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On the whole, if the climate of the saline soil distribution area is arid or semi-arid, with small precipitation and large evaporation. Thus, soil desiccation cracks are a common phenomenon in saline soil. The cracks of soils cause serious problems in several fields (e.g., agriculture, geotechnical engineering, and engineering geology). The type and amount of salt in saline soil have important effects on its mechanical and hydraulic characteristics and cracking behavior. The purpose of this study is to quantitatively analyze the effects of salt content on the characteristics of crack development and water evaporation in clay. Laboratory experiments were carried out on six groups of samples with thin layers of saturated clay slurry with NaCl content of 0, 1%, 2%, 4%, 8%, and 16% by weight of dry soil under controlled temperature and humidity. During the drying process, the mass of the samples was recorded using an electronic balance, and photographs were taken with a digital camera to record the development of surface cracks. The geometric parameters of the crack images, such as the crack ratio and the total length of the crack network, were determined using digital image processing technology. Combined with the fractal theory, the development process of cracking was analyzed quantitatively. The results showed that water evaporation and crack development were both reduced with increases in NaCl content. The evaporation process of water could be divided into three stages: the steady, falling, and residual rate stages. With increases in NaCl content, the crack ratio, total length of the crack network, average crack width, and fractal dimension of the crack all decreased. When the NaCl content was equal to 16%, the surface of the soil was covered by a layer of salt due to salt crystallization, and no cracks appeared on the surface to the end of the evaporation process. The study results would contribute to hydrological analyses in arid and semi-arid lands with potential applications in water and land resources management.

Several studies have been conducted on the fatigue behavior of copper and 7-3, and 6-4 brasses. However, there have been fewer studies on the fatigue behavior and fatigue crack growth (FCG) properties of free-cutting brass, primarily because emphasis has been placed on the development of lead-free free-cutting brass. In this study, fatigue experiments were performed in the atmosphere at room temperature using three types of free-cutting, two types of bismuth (Bi)-based (with different grain sizes), and lead (Pb)-based brasses. It was found that lead-free Bi-based free-cutting brass had approximately the same fatigue performance as that of Pb-based free-cutting brass. It was also clarified that the addition of Bi or Pb initiated fatigue cracks, and that the crack growth period occupied most of the fatigue life. Differences in the FCG behavior of the three free-cutting brasses were observed in the low ΔK range. The modified linear fracture mechanics parameter M was used to quantitatively analyze the fatigue life and FCG behavior (short surface cracks). A comparison between the calculated and experimental results showed that M was useful.

It is significant to conduct anchorage on the non-persistent jointed rock mass by bolts to inhabit the unstable fracture evolution of rock engineering. In this research, a series of experiments are done on 70 specimens to investigate the effect of anchorage method on strength and deformation behavior of a non-persistent jointed rock mass. First, based on the stress–strain curves of anchorage jointed specimens, the effect of anchorage method on the peak strength and elastic modulus of non-persistent jointed rock mass is investigated. The experimental results show that the peak strength and elastic modulus of jointed specimens changes with anchorage method for the same joint angle, while first decreases and then increases from 0° to 90° for the same anchorage method. Second, the effect of pretightening force on the strength and deformation behavior of anchorage jointed specimens is analyzed. The axial stress-axial strain curves of anchorage jointed specimens with various pretightening forces can be characterized into five types: (I) strain softening after the specimen drops to yield platform from peak strength; (II) strain hardening after the specimen drops to yield platform from peak strength; (III) strain hardening after the specimen yields; (IV) stepwise strain softening after the peak strength; (V) single stress drop after the peak strength. The peak strength and elastic modulus of jointed specimens for the same dip angle all increases nonlinearly with the pretightening force. And then, based on a series of surface failure mode of non-persistent jointed rock specimen, it can be seen that the initiation, propagation and coalescence of surface cracks depend on not only the joint angle, but also the magnitude of pretightening force. Twelve crack coalescence types are identified to analyze the surface crack mode of anchorage non-persistent jointed rock specimens. Finally, the effect of pretightening force on brittleness index of non-persistent jointed rock mass is made a discussion.

Freeze‐thaw cycles and desiccation drastically affect soil surface cracking and shrinkage processes, altering soil pore structures and hydraulic properties. However, studies using geophysical methods to assess how soil crack patterns and shrinkage respond to climate change remain scarce. Induced polarization, a non‐intrusive geophysical technique, is highly sensitive to the moisture content and pore structure of the porous media. For the first time, we investigate the effect of soil surface crack patterns on complex conductivity under freeze‐thaw cycling and desiccation processes using a new experimental set‐up. Results show that Y‐junction‐dominated crack patterns form on the sample surface after freeze‐thaw cycling, and surface cracking network extends along existing cracks until desiccation ceases. Our findings from induced polarization experiments reveal that two components of complex conductivity are linearly sensitive to surface crack ratio and gravimetric water content. Additionally, they both exhibit a similar decay behavior with drying time. In summary, these results indicate that induced polarization provides a preliminary approach for monitoring the surface crack patterns of clayey soils. Plain Language Summary Geophysical measurements (e.g., induced polarization) provide crucial information about pore structure and surface electrochemical properties, enabling the non‐invasive identification of clay minerals. Despite growing concern about soil cracking due to global climate change, induced polarization is rarely used to monitor soil surface crack patterns. Laboratory experiments in this study demonstrate its usefulness in characterizing the cracks propagation of natural soils during freeze‐thaw cycles and desiccation, highlighting the potential application for induced polarization in addressing this complex issue. Key Points We propose a novel set‐up for measuring the complex conductivity spectra and monitoring the soil surface crack patterns An exponential decline in both in‐phase and quadrature conductivities with drying time, characterized by the same decay time (τ = 358 min) We evidence a clear relationship between both in‐phase and quadrature conductivities and the gravimetric water content or the surface crack ratio

Landslide is one of the most frequent and destructive geohazards around the world. The accurate identification of potential landslides plays a vital role in the management of landslide risk. The use of unmanned aerial vehicle (UAV) techniques has recently gained much popularity in landslide assessment; however, most of the current UAV-image-based landslide identifications rely upon visual inspections. In this paper, an image-analysis-based landslide identification framework is developed to detect the landslides in UAV images by recognizing the landslide boundaries and ground surface cracks. In this framework, object-oriented image analysis is undertaken to identify the potential landslide boundaries in the input UAV images and the ground surface cracks in the UAV images are recognized by an automatic ground surface crack recognition model, which is trained through a deep transfer learning strategy. With the aid of this transfer learning strategy, the crack recognition model trained can take advantage of the feature of local ground surface cracks in the concerned area and the crack recognition model that has well been developed based on the samples of ground surface cracks collected from different landslide sites. Then, the landslide boundaries and the ground surface cracks obtained are fused based on Boolean operations; the fusion results can allow for informed landslide identification in UAV Images. To illustrate the effectiveness of the proposed image-analysis-based landslide identification framework, the Heifangtai Terrace of Gansu, China, was selected as a study area, and the identification results are further validated through comparisons with the field survey results.

Current semantic segmentation models have limitations in addressing tunnel lining crack, such as high complexity, misidentification, or inability to detect tiny cracks in specific practical scenarios, which is crucial for precise assessment of tunnel lining health. We developed a novel approach called EDeepLab, aiming to achieve a higher precision detection and segmentation of lining surface crack. EDeepLab improves upon the original DeepLabV3+ framework by replacing its backbone network with an optimized lightweight EfficientNetV2. The amount of EfficientNetV2 block computation is reduced and a self-designed shallow feature fusion module is used to merge the layers to enhance parameter utilization efficiency. Furthermore, the normalization-based attention module and convolutional block attention module attention mechanisms are integrated to classify and process both high and low dimensional information features. This allows for comprehensive utilization of global semantic information and channel information, thereby enhancing the model’s feature extraction capability. Results in constructed metro-tunnel crack dataset demonstrate that the number of parameters is reduced from 144.45 M in the DeepLabV3+ to 99.80 M in the EDeepLab. EDeepLab achieves a mean intersection over union of 84.77%, mean pixel accuracy of 94.96%, and frames per second of 18.52 f/s. The proposed EDeepLab outperforms other models including U-Net, ResNet and fully convolutional networks in the quantitative analysis of tiny cracks and noise interference.

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

Concrete surface crack detection is a critical problem in the health monitoring and maintenance of engineering structures. The existence and development of cracks may lead to the deterioration of structural performance, potentially causing serious safety accidents. However, detecting cracks accurately remains challenging due to various factors such as uneven lighting, noise interference, and complex backgrounds, which often lead to incomplete or false detections. Traditional manual inspection methods are subjective, inefficient, and costly, while existing deep learning-based approaches still have the problem of insufficient precision and completeness. Therefore, this paper proposes a new crack detection model based on an improved TransUNet: AG-TransUNet, an adaptive multi-head self-attention mechanism, and a gated mechanism-based decoding module (GRU-T) is introduced to improve the accuracy and completeness of crack detection. Experimental results show that the AG-TransUNet outperforms the original TransUNet with a 4.05% increase in precision, a 2.59% improvement in F1-score, and a 0.36% enhancement in IoU on the CFD dataset. The AG-TransUNet achieves a 2.21% increase in precision, a 5.63% improvement in F1-score, and a 9.07% enhancement in IoU on the concrete crack dataset. In addition, in order to further quantitatively analyze the crack width, the orthogonal skeleton method is used to calculate the maximum width of a single crack to provide a reference for engineering maintenance. Experiments show that the maximum error between the real values and detection results is about 5%. Therefore, the proposed method better meets the needs of crack detection in practical engineering applications and provides a solution for improving the efficiency of crack detection.

In order to determine the causes of crack defects in Mn18Cr2 high-manganese wear-resistant steel plates, this paper conducted a systematic analysis of the steel plates’ microstructure, chemical composition, and hardness via metallographic microscopy, field-emission scanning electron microscopy, and Vickers hardness tester. The results indicated that there were folded cracks on the surface of the steel plate. The interior of the cracks was oxidized, and inclusions were observed in the crack gaps. A significant difference in the contents of Mn and Cr elements was detected at the defect locations, indicating that very obvious long-range diffusion of Mn and Cr elements had occurred during long-term high-temperature oxidation. The crack defects on the surface of the steel plate were related to the inheritance of the original cracks on the surface of the cast billet before rolling. There were cracks on the surface of the cast billet; the oxide scale and inclusions inside the cracks had not been completely removed. Multiple passes of rolling led to the cracks and oxide scale being pressed into the steel surface, thereby forming folding defects. The fine grain strengthening and deformation twinning generated by rolling deformation formed the hardened layer on the surface, resulting in higher surface hardness than core hardness. The austenite grain size inside the steel plate was in the range of 23–30 μm, and the hardness was around 275 HV. The grain size near the surface of the steel plate was around 10 μm. The surface hardness was 351 HV, which was higher than the core hardness of the steel plate.

The surface crack of ballastless track slab can seriously reduce the serviceability and durability of high-speed railway (HSR). Aiming at accurately and efficiently detecting the slab cracks, this research proposes an infrared thermography (IRT)-based method for the surface crack, which is the most serious and common crack type in track slab. A three dimensional finite element (FE) model of IRT detection of concrete slab with surface cracks is established. The relation between the width of detectable cracks and the ambient temperature can be thereby obtained by inputting the measured thermodynamic parameters in the model. Parametric study shows that with ambient temperature higher than 15 °C, cracks with a width of no less than 0.2 mm can be well detected. Scale model test and field test are conducted, IRT method can effectively locate the slab surface cracks with width as small as 0.14 mm when ambient temperature is no less than 20 °C.