminium system and the ammonium nitrate-aluminium system has been extensively investigated previously in the literature. However, there is a paucity of research on the thermal hazard characteristics of non-ideal explosives under varying oxygen balance conditions within the academic sphere. Therefore, this research focused on the study of the thermal hazards of non-ideal explosives based on thermokinetic analysis. The thermal hazards of non-ideal explosive mixtures of ammonium perchlorate and aluminium and of ammonium nitrate and aluminium were studied by thermal analysis kinetics. The thermokinetic parameters were meticulously studied through differential scanning calorimetry (DSC) analysis. The results showed that the peak reaction temperature and activation energy of the ammonium perchlorate-aluminium system were significantly higher than those of the ammonium nitrate-aluminium system. Under the condition of zero oxygen balance, the peak reaction temperature of the ammonium nitrate-aluminium system was 259 °C (heating rate 5 °C/min), and the activation energy was 84.7 kJ/mol. Under the same conditions, the peak reaction temperature and activation energy of the ammonium perchlorate-aluminium system were 292 °C (heating rate 5 °C/min) and 94.9 kJ/mol, respectively. These results indicate that the ammonium perchlorate-aluminium system has higher safety under the same thermal stimulation conditions. Furthermore, research on both non-ideal explosive systems reveals that the activation energy is at its peak under negative oxygen balance conditions, recorded at 104.2 kJ/mol (ammonium perchlorate-aluminium) and 86.2 kJ/mol (ammonium nitrate-aluminium), which indicates a higher degree of safety. Therefore, the investigation into the thermal hazards of non-ideal explosive systems under different oxygen balance conditions is of utmost importance for the enhancement and improvement of safety emergency management practices.

Biomass-derived 5-hydroxymethylfurfural (HMF) is a key platform molecule for producing high-value chemicals and clean fuels. This study presents a Cu₂O-decorated BiVO₄ nanocomposite photocatalyst synthesized via a sol-gel-hydrothermal method, enabling efficient visible-light-driven oxidation of HMF to 2,5-diformylfuran (DFF) with simultaneous H₂ production. The p-n heterojunction formed between Cu₂O and BiVO₄ promotes charge separation, enhancing photocatalytic activity. Under optimized conditions, the system achieved 49% DFF yield and 947 μmol/g H₂ in 8 h, maintaining high selectivity and stability over five cycles. The work offers a sustainable route for biomass valorization and clean energy generation.

As China’s urban underground area grows, deep foundation pit projects in complex geological circumstances, particularly near critical infrastructure, must adhere to tight deformation control guidelines. However, limited research has been conducted on the deformation behavior of internal bracing systems in Sichuan’s sandy cobble strata. This research centers on a deep excavation near civil defense facilities in Pujiang County, Chengdu. We investigated the deformation characteristics of retaining piles and internal bracing systems using field monitoring, finite element simulations, and parameter sensitivity analysis, and proposed optimization solutions for the support scheme. Road settlement, pile-head vertical displacement, building settlement, and deep lateral displacement of retaining piles were all monitored in the field at different phases of excavation. MIDAS/GTS was used to generate a 3D finite element model that included bored piles as a contiguous pile wall. The model was verified against monitored data and showed a maximum variation of 3.7%. Parametric studies were conducted to optimize the equivalent stiffness of the contiguous pile wall and the standardized internal bracing system. The findings indicate that the maximum lateral displacement of retaining piles is the primary optimization restriction. Reducing the equivalent stiffness to 0.6t (relative to the baseline thickness t) causes displacement to surpass the warning threshold (35 mm), whereas increasing it to 1.2t or 1.4t limits deformation without incurring significant costs. Case G of the standardized internal bracing system ensures that the maximum pile displacement (21.95 mm) remains below the warning criterion (24.5 mm) while improving constructability. This work elucidates the deformation characteristics of internal bracing systems in sandy cobble strata near sensitive buildings, offering theoretical and practical assistance for comparable projects.

Pavement surface texture significantly affects its skid resistance. To characterize pavement surface texture and analyze its correlation with skid resistance, this paper proposes a novel three-dimensional (3D) texture evaluation index: mean texture surface area density (MTSAD). First, field tests were conducted on Chengdu Greenway pavement using a portable laser scanner to collect high-precision texture data, while a pendulum friction tester was employed to measure the British Pendulum Number (BPN). Subsequently, digital image processing technology was employed for the 3D reconstruction of pavement texture. Leveraging the high-resolution data characteristics and incorporating the concept of infinite subdivision, an innovative method for calculating the pavement texture surface area was developed, ultimately yielding the MTSAD. Finally, polynomial regression analysis was performed to examine the correlation between MTSAD and BPN, revealing a coefficient of determination (R²) of 0.8302. The results demonstrate a close relationship between MTSAD and pavement friction, while proving that texture indices that are easy to promote can be obtained through high-precision 3D point cloud images, and validating the potential of non-contact texture measurement as a viable alternative to conventional contact-based friction testing methods.

Field-road cracks in high-standard farmland are often slender, low-contrast, and surrounded by complex textures that cause U-Net-based models to misclassify aggregates, tire marks, shadows, and repair edges as cracks. To reduce these false positives, this study develops a task-oriented context-guided hard-negative background suppression network (CGHN-Net) based on Squeeze-and-Excitation U-Net (SE-U-Net). The context-guided skip gate (CGSG), a same-resolution adaptation of additive attention gating, uses already upsampled decoder features as semantic guides to filter encoder skip features at all three scales. Hard-negative background suppression loss (HNBS Loss), a background-restricted hard-example mining objective, further targets elevated-probability responses within ground-truth background regions. The dataset comprised 2235 vehicle-acquired grayscale pavement images from independent sessions and mutually exclusive road sections: 1684 for training, 464 for validation, and 87 for testing. Across three random seeds, CGHN-Net achieved Dice, IoU, precision, recall, and FP area ratio values of 0.8682 ± 0.0055, 0.7791 ± 0.0102, 0.8780 ± 0.0161, 0.8734 ± 0.0266, and 0.0042 ± 0.0008, respectively. Against U-Net, Attention U-Net, UNet++, DeepLabV3+, SegFormer-B0, and BGCrack, it achieved the highest Dice, IoU, and recall, indicating the strongest overall overlap and crack recovery. Sequence-aware paired analysis against UNet++ preserved contiguous acquisition order through block lengths of 3, 5, and 10 images, and all block-bootstrap confidence intervals excluded zero. On 100 held-out crack-free images, CGHN-Net also achieved the lowest post-processed image-level false-alarm rate and FP area ratio among the included models. Additional three-seed validation on the independently acquired public CrackForest Dataset (CFD), with the selected models retrained on mutually exclusive CFD partitions, showed that CGHN-Net achieved Dice, IoU, and recall of 0.6679 ± 0.0162, 0.5028 ± 0.0182, and 0.9486 ± 0.0085, respectively, exceeding UNet++ and BGCrack in overlap and crack recovery. The results support the task-oriented combination of same-resolution skip filtering and background-restricted hard-example mining for suppressing texture-induced false responses, while the CFD experiment is interpreted as independent public-dataset retraining rather than zero-shot transfer.