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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.

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.

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.

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.

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.

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.

Linear fracturing fluid (LFF) provides viscosity driven benefits of proppant suspensibility and fluid loss control, and with the use of a breaker agent, flowback recovery can be greatly enhanced. Shale tensile strength is critical in the prediction of fracture initiation and propagation, but its behavior under the interaction with LFF at reservoir temperature conditions remains poorly understood. This necessitated an in-depth investigation into the tensile strengths of Eagle Ford and Wolfcamp shales under thermally conditioned LFF and reservoir temperature controlled conditions. Brazilian Indirect Tensile Strength (BITS) testing was carried out for the quantitative evaluation of shale tensile strength, followed by extensive failure pattern classifications and surface crack length analysis. The thermally conditioned LFF saturation of shale samples led to average tensile strength (ATS) increases ranging from 26.33–51.33% for Wolfcamp. Then, for the Eagle Ford samples, ATS increases of 3.94 and 6.79% and decreases of 3.13 and 15.35% were recorded. The exposure of the samples to the temperature condition of 90 °C resulted in ATS increases of 24.46 and 33.78% for Eagle Ford and Wolfcamp shales, respectively. Then, for samples exposed to 220 °C, ATS decreases of 6.11 and 5.32% were respectively recorded for Eagle Ford and Wolfcamp shales. The experimental results of this research will facilitate models’ development towards tensile strength predictions and failure pattern analysis and quantifications in the LFF driven hydraulic fracturing of shale gas reservoirs.

In the process of large-scale industrial production, in order to control the surface accuracy and quality of components, it is necessary to carry out efficient detection of surface cracks. However, traditional manual visual inspection and other means are not conducive to large-scale industrial utilization due to the subjective human factors such as personnel experience and level. Based on this, this paper first analyses the basic principle of computer image recognition, and then studies the utilization of computer image recognition in surface crack detection, and gives the specific utilization steps, utilization methods and detection results.

This study investigates material fatigue failure and fractures, mainly focusing on surface cracks and their implications in engineering applications. It reviews existing research to underscore the significance of understanding fatigue behaviour for maintaining structural reliability. The problem statement emphasises the challenges posed by material failure and the necessity for practical analysis to extend the product lifespan. The study aims to predict fatigue crack propagation in aluminium alloys under bending tests by using finite element analysis and fatigue crack growth (FCG) models. It utilises the S-version finite element method (S-FEM). It compares different FCG models to select the best model and to improve its accuracy towards actual experiment results. Specific study limits have been employed in this research to provide a particular investigational scope. The study examines four FCG models: Paris, Walker, Frost & Pook, and Huang & Moan. The specimens used in this analysis are aluminium alloy 7075-T6 and aluminium alloy AISi10Mg. The bending models used in this research are the three-point bending and four-point bending models. The result is examined based on the objectives of this study by using data collected from the simulation and experiment. Subsequently, a conclusion is drawn from the study’s findings which found that the Frost and Pook model demonstrated the most effective FCG prediction among the evaluated models.

This study aims to investigate the impact of various input variables in electrical discharge machining (EDM) on specific responses, including surface crack length (SCL) and surface roughness (SR). The variables under scrutiny are the electrical conductivity of the workpiece tool, pulse-on time, gap voltage, pulse-off time, and gap current. The study focuses on generating a mesoscale square blind hole in both cryo-treated and untreated workpieces using electrolytic oxygen-free copper. Experimental design and statistical software were employed to facilitate the analysis, following Taguchi’s L18 (61 × 34) orthogonal array. Through heat map, it was determined that pulse on time, pulse off time, and gap voltage significantly influence surface roughness. On the other hand, workpiece electrical conductivity, gap current, gap voltage, and pulse on time were found to impact surface crack length. It can be seen from the study that the formation of surface cracks exhibited a decreasing trend at the initial level of conductivity of the workpiece, while SCL increased as the WEC was raised. Additionally, lower values of gap current were associated with greater crack length, whereas increasing the gap current reduced crack length. Furthermore, an increase in gap voltage corresponded to an increase in crack length, whereas crack length decreased with an increase in pulse on time. Machine learning regression methods employed in the study could predict surface roughness and surface crack length values with R-squared values more than 0.90.