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Crack detection plays a critical role in ensuring road safety and maintenance. Traditional, manual, and semi-automatic detection methods have proven inefficient. Nowadays, the emergence of deep learning techniques has opened up new possibilities for automatic crack detection. However, there are few methods with both localization and segmentation abilities, and most perform poorly. The consistent nature of pavement over a small mileage range gives us the opportunity to make improvements. A novel data-augmentation strategy called CrackMover, specifically tailored for crack detection methods, is proposed. Experiments demonstrate the effectiveness of CrackMover for various methods. Moreover, this paper presents a new instance segmentation method for crack detection. It adopts a redesigned backbone network and incorporates a cascade structure for the region-based convolutional network (R-CNN) part. The experimental evaluation showcases significant performance improvements achieved by these approaches in crack detection. The proposed method achieves an average precision of 33.3%, surpassing Mask R-CNN with a Residual Network 50 backbone by 8.6%, proving its effectiveness in detecting crack distress. [ABSTRACT FROM AUTHOR]

The normal contact stiffness (NCS) on rough surfaces has a significant impact on the dynamic characteristics of helical gear. Aiming at the problem of inaccurate calculation of the NCS model under the traditional Hertz theory of smooth surfaces, a fractal prediction model of helical gear contact stiffness considering asperity lateral contact and interaction between asperities is proposed in this paper. The variation formula of asperity and the correction coefficient of a tooth contact surface under asperity lateral contact and interaction are derived, and the influence of micro-elements on normal load and NCS is qualitatively analyzed. The results show that the NCS of considering the interaction and lateral contact of asperity is closer to the experimental results; the contact surface correction coefficient increases with the increase of curvature radius and load. The NCS of a tooth surface increases with the increase in fractal dimension D or the decrease in roughness amplitude G. The influence of asperity lateral contact and interaction decreases with the increase in D and the decrease in G. The NCS of the helical gear decreases under the lateral contact and interaction of the asperity, which is critical for exact estimation of the NCS of contact surfaces in gear. [ABSTRACT FROM AUTHOR]

The application of three-dimensional laser scanning technology in the field of tunnel deformation monitoring has changed the traditional measurement method. It provides an automated and intelligent solution for monitoring the geometric deformation of tunnel sections due to its high efficiency and independence from environmental influences. In this paper, based on B-spline fitting and iterative nearest point (ICP) alignment, the calculation of the difference between the radial distance and the design radius of a tunnel is transformed into a curve transformation that iterates over the nearest-neighbor points and calculates the difference in the distance between the corresponding points. The innovation of this paper is that the high-precision tunnel deformation monitoring method integrating B-spline fitting and ICP alignment can automatically compensate for the missing point clouds, is not affected by the point clouds of the tunnel inner and outer liner appendages, is more sensitive in the local deformation feedback and can be applied to a variety of tunnel shapes. The results indicate that our method maximally improves the accuracy of the horizontal convergence calculation by 28.6 mm and the accuracy of the vault settlement by 27.8 mm in comparison with the least squares circle fitting algorithm. [ABSTRACT FROM AUTHOR]

Bamboo invasion into broadleaf forests is a common phenomenon in karst areas; however, the effect of bamboo invasion on soil organic carbon (SOC) in karst areas and the mechanism of the effect are not clear. We selected the study site with broad-leaved forests (BF), mixed forests (MF), and pure bamboo (Indosasa shibataeoides McClure) forests (IF). Furthermore, we sampled the soil from 0 cm to 20 cm and 20 cm to 40 cm layers in the region and investigated the soil properties, organic carbon fractions, and microbial communities. At the same time, we sampled the litterfall layer of different stands and determined the biomass. The results showed that bamboo invasion increased the litterfall biomass per unit area of karst forest, increased the bulk weight of the 0–20 cm soil layer, and lowered the soil pH in the 0–20 cm and 20–40 cm soil layers, bamboo invasion consistently increased the content of soil AN and AK, whereas the content of AP was significantly reduced after bamboo invasion. Both active organic carbon groups (MBC, DOC, and EOC) and passive organic carbon groups (Fe/Al-SOC and Ca-SOC) increased significantly after bamboo invasion. The bamboo invasion increased the diversity of soil microorganisms and bacterial communities; the relative abundance of Actinobacteriota increased in MF and IF, while the relative abundance of Firmicutes decreased in IF. The structure of fungal communities was altered during the bamboo invasion, with an increase in the relative abundance of Mortierellomycota and a decrease in the relative abundance of Basidiomycota at the level of fungal phyla. Partial least squares path modeling analysis identified bamboo invasion enhanced SOC sequestration mainly by increasing litterfall biomass and altering the structure of the fungal community, and the effect of bacteria on SOC was not significant. Our study suggests that bamboo invasion of broadleaf forests is more favorable to soil organic carbon sequestration in karst areas. [ABSTRACT FROM AUTHOR]