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Pore and crack formation in parts produced by additive manufacturing (AM) processes, such as laser powder bed fusion, is one of the issues associated with AM technology. Surface and subsurface cracks and pores are induced during the printing process, undermining the printed part durability. In-situ detection of defects will enable the real-time or intermittent control of the process, resulting in higher product quality. In this paper, a new eddy current-based probe design is proposed to detect these defects in parts with various defects that mimic pores and cracks in additively manufactured parts. Electromagnetic finite element analyses were carried out to optimize the probe geometry, followed by fabricating a prototype. Artificial defects were seeded in stainless steel plates to assess the feasibility of detecting various flaws with different widths and lengths. The smallest defect detected had a 0.17 mm radius for blind holes and a 0.43 mm notch with a 5 mm length. All the defects were 0.5 mm from the surface, and the probe was placed on the back surface of the defects. The surface roughness of the tested samples was less than 2 µm. The results show promise for detecting defects, indicating a potential application in AM. [ABSTRACT FROM AUTHOR]

Concrete surface crack detection is a critical research area for ensuring the safety of infrastructure, such as bridges, tunnels and nuclear power plants, and facilitating timely structural damage repair. Addressing issues in existing methods, such as high cost, lengthy processing times, low efficiency, poor effectiveness and difficulty in application on mobile terminals, this paper proposes an improved lightweight concrete surface crack detection algorithm, YOLOv8-Crack Detection (YOLOv8-CD), based on an improved YOLOv8. The algorithm integrates the strengths of visual attention networks (VANs) and Large Convolutional Attention (LCA) modules, introducing a Large Separable Kernel Attention (LSKA) module for extracting concrete surface crack and local feature information, adapted for features such as fracture susceptibility, large spans and slender shapes, thereby effectively emphasizing crack shapes. The Ghost module in the YOLOv8 backbone efficiently extracts essential information from original features at a minimal cost, enhancing feature extraction capability. Moreover, replacing the original convolution structure with GSConv in the neck network and employing the VoV-GSCSP module adapted for the YOLOv8 framework reduces floating-point operations during feature channel fusion, thereby lowering computational complexity whilst maintaining model accuracy. Experimental results on the RDD2022 and Wall Crack datasets demonstrate the improved algorithm increases in mAP50 by 15.2% and 12.3%, respectively, and in mAP50-95 by 22.7% and 17.2%, respectively, whilst achieving a reduced model computational load of only 7.9 × 109, a decrease of 3.6%. The algorithm achieves a detection speed of 88 FPS, enabling real-time and accurate detection of concrete surface crack targets. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

This study proposed a strategy to prepare metalized pellets for direct steelmaking by hydrogen cooling reduction (HCR) of iron ore pellets with a focus on the effect of H2 flow rate on the process. It was demonstrated that increasing H2 flow rate could effectively enhance the reduction performance of iron ore pellets. However, due to the influence of the countercurrent diffusion resistance of gas molecules, too high H2 flow rate no longer promoted the reduction of the pellets when the maximum reduction rate was reached. The reduction swelling index (RSI) of the pellets initially increased and then decreased with increasing H2 flow rate. This change was associated with the decreased content of Fe2SiO4 in the metalized pellets and the changes in porosity and iron particle size. The compressive strength (CS) decreased continuously, showing a sharp decline when the H2 flow rate reached 0.6 L/min. It was attributed to the significant increases in porosity and average pore size of the metalized pellets, with the presence of surface cracks. When the H2 flow rate was 0.8 L/min, the metalized pellets had the optimal performance, namely, reduction degree of 91.45%, metallization degree of 84.07%, total iron content of 80.67 wt%, RSI of 4.66%, and CS of 1265 N/p. The findings demonstrated the importance of controlling the H2 flow rate in the preparation of metallized pellets by HCR. [ABSTRACT FROM AUTHOR]

Electrocatalytic alcohol oxidation (EAO) is an attractive alternative to the sluggish oxygen evolution reaction in electrochemical hydrogen evolution cells. However, the development of high-performance bifunctional electrocatalysts is a major challenge. Herein, we developed a nitrogen-doped bimetallic oxide electrocatalyst (WO-N/NF) by a one-step hydrothermal method for the selective electrooxidation of benzyl alcohol to benzoic acid in alkaline electrolytes. The WO-N/NF electrode features block-shaped particles on a rough, inhomogeneous surface with cracks and lumpy nodules, increasing active sites and enhancing electrolyte diffusion. The electrode demonstrates exceptional activity, stability, and selectivity, achieving efficient benzoic acid production while reducing the electrolysis voltage. A low onset potential of 1.38 V (vs. RHE) is achieved to reach a current density of 100 mA cm−2 in 1.0 M KOH electrolyte with only 0.2 mmol of metal precursors, which is 396 mV lower than that of water oxidation. The analysis reveals a yield, conversion, and selectivity of 98.41%, 99.66%, and 99.74%, respectively, with a Faradaic efficiency of 98.77%. This work provides insight into the rational design of a highly active and selective catalyst for electrocatalytic alcohol oxidation. [ABSTRACT FROM AUTHOR]

Featured Application: The findings of this study can inform the selection of the most appropriate post-printing process for achieving the desired final physical characteristics of 3D-printed foods. Extrusion-based three-dimensional (3D) food printing (3DFP) enhances the customization of 3D-printed foods by using multiple food pastes. Post-printing processes like baking are usually necessary and significantly impact the stability of the 3D-printed foods. This study aimed to produce multi-material 3D-printed foods using nixtamalized corn dough and chickpea paste (CP) in extrusion-based 3DFP and to study the effect of post-printing processes (water oven cooking and steam cooking) and the type of material used (single- or multi-material) on the final appearance, weight, size, and texture of the 3D-printed foods. Multi-material 3D-printed foods were successfully produced using extrusion-based 3DFP. Steam-cooked 3D-printed foods cooked uniformly and had a better appearance, as they did not develop surface cracks compared to water oven-cooked foods. Water-oven cooked foods experienced a greater weight loss of 35.6%, and higher height and length reduction of 1.5% and 8.4%, respectively. Steam-cooked multi-material 3D-printed foods were harder at 40% of strain, with force values of 66.9 and 46.3 N for water-oven cooked foods. Post-printing processes, as well as the presence of CP in the middle of the 3D-printed foods, influenced their final appearance, weight, size, and texture. This study offers interesting findings for the innovative design of chickpea- and corn-based multi-material 3D-printed foods. [ABSTRACT FROM AUTHOR]

In this work, a two-parameter inversion problem is analyzed, related to surface crack widths for measuring depths of normal surface notches, based on a laser-based ultrasonic measurement method in the time domain. In determining the depth measurement formulas, the main technique is the time delay between reflected and scattered waves. Scattered waves are generated by two reflections along the bottom and three mode transformations at the surface of the crack tips. Moreover, the scattering angle of the mode-conversion waves is 30°. These two key factors lead to corrected item "2wβ" in the depth measurement formula. A laser-based ultrasonic experimental platform is built to generate and receive surface waves in a non-contact manner on aluminum and steel specimens with surface cracks. The depth measurement method proposed in this paper has been validated through theoretical, simulation, and experimental methods. Finally, in this paper, an effective approach for quantitatively measuring crack depths, based on laser ultrasound, using the time-domain properties of surface wave propagation is provided. [ABSTRACT FROM AUTHOR]

In recent years, computer vision technology has been extensively applied in the field of defect detection for transportation infrastructure, particularly in the detection of road surface cracks. Given the variations in performance and parameters across different models, this paper proposes an improved Faster R-CNN crack recognition model that incorporates attention mechanisms. The main content of this study includes the use of the residual network ResNet50 as the basic backbone network for feature extraction in Faster R-CNN, integrated with the Squeeze-and-Excitation Network (SENet) to enhance the model's attention mechanisms. We thoroughly explored the effects of integrating SENet at different layers within each bottleneck of the Faster R-CNN and its specific impact on model performance. Particularly, SENet was added to the third convolutional layer, and its performance enhancement was investigated through 20 iterations. Experimental results demonstrate that the inclusion of SENet in the third convolutional layer significantly improves the model's accuracy in detecting road surface cracks and optimizes resource utilization after 20 iterations, thereby proving that the addition of SENet substantially enhances the model's performance. [ABSTRACT FROM AUTHOR]

Longitudinal crack defects are a frequent occurrence on the surface of thin slabs during the high-speed continuous casting process. Therefore, this study undertakes a detailed analysis of the solidification characteristics of hyper-peritectic steel thin slabs. By establishing a three-dimensional heat transfer numerical model of the thin slab, the formation mechanism of longitudinal cracks caused by uneven growth of the initial shell is determined. Based on the mechanism of longitudinal crack formation, by adjusting the performance parameters of the mold flux, the contradiction between the heat transfer control and lubrication improvement of the mold flux is fully coordinated, further reducing the incidence of longitudinal cracks on the surface of the casting thin slab. The results show that, using the optimized mold flux, the basicity increases from 1.60 to 1.68, the F- mass fraction increases from 10.67% to 11.22%, the Na2O mass fraction increases from 4.35% to 5.28%, the Li2O mass fraction increases from 0.68% to 0.75%, and the carbon mass fraction reduces from 10.86% to 10.47%. The crystallization performance and rheological properties of the mold flux significantly improve, reducing the heat transfer performance while ensuring the lubrication ability of the molten slag. After optimizing the mold flux, a surface detection system was used to statistically analyze the longitudinal cracks on the surface of the casting thin slab. The proportion of longitudinal cracks (crack length/steel coil length, where each coil produced is about 32 m long) on the surface of the thin slab decreases from 0.056% to 0.031%, and the surface quality of the thin slab significantly improves. [ABSTRACT FROM AUTHOR]

This work demonstrates the potentials of a commercially available few-layer graphene (FLG) in enhancing the electro-dissipative properties, mechanical strength, and UV protection of polyolefin blend composites; interesting features of electronic packaging materials. Polyethylene (PE)/ polypropylene (PP)/ FLG blend composites were prepared following two steps. Firstly, different concentrations of FLG were mixed with either the PE or PP phases. Subsequently, in the second step, this pre-mixture was melt-blended with the other phase of the blend. FLG-filled composites were characterized in terms of electrical conductivity, morphological evolution upon shear-induced deformation, mechanical properties, and UV stability of polyolefin blend composites. Premixing of FLG with the PP phase has been observed to be a better mixing strategy to attain higher electrical conductivity in PE/PP/FLG blend composite. This observation is attributed to the influential effect of FLG migration from a thermodynamically less favourable PP phase to a favourable PE phase via the PE/PP interface. Interestingly, the addition of 4 wt.% (~2 vol.%) and 5 wt.% (~2.5 vol.%) of FLG increased an electrical conductivity of ~10 orders of magnitude in PE/PP—60/40 (1.87 × 10−5 S/cm) and PE/PP—20/80 (1.25 × 10−5 S/cm) blends, respectively. Furthermore, shear-induced deformation did not alter the electrical conductivity of the FLG-filled composite, indicating that the conductive FLG network within the composite is resilient to such deformation. In addition, 1 wt.% FLG was observed to be sufficient to retain the original mechanical properties in UV-exposed polyolefin composites. FLG exhibited pronounced UV stabilizing effects, particularly in PE-rich blends, mitigating surface cracking and preserving ductility. [ABSTRACT FROM AUTHOR]

One advantage of twin-roll casting for aluminum alloys is that hot rolling can be omitted, thus shortening the process. The effect of inline hot rolling on the anisotropy of the mechanical properties, especially the elongation, of the roll-cast strip has not been investigated. In a high-speed twin-roll caster, inline hot rolling forms the metal shape before the temperature of the cast strip decreases below the temperature needed for hot rolling. In this study, inline hot rolling of Al-5%Mg strips cast using an unequal diameter twin-roll caster was performed to validate the technique and evaluate its ability to reduce surface cracking and improve the elongation anisotropy. A rolling speed of 30 m/min was used, and the effects of temperature and thickness reduction during inline hot rolling on the surface and mechanical properties were investigated. Inline hot rolling was found to effectively reduce the formation of surface cracks and the anisotropy of the mechanical properties. [ABSTRACT FROM AUTHOR]

High-strength concrete (HSC) has a high compressive strength, high density, excellent durability, and seepage resistance, but its deformation ability is weak. Adding fibers can improve the physical and mechanical properties of HSC. Additionally, the HSC structure may face the threat of fire. In the process of fire extinguishing, the damage mechanism of high-temperature-resistant concrete is complicated due to the different contact conditions with water at different locations. Hence, it is essential to conduct pertinent research on the behavior of fiber-reinforced HSC with different cooling methods after high-temperature action. In this paper, polyvinyl alcohol fiber (PVA fiber) was selected to be added into the HSC to carry out high-temperature experimental research, so as to explore the apparent changes, failure pattern, and mass loss rate of the fiber-reinforced HSC using different cooling methods and analyze the influence of its residual compressive strength and flexural strength. The test results suggest that, with the increase in heating temperature, the color of the specimen's surface transitions from dark blue-gray to white, and the quantity of surface cracks on the specimen gradually rises. The mechanical strength gradually decreases as the heating temperature increases. At a consistent heating temperature, the mechanical strength initially rises, and then falls with an increase in fiber content. The maximum compressive strength and flexural strength were achieved at PVA fiber contents of 0.2% and 0.3%, respectively. For different temperatures and fiber contents, the mechanical strength after natural cooling is generally higher than that after immersion cooling. In addition, X-ray polycrystalline diffractometry (XRD) and scanning electron microscopy (SEM) tests were conducted to analyze the compositional alterations and microstructure of the fiber-reinforced HSC following high-temperature exposure, accompanied by an explanation of the factors influencing the alterations in the physical and mechanical properties. Therefore, the findings of this study can serve as a valuable reference for the utilization of HSC in engineering structures and contribute to the advancement of HSC technology. [ABSTRACT FROM AUTHOR]

Surface cracks are alluded to as one of the early signs of potential damage to infrastructures. In the same vein, their detection is an imperative task to preserve the structural health and safety of bridges. Human-based visual inspection is acknowledged as the most prevalent means of assessing infrastructures' performance conditions. Nonetheless, it is unreliable, tedious, hazardous, and labor-intensive. This state of affairs calls for the development of a novel YOLOv8-AFPN-MPD-IoU model for instance segmentation and quantification of bridge surface cracks. Firstly, YOLOv8s-Seg is selected as the backbone network to carry out instance segmentation. In addition, an asymptotic feature pyramid network (AFPN) is incorporated to ameliorate feature fusion and overall performance. Thirdly, the minimum point distance (MPD) is introduced as a loss function as a way to better explore the geometric features of surface cracks. Finally, the middle aisle transformation is amalgamated with Euclidean distance to compute the length and width of segmented cracks. Analytical comparisons reveal that this developed deep learning network surpasses several contemporary models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and Mask-RCNN. The YOLOv8s + AFPN + MPDIoU model attains a precision rate of 90.7%, a recall of 70.4%, an F1-score of 79.27%, mAP50 of 75.3%, and mAP75 of 74.80%. In contrast to alternative models, our proposed approach exhibits enhancements across performance metrics, with the F1-score, mAP50, and mAP75 increasing by a minimum of 0.46%, 1.3%, and 1.4%, respectively. The margin of error in the measurement model calculations is maintained at or below 5%. Therefore, the developed model can serve as a useful tool for the accurate characterization and quantification of different types of bridge surface cracks. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM in heritage has its challenges because BIM libraries are designed for new constructions and are incapable of accommodating the morphological irregularities found in historical structures. This article discusses an architecture survey workflow that uses TLS, imagery, and deep learning algorithms to optimize HBIM for the conservation of the Nabatean built heritage. In addition to creating new resourceful Nabatean libraries with high details, the proposed approach enhanced HBIM by including two data outputs. The first dataset contained the TLS 3D dense mesh model, which was enhanced with high-quality textures extracted from independent imagery captured at the optimal time and location for accurate depictions of surface features. These images were also used to create true orthophotos using accurate and reliable 2.5D DSM derived from TLS, which eliminated all image distortion. The true orthophoto was then used in HBIM texturing to create a realistic decay map and combined with a deep learning algorithm to automatically detect and draw the outline of surface features and cracks in the BIM model, along with their statistical parameters. The use of deep learning on a structured 2D true orthophoto produced segmentation results in the metric units required for damage quantifications and helped overcome the limitations of using deep learning for 2D non-metric imagery, which typically uses pixels to measure crack widths and areas. The results show that the scanner and imagery integration allows for the efficient collection of data for informative HBIM models and provide stakeholders with an efficient tool for investigating and analyzing buildings to ensure proper conservation. [ABSTRACT FROM AUTHOR]

Expansive clays present serious issues in a variety of engineering applications, including roadways, light buildings, and infrastructure, because of their notable volume changes with varying moisture content. Tough weather conditions can lead to drying and shrinking, which alters expansive clays' hydro-mechanical properties and results in cracking. The hydro-mechanical behavior of Al-Ghatt expansive clay and the impact of wetting and drying cycles on the formation of surface cracks are addressed in this investigation. For four cycles of wetting and drying and three vertical stress levels, i.e., 50 kPa, 100 kPa, and 200 kPa, were investigated. The sizes and patterns of cracks were observed and classified. A simplified classification based on main track and secondary branch tracks is introduced. The vertical strain measure at each cycle, which showed swell and shrinkage, was plotted. The hydromechanical behavior of the clay, which corresponds to three levels of overburden stress as indicated by its swell potential and hydraulic conductivity was observed. It was found that at low overburden stresses of 50 kPa, the shrinkage is high and drops with increasing the number of cycles. Al-Ghatt clay's tendency to crack is significantly reduced or eliminated by the 200 kPa overburden pressure. The results of this work can be used to calculate the depth of a foundation and the amount of partial soil replacement that is needed. [ABSTRACT FROM AUTHOR]

This paper proposes a road crack detection method based on residual and attention mechanisms to address the issues of difficult detection of small cracks on road surfaces in complex backgrounds and inaccurate crack detection edges. This method introduces residual modules in the encoder stage to better extract crack detail features and introduces attention mechanism modules in the skip connection structure of the network to better locate crack positions. Training and testing on public datasets have shown that compared with existing partial detection methods, our method has improved segmentation accuracy and generalization, and is more precise in segmenting small cracks, thus verifying the superiority of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

With the gradual densification of China's railway network, more and more railways are inevitably crossing active fault zones. Creep dislocation and stick–slip dislocation of the fault zones can lead to uneven deformation and cracks in the railway subgrade, threatening the sustainability of the railway. Therefore, a model test and numerical simulation were used to analyze the deformation mechanism and sustainability of the subgrade and foundation induced by a strike–slip fault dislocation in a flat site under different angles between the subgrade and fault strikes. The test and simulation results show that (1) under the influence of strike–slip fault dislocation, the horizontal direction of the subgrade appeared to have an S–shaped displacement. The top surface of the subgrade appeared to have a vertical settlement. Further, tension–shear cracks and compression crush zones appeared on the subgrade's surface. (2) When the angle between the fault and subgrade strikes increased, the subgrade's uneven deformations induced by fault dislocation first decreased and then increased. The angle between the main tension–shear crack on the top surface of the subgrade and the fault strike showed a trend of increasing and then decreasing. In addition, the compressive stress along the subgrade strike decreased, while the tensile stress along the subgrade strike gradually increased. (3) The tension–shear cracks in the subgrade and foundation were spiral and converged to the basement strike–slip fault along the depth direction. This study provides a reference for the safe operation and sustainability of railways near faults. [ABSTRACT FROM AUTHOR]

This research manuscript presents a comprehensive investigation into the prediction and detection of reflective cracking in pavement infrastructure through a combination of machine learning approaches and advanced image detection techniques. Leveraging machine learning algorithms, reflective cracking prediction models were developed and optimized for accuracy and efficiency. Additionally, the efficacy of image detection methods, particularly utilizing Mask R-CNN, was explored for robust and precise identification of reflective cracking on pavement surfaces. The study not only aims to enhance the predictive capabilities of pavement management systems (PMSs) through machine learning-based models but also seeks to integrate advanced image detection technologies to support real-time monitoring and assessment of pavement conditions. By providing accurate and timely detection of reflective cracking, these methodologies contribute to the optimization of pavement maintenance strategies and the overall improvement of pavement infrastructure management practices. Results indicate that the developed machine learning models achieve an average predictive accuracy of over 85%, with some models achieving accuracies exceeding 90%. Moreover, the utilization of a mask region-based convolutional neural network (Mask R-CNN) for image detection demonstrates exceptional precision, with a detection accuracy of over 95% on average across different pavement types and weather conditions. The results demonstrate the promising performance of the developed machine learning models in predicting reflective cracking, while the utilization of Mask R-CNN showcases exceptional accuracy in the detection of reflective cracking from images. This research underscores the importance of leveraging cutting-edge technologies to address challenges in pavement infrastructure management, ultimately supporting the sustainability and longevity of transportation networks. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic method to predict fatigue crack growth for surface flaws in pipelines using a particle filtering method based on Bayes theorem. The random response of the fatigue behavior is updated continuously as measured data are accumulated by the particle filtering method. Fatigue crack growth is then predicted through an iterative process in which particles with a high probability are reproduced more during the update process, and particles with a lower probability are removed through a resampling procedure. The effectiveness of the particle filtering method was confirmed by controlling the depth and length direction of the cracks in the pipeline and predicting crack growth in one- and two-dimensional cases. In addition, the fatigue crack growth and remaining service life with a 90% confidence interval were predicted based on the findings of previous studies, and the relationship between the fatigue crack growth rate and the crack size was explained through the Paris' law, which represents fatigue crack growth. Finally, the applicability of the particle filtering method under different diameters, aspect ratios, and materials was investigated by considering the negative correlation between the Paris' law parameters. [ABSTRACT FROM AUTHOR]

Longitudinal cracks are a common defect on the surface of continuous casting slabs, and cause increases in additional processing costs or long-time interruptions. The accurate identification of surface longitudinal cracks is helpful to ensure the casting process is adjusted in time, which significantly improves the quality of slabs. In this research, the typical temperature characteristics of thermocouples at the position of longitudinal cracks and their adjacent locations were extracted. The principal component analysis (PCA) method was used to reduce the dimensions of these characteristics to remove the redundant information. The particle swarm optimization (PSO) method was introduced to optimize the parameter. On this basis, a recognition model of surface longitudinal cracks was established, based on a particle swarm optimization–eXtreme gradient boosting (XGBOOST) model. Finally, this model was trained and tested using longitudinal crack and non-longitudinal crack samples and compared with the decision tree, the gradient boosting decision tree (GBDT) and XGBOOST models. The test results showed that PSO-XGBOOST had the best identification performance in all evaluation indexes. The accuracy, F1 score and alarm rate results were 95.8%, 92.3% and 100%, respectively, and the false alarm rate was as low as 5.5%. The research results provide a theoretical basis and a reliable model for surface longitudinal crack identification. [ABSTRACT FROM AUTHOR]

Cracks in engineered pipelines often appear in the form of multiple cracks or crack clusters with interactions between them. It is important to study the interaction between cracks if the pipeline crack cluster is to be evaluated in terms of equivalence and safety assessment. In this paper, based on FRANC3D crack analysis software, the interaction between circumferential parallel double cracks on the inner surface of pipelines was investigated, the factors affecting the interaction were examined, and the empirical equations for calculating the stress intensity factor (SIF) of double cracks was proposed. The results show that if there is no bias between the double cracks, the crack leading edge is shielded, but if there is offset between the double cracks, the crack leading edge is subjected to different interactions at different locations. The distal end of the cracks is generally strengthened, while the proximal end of the cracks is probably more shielded. The interaction effects between cracks are dependent on their relative positions rather than the pipe size or concerned crack size. According to the numerical simulation, boundaries for shielding or enhancing interactions were obtained, and the stress intensity factor calculation equations were fitted. [ABSTRACT FROM AUTHOR]

This study focused on the characterization of emulsions and microparticles encapsulating Chavir essential oil (EO) by application of modified whey protein concentrate–maltodextrin (WPC-MD). Different physical, chemical, morphological, thermal, and antioxidant properties and release behavior of spray-dried microparticles were assessed. Antioxidant, solubility, emulsifying, and foaming activities of modified WPC were increased compared to those of primary material. The results indicated that the particle size distribution varied depending on the type of carriers used, with the smallest particles formed by hydrolyzed WPC (HWPC). Binary blends of modified WPC-MD led to improved particle sizes. The spray-drying yield ranged from 64.1% to 85.0%, with higher yields observed for blends of MD with sonicated WPC (UWPC). Microparticles prepared from primary WPC showed irregular and wrinkled surfaces with indentations and pores, indicating a less uniform morphology. The UWPC as a wall material led to microparticles with increased small cracks and holes on their surface. However, HWPC negatively affected the integrity of the microparticles, resulting in broken particles with irregular shapes and surface cracks, indicating poor microcapsule formation. Encapsulating EO using WPC-MD increased the thermal stability of EO significantly, enhancing the degradation temperature of EO by 2 to 2.5-fold. The application of primary WPC (alone or in combination with MD) as wall materials produced particles with the lowest antioxidant properties because the EO cannot migrate to the surface of the particles. Enzymatic hydrolysis of WPC negatively impacted microparticle integrity, potentially increasing EO release. These findings underscore the crucial role of wall materials in shaping the physical, morphological, thermal, antioxidant, and release properties of spray-dried microparticles, offering valuable insights for microencapsulation techniques. [ABSTRACT FROM AUTHOR]

Accurate pavement surface crack detection is crucial for analyzing pavement survey data and the development of maintenance strategies. On the basis of Swin-Unet, this study develops the improved Swin-Unet (iSwin-Unet) model with the developed skip attention module and the residual Swin Transformer block. Based on the channel attention mechanism, the pavement crack region can be better captured while the crack feature channels can be assigned more weights. Taking advantage of the developed residual Swin Transformer block, the encoder architecture can globally model the pavement crack feature. Meanwhile, the crack feature information can be efficiently exchanged. To verify the pavement crack detection performance of the proposed model, we compare the training performance and visualization results with the other three models, which are Swin-Unet, Swin Transformer, and Unet, respectively. Three public benchmarks (CFD, Crack500, and CrackSC) have been adopted for the purpose of training, validation, and testing. Based on the test results, it can be found that the developed iSwin-Unet achieves a significant increase in mF1 score, mPrecision, and mRecall compared to the existing models, thereby establishing its efficacy in pavement crack detection and underlining its significant advancements over current methodologies. [ABSTRACT FROM AUTHOR]

The instability of hard and brittle rock often leads to disastrous consequences in underground engineering. Under various surrounding rock pressure conditions, in situ stress induces corresponding deformation and damage to the floor post-mining. Therefore, it is crucial to examine the effects of mining under different confining pressures on rock disturbance, damage characteristics, and their distribution. Consequently, triaxial loading experiments under varying intermediate principal stress conditions were conducted on red sandstone specimens, using an acoustic emission monitoring system to track energy changes during rock damage and failure. This approach aids in studying crack generation, propagation, and fracture damage evolution. The results indicate that rock deformation results in axial compression and dilatancy, aligned with the direction of minimum and intermediate principal stresses. Ductility in rock failure becomes more pronounced with increased stress, primarily manifesting as shear failure. Internal cracks in the specimen lead to stress concentration and marked plastic deformation under compression, yet do not result in macroscopic surface cracks. The fracture angle θ of specimens post-failure generally exceeds 45° and varies with stress changes; at consistent burial depths, the angle of the sandstone failure surface increases with intermediate principal stress. This paper preliminarily establishes the informational linkage between rock failure and energy release, analyzing the rock samples over time and space. This research offers insights for analyzing and mitigating sudden rock instability. [ABSTRACT FROM AUTHOR]

Investigation of breakdown pressure in wellbores in complex conditions is of great importance, both in fracture design and in wellbore log interpretation for in situ stress estimation. In this research, using a two-dimensional numerical model, the breakdown pressure is determined in ellipsoidal and breakout wellbores. To find the breakdown pressure, the mixed criterion is used, in which the toughness and the tensile strength criteria must be satisfied concurrently. In breakout boreholes, the breakdown pressure is lower than the circular wellbores; indeed, the ratio of the breakdown pressure of the breakout wellbore to the breakdown pressure in the circular wellbore is between 1 and 0.04, depending on the deviatoric stress and the width and depth of the breakout zone. In breakout wellbores, the fracture initiation position depends on the deviatoric stress. In small deviatoric stresses, the fracture initiation position is aligned with the minimum in situ stress, unlike circular boreholes; and in large deviatoric stresses, the fracture initiates in the direction of the major principal stress. In large wellbores, the breakdown pressure is controlled by the tensile strength of the rock; and in small wellbores, the breakdown pressure is under the control of the energy spent to create new crack surfaces. [ABSTRACT FROM AUTHOR]

The present research work describes the synthesis and characterization of CuO/SiO2 for coating-perforated 304 stainless steel (SS) substrates to degrade methylene blue and amoxicillin under visible light irradiation. The foregoing photocatalytic system was achieved through the coprecipitation method by adding pure CuO to a SiO2 sol at 1:5, 1:10, and 1:15 molar ratios. The conditions for carrying out the depositions on the SS substrates (three per substrate) involved an immersion rate of 90 mm/min with a drying time of 20 min at 120 °C. The XRD technique confirmed the presence of the SiO2 amorphous phases and CuO monoclinic systems in the coatings, with a particle size distribution ranging from 0.5 to 2.5 μm (with an average of 1.26 ± 0.06 μm). As for SEM, it revealed a homogeneous coating surface without cracks. The produced photoactive CuO/SiO2 coatings were capable of degrading methylene blue (98%) at 1500 min and amoxicillin (55%) at 450 min. [ABSTRACT FROM AUTHOR]

This paper describes hybrid fiber's influence on the crack permeability of cracked concrete exposed to freeze–thaw cycles. A permeability setup and a laser-scanning setup have been designed to measure the crack permeability and the fractured surface roughness of cracked hybrid fiber-reinforced concrete, containing polypropylene fiber and steel fiber, under a splitting tensile load. The results show that, when the effective crack width of the specimens is less than 25 μm, the rough crack surface significantly reduces the concrete's crack permeability. As the crack width increases, the effect of the concrete crack surface on crack permeability gradually decreases, and the crack permeability of the concrete is closer to the Poiseuille flow model. The permeability parameter α derived from the Poiseuille flow model is effective for assessing the crack permeability of concrete. Compared to the modified factor ξ of crack permeability, the permeability parameter α can effectively evaluate and quantify the development trend of crack permeability within a certain range of crack widths. The permeability parameter α of SF20PP2.3, subjected to the same freeze–thaw cycles, decreases by 16.3–94.8% compared to PP4.6 and SF40, and SF20PP2.3 demonstrates a positive synergistic effect on the crack impermeability of cracked concrete. The crack impermeability of SF40PP2.3, subjected to the same freeze–thaw cycles, lies between that of PP6.9 and SF60. The roughness of crack surface (X) and the crack permeability (Y) are highly correlated and follow an exponential curve (Y = 1.0415 × 107·e−6.025·X) in concrete. This demonstrates that hybrid fibers enhance crack impermeability by increasing the crack surface roughness. Furthermore, the combination of polypropylene fiber and steel fiber effectively promotes the formation of micro-cracks and facilitates the propagation of multiple cracks in the concrete matrix. This combination increases the head loss of water flow through the concrete and decreases the crack permeability. [ABSTRACT FROM AUTHOR]

This paper describes the effects of macro fibers on permeability and crack surface topography of layered fiber-reinforced concrete (FRC) specimens with different layering ratios under uniaxial tensile load. The crack permeability of layered FRC specimens is investigated by a self-designed permeability setup. The topographical analysis of crack surfaces is investigated by a custom-designed laser scanning setup. The results show that when the fiber volume content and layering ratio of the FRC layer are constant, the tensile toughness of layered FRC specimens depends on the proportion of steel fiber in macro fibers, and with an increase in the proportion of steel fiber, the tensile toughness of layered FRC specimens increases. For the layered FRC specimens, the crack permeability is much lower than that of the normal concrete (NC) specimen. A significant positive synergistic effect on crack impermeability can be achieved by the combination of steel fiber and polypropylene fiber in the SF80PP2.3 specimen. The crack surface roughness parameter (Rn) values of the NC layer in layered FRC specimens are all higher than those of the NC specimen, and the crack surface Rn of the FRC layer in layered FRC specimens is higher than that of the unlayered FRC specimens. This can effectively increase the head loss of cracks and reduce the crack permeability of layered FRC specimens. [ABSTRACT FROM AUTHOR]

Polymeric materials undergo degradation when exposed to outdoor conditions due to the synergistic effects of sunlight, air, heat, and moisture. The degradation can lead to a decline in mechanical properties, fading, surface cracking, and haziness, attributed to the cleavage of the polymer chains and oxidation reactions. Accelerated weathering testing is a useful technique to evaluate the comparative photodegradation of materials within a reasonable timeframe. This review gives an overview of the different degradation mechanisms occurring in conventional plastics and bio-based materials. Case studies on accelerated weathering and its effect on the mechanical properties of conventional plastics and biocomposites are discussed. Different techniques for analysing volatile organic emissions (VOCs) have been summarized and studies highlighting the characterization of VOCs from aged plastics and biocomposites after aging have been cited. [ABSTRACT FROM AUTHOR]

To ensure the safety and durability of concrete structures, timely detection and classification of concrete cracks using a low-cost and high-efficiency method is necessary. In this study, a concrete surface crack damage detection method based on the ResNet-18 residual network was developed. This method was implemented by training a model with images to extract the cracks, where the image processing algorithms and deep learning were combined. The results show that the computational accuracy can meet the requirements by utilizing the established image dataset and appropriate model hyperparameters. The trained model had high recognition accuracy when the 256 × 256 resolution images were adopted, and the worst accuracy of crack recognition in the test set was over 90%. The average accuracy in the test set was 91.3% when considering environmental interference generated by processing the images with a brightness adjustment, salt-and-pepper noise, and localized interference. Then, it was demonstrated that the present model possesses good robustness for crack identification in different environments. The average recognition accuracy when dealing with images of a real bridge, which are outside the training dataset, was 99.7%. The residual network model developed in this study has the advantages of low cost, high efficiency, and practicality compared to traditional detection methods. Compared to the existing deep learning methods, the model created in this study requires less computational resources and storage space, and shows a faster training speed and higher accuracy. [ABSTRACT FROM AUTHOR]

The use of processed limestone sludge as a crosslinking agent for films based on Na–alginate and ɩ-carrageenan/Na-alginate blends was studied. Sorbitol was tested as a plasticizer. The produced gel formulations included alginate/sorbitol and carrageenan/alginate/sorbitol mixtures, with tested sorbitol concentrations of 0.0, 0.5 and 1.0 wt%. The limestone sludge waste obtained from the processing of quarried limestone was converted into an aqueous solution of Ca2+ by dissolution with mineral acid. This solution was then diluted in water and used to induce gel crosslinking. The necessity of using sorbitol as a component of the crosslinking solution was also assessed. The resulting films were characterized regarding their dimensional stability, microstructure, chemical structure, mechanical performance and antifungal properties. Alginate/sorbitol films displayed poor dimensional stability and were deemed not viable. Carrageenan/alginate/sorbitol films exhibited higher dimensional stability and smooth and flat surfaces, especially in compositions with 0.5 wt% sorbitol. However, an increasing amount of plasticizer appears to result in severe surface cracking, the development of a segregation phenomenon affecting carrageenan and an overall decrease in films' mechanical resistance. Although further studies regarding film composition—including plasticizer fraction, film optimal thickness and film/mold material interaction—are mandatory, the attained results show the potential of the reported ɩ-carrageenan/alginate/sorbitol films to be used towards the development of viable films derived from algal polysaccharides. [ABSTRACT FROM AUTHOR]

Ni60-WC coatings with different WC contents on the bucket tooth substrates were pre- pared using laser cladding technology. Their abrasive wear properties were assessed using the dry sand rubber wheel test system. The substrate and the hard-facing layer were tested for comparison. The results showed that the hardness of the Ni60-WC coatings increased with the increase in WC content. The wear resistance of the bucket tooth substrate was greatly improved by hard-facing and laser cladding Ni60-WC coatings. The wear rate of the hard-facing layer was reduced to 1/6 of that of the tooth substrate. The wear rate of the laser cladding coatings with 20–40 wt.% WC was similar to that of the hard-facing layer. It is worth mentioning that the wear rate of the coatings with 60–80 wt.% WC was only 1/4 of that of the hard-facing layer. Micro-cutting with surface plastic deformation was the main wear mechanism of the substrate to form narrow and deep furrows. The wear mechanism of the hard-facing layer was mainly plastic deformation with a wide groove, and the surface cracks promoted the removal of the material. The removal of the binder phase caused by micro-cutting was the main wear mechanism of the laser cladding Ni60-WC coatings. However, the hard phase of WC hinders micro-cutting and plastic deformation, which improves the wear resistance of the coating. [ABSTRACT FROM AUTHOR]

This paper presents a review on the formability evaluation of AHSS, enhancing necking-based failure criteria limitations. Complementary fracture/damage constitutive modeling approaches specifically tailored to formability evaluation, validated through numerical and experimental methods, are also subjects of research. AHSS are widely processed through sheet metal forming processes. Although an excellent choice when lightweight, high-strength, and ductility are critical factors, their multi-phase microstructure accentuates forming challenges. To accurately model forming behavior, necking-based failure criteria as well as direct fracture models require improvements. As a necking-based failure model, the conventional forming limit diagram/curve (FLD/FLC) presents limitations in estimating direct fracture (surface cracks, edge cracks, shear cracks), as well as deformation histories under non-linear strain paths. Thus, significant research efforts are being made towards the development of advanced fracture constitutive models capable of predicting fracture scenarios without necking, which are more frequently observed in the realm of AHSS. Scientific community research is divided into several directions aiming at improving the forming and fracture behavior accuracy of parts subjected to sheet metal forming operations. In this review paper, a comprehensive overview of ductile fracture modeling is presented. Firstly, the limitations of FLD/FLC in modeling fracture behavior in sheet metal forming operations are studied, followed by recent trends in constitutive material modeling. Afterwards, advancements in material characterization methods to cover a broad range of stress states are discussed. Finally, damage and fracture models predicting failure in AHSS are investigated. This review paper supplies relevant information on the current issues the sheet metal forming community is challenged with due to the trend towards AHSS employment in the automotive industry. [ABSTRACT FROM AUTHOR]

In this study, a machine learning-based method to assess and predict the cracking degree (CD) on wood fiber bundles (WFB) was developed, which is crucial for enhancing the quality control and refining the production process of wood scrimber (WS). By roller-cracking poplar wood one to three times, three distinct CD levels were established, and 361 WFB specimens were analyzed, using their water absorption rate (WAR) as the foundation for CD prediction. Through crack image analysis, four key quantitative parameters were identified—cracking density, coherence degree, crack count, and average width—and this study found through discriminant analysis that the discrimination accuracy on the CD levels by cracking density or coherence degree over 90%, emphasizing their significance in evaluation. Cluster analysis grouped the specimens into three clusters based on four key quantitative parameters, aligning with the CD levels. This study developed specialized prediction models for each CD level, integrating principal component analysis for dimensionality reduction with polynomial fitting, achieving mean squared error (MSE) of 0.0132, 0.0498, and 0.0204 for levels 1, 2, and 3, respectively. An integrated model, with an accuracy of 94.3% and predictions within a 20% error margin, was created, demonstrating the effectiveness of using surface crack image features to predict WAR of WFB. This research establishes a methodological framework for assessing CDs on WFB, contributing to enhancing WS product quality and helping to better understand wood cracking and water absorption mechanisms. [ABSTRACT FROM AUTHOR]

While thermal barrier coatings (TBCs) are being sprayed onto aero-engine turbine blades, or while the engine blade is working, high temperatures and strong impact forces will damage TBCs under thermal cycles, resulting in the coating peeling off from the blades. The current method of using ECT, IRT, or another method alone cannot achieve the real-time detection of coating defects with both high precision and high penetration power. Two detection methods, namely, terahertz pulsed imaging (TPI) and optical coherence tomography (OCT), were combined to evaluate typical defects observed in TBCs (including internal debonding cracks, surface high-temperature cracks, and surface etched cracks). The results showed that the OCT system successfully obtained the micron-level axial resolution, but the detection depth of the OCT system was limited. The TPI system achieved a higher penetration depth than OCT—hence, it can be used for the nondestructive detection and evaluation of the internal debonding defects in the sample—but its resolution needs to be improved. Following this conclusion, a method is proposed using TPI and OCT concurrently for the nondestructive testing and quantitative evaluation of TBCs on etched cracks, thus achieving progress both in terms of depth and resolution. In our experiment, defects with a depth of 519 μm and a width of 100 μm were measured. The proposed method is suitable for situations where multiple defects in TBC samples of blades need to be detected simultaneously during the working process. When there are defects deep inside the sample, more small cracks on the surface can be evaluated to achieve a combination of depth and accuracy. [ABSTRACT FROM AUTHOR]

Thermally induced pores (TIPs) are generally the source of fatigue crack initiation in the powder metallurgy (PM) Ni-based FGH96 superalloy. The effect of TIPs on fatigue crack initiation on the surface of the FGH96 superalloy was detected using scanning electron microscopy (SEM). The cause of fatigue crack deflection was studied using electron backscatter diffraction (EBSD) analysis. The results indicated that there are two states of TIPs including isolated TIPs and clustered TIPs located at the grain boundary. The investigation of crack initiation and propagation around TIPs was conducted in detail through the comprehensive integration of experimental findings and computational results. For cracks initiated by isolated TIPs, the maximum equivalent size and the ratio of the vertical–parallel axis to the loading direction of the TIPs reveal a linear relationship, and both of them determine crack initiation. Regarding clustered TIPs, the constituent pores of the clustered TIPs will compete to initiate cracks based on the experimental results, and the largest pore will be more likely to initiate cracking. Moreover, the results showed that fatigue crack propagation can be hindered by hard-orientation grains and twins with a low Schmid factor (SF). Large-angle crack deflection due to twins with a low SF can significantly increase crack length and resistance to crack propagation. [ABSTRACT FROM AUTHOR]

Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. Firstly, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Secondly, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimized from a Smooth L1 Loss to IoU Loss to lessen its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7 Hz, increases the GPU memory by 62 MB, increases the FLOPs by 3.81 times per second, and increases the AP by 15.4% compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness. [ABSTRACT FROM AUTHOR]

Surface cracks and joint deteriorations are typical premature failures of urban cement concrete pavement. However, traffic loads on the urban pavement are much lower than those on highways. Limited research has been conducted to investigate the causes of accelerated damage in urban cement concrete roads. To investigate the foundation issues that may cause the accelerated damage of urban cement concrete pavements, in this study, field evaluations were conducted to assess pavement foundation support and drainage conditions. Field visual inspections, Ground Penetrating Radar (GPR) survey, Dynamic Cone Penetrometer (DCP) test, and the Core-Hole Permeameter (CHP) test were performed. In urban residential areas with inadequate subgrade bearing capacity, cement concrete pavements are prone to early damage. Foundations with a higher content of coarse particles exhibit a higher CBR value, which can extend the service life of the pavement. The compaction of foundation materials near sewer pipelines and manholes is insufficient, leading to non-uniform support conditions. Moreover, the permeability of the foundation material can influence the service life of pavement surface structures. Foundation materials with fewer fine particles enhance drainage performance, contributing to a longer service life for PCC pavements. In areas with inadequate drainage, water accumulation reduces the bearing capacity of the foundation, thereby accelerating pavement deterioration. The poor bearing capacity and drainage conditions of the foundation lead to cavities between the surface layer and foundation material thus yielding stress concentrations on the pavement surface, which cause the formation of pavement surface cracks. [ABSTRACT FROM AUTHOR]

This study aims to enhance pavement crack detection methods by integrating unmanned aerial vehicles (UAVs) with deep learning techniques. Current methods encounter challenges such as low accuracy, limited efficiency, and constrained application scenarios. We introduce an innovative approach that employs a UAV equipped with a binocular camera for identifying pavement surface cracks. This method is augmented by a binocular ranging algorithm combined with edge detection and skeleton extraction algorithms, enabling the quantification of crack widths without necessitating a preset shooting distance—a notable limitation in existing UAV crack detection applications. We developed an optimized model to enhance detection accuracy, incorporating the YOLOv5s network with an Efficient Channel Attention (ECA) mechanism. This model features a decoupled head structure, replacing the original coupled head structure to optimize detection performance, and utilizes a Generalized Intersection over Union (GIoU) loss function for refined bounding box predictions. Post identification, images within the bounding boxes are segmented by the Unet++ network to accurately quantify cracks. The efficacy of the proposed method was validated on roads in complex environments, achieving a mean Average Precision (mAP) of 86.32% for crack identification and localization with the improved model. This represents a 5.30% increase in the mAP and a 6.25% increase in recall compared to the baseline network. Quantitative results indicate that the measurement error margin for crack widths was 10%, fulfilling the practical requirements for pavement crack quantification. [ABSTRACT FROM AUTHOR]

The specific lithology of the southern part of Bejaia city represents a major limitation to urban settlement and expansion. This is partly due to landslides that tend to affect this region. To date, one of these landslides in this region has occurred in the Targa Ouzemour area, where the damage extended approximately six hectares. The main purpose of this study is to identify the failure surfaces characterizing the internal structure of this landslide as well as the significant influence of groundwater on slope instability, which manifests as surface cracking and subsidence. We have combined several geotechnical and geophysical methods, including field observations. The exploitation of the collected geotechnical data from the six (06) boreholes drilled in the landslide zone has allowed for knowledge to be gained on the lithological components, as well as the characterizations of physical and mechanical properties on a range of different types of affected rocks, whereas electrical resistivity tomography (ERT) data allowed an in-depth examination, leading us to reconstruct the landslide geometry and particularly to evaluate the hydrological characteristics of the studied site. Moreover, the resistivity contrast patterns provided more clarity to discern between the various lithological formations that are still stable or actively moving within this landslide. All these findings have contributed to the construction of a characteristic geomodel that highlights the failure surfaces over which displacement is still experienced. Finally, with the evidence of rainfall effects on the deformation and stability of the slope, specific landslide remedial measures were accordingly suggested. [ABSTRACT FROM AUTHOR]

The paper analyzes the occurrence of evenly spaced cracks on the surface of lamellar specimens of Fe-28Mn-6Si-5Cr (mass %) shape-memory alloy (SMA), during cold rolling. The specimens were hot rolled and normalized and developed cold rolling cracks with an approximate spacing of about 1.3 mm and a depth that increased with the thickness-reduction degree. At normalized specimens, X-ray diffraction patterns revealed the presence of multiple crystallographic variants of brittle α′ body-bcc martensite, which could be the cause of cold-rolling cracking. Both normalized and cold-rolled specimens were analyzed using scanning electron microscopy SEM. SEM micrographs revealed the presence of several crystallographic variants of α′-body-centered cubic (bcc) and ε hexagonal close-packed (hcp) martensite plates within a γ-face-centered cubic (fcc) austenite matrix in a normalized state. High-resolution SEM, recorded after 25% thickness reduction by cold-rolling, emphasized the ductile character of the cracks by means of an array of multiple dimples. After additional 33% cold-rolling thickness reduction, the surface of crack walls became acicular, thus revealing the fragile character of failure. It has been argued that the specimens cracked in the neutral point but preserved their integrity owing to the ductile character of γ-fcc austenite matrix. [ABSTRACT FROM AUTHOR]

This article investigates the microscopic mechanism of crack initiation and propagation in three-dimensional embedded cracks in brittle materials containing circular holes. First, a method for the development of transparent, brittle materials is proposed. Second, UCS tests were conducted on transparent, brittle materials containing circular holes and internally embedded three-dimensional cracks. Finally, a numerical model was established in PFC3D to analyze the crack initiation and propagation mechanism. The results show that when α = 0° (α refers to the pre-existing crack inclination), the upper tip of the pre-existing crack appears as a tensile wing crack, and the lower tip of the pre-existing crack appears as a tensile–shear mixed crack. When α = 30°, no wing crack appears, and the tensile crack on the fracture surface only appears after the hole cracks. When α = 60 and 90°, a tensile wing crack and an anti-wing tensile–shear mixed crack appear at the upper tip of the pre-existing crack. A tensile wing crack appears at the lower tip of the pre-existing crack and appears "self-limiting". During the propagation of wing cracks to the surface of the specimen, the transition sequence of the crack propagation mechanism is tensile through failure—tension-shear mixed failure—tensile failure. It can be seen that the interaction between the crack and hole has an important influence on the evolution mechanism of the crack and the failure mode of the specimen. [ABSTRACT FROM AUTHOR]

Concrete surface cracks serve as early indicators of potential structural threats. Visual inspection, a commonly used and versatile concrete condition assessment technique, is employed to assess concrete degradation by observing signs of damage on the surface level. However, the method tends to be qualitative and needs to be more comprehensive in providing accurate information regarding the extent of damage and its evolution, notwithstanding its time-consuming and environment-sensitive nature. As such, the integration of image analysis techniques with artificial intelligence (AI) has been increasingly proven efficient as a tool to capture damage signs on concrete surfaces. However, to improve the performance of automated crack detection, it is imperative to intensively train a machine learning model, and questions remain regarding the required image quality and image collection methodology needed to ensure the model's accuracy and reliability in damage quantitative analysis. This study aims to establish a procedure for image acquisition and processing through the application of an image-based measurement approach to explore the capabilities of concrete surface damage diagnosis. Digitizing crack intensity measurements were found to be feasible; however, larger datasets are required. Due to the anisotropic behavior of the damage, the model's ability to capture crack directionality was developed, presenting no statistically significant differences between the observed and predicted values used in this study with correlation coefficients of 0.79 and 0.82. [ABSTRACT FROM AUTHOR]

Cracks inside urban underground comprehensive pipe galleries are small and their characteristics are not obvious. Due to low lighting and large shadow areas, the differentiation between the cracks and background in an image is low. Most current semantic segmentation methods focus on overall segmentation and have a large perceptual range. However, for urban underground comprehensive pipe gallery crack segmentation tasks, it is difficult to pay attention to the detailed features of local edges to obtain accurate segmentation results. A Global Attention Segmentation Network (GA-SegNet) is proposed in this paper. The GA-SegNet is designed to perform semantic segmentation by incorporating global attention mechanisms. In order to perform precise pixel classification in the image, a residual separable convolution attention model is employed in an encoder to extract features at multiple scales. A global attention upsample model (GAM) is utilized in a decoder to enhance the connection between shallow-level features and deep abstract features, which could increase the attention of the network towards small cracks. By employing a balanced loss function, the contribution of crack pixels is increased while reducing the focus on background pixels in the overall loss. This approach aims to improve the segmentation accuracy of cracks. The comparative experimental results with other classic models show that the GA SegNet model proposed in this study has better segmentation performance and multiple evaluation indicators, and has advantages in segmentation accuracy and efficiency. [ABSTRACT FROM AUTHOR]

This study developed advanced ceramic materials with both healing and decomposition functions using a metastable product generated under superheated steam. The developed composite material comprises ZrC particles dispersed in a yttria-stabilized zirconia (YSZ) matrix. After introducing a surface crack of approximately 120 μm on the composite specimen, it showed a complete strength recovery rate after one hour of heat treatment under superheated steam at 400 °C, while it exhibited a decomposition behavior after one hour of heat treatment in air at 400 °C. The XRD analysis of the heat-treated specimens showed that the final product was monoclinic ZrO2 under both steam and air conditions. In other words, full strength recovery in superheated steam was achieved by a chain reaction involving metastable intermediate products derived from H2O, unlike the reaction in air. [ABSTRACT FROM AUTHOR]

This paper presents an investigation on the forming characteristics of the tailor rolled blank of an aluminum alloy (Al-TRB) during three-point bending at room temperature through experiments and finite element simulations. The strain distribution, spring-back characteristics, and metal flow law of 6000 series Al-TRB during three-point bending are explored. The prepared Al-TRB has good bending properties, and no surface cracks appear in the bending region of the Al-TRB when bent to 180°. Surface roughening occurs on the outside of the bending region. Since the strain in the thick zone is greater than that in the thin zone, the surface roughening in the thick zone is more obvious than that in the thin zone. The spring-back angle in the thin zone is higher than that in the thick zone after three-point bending, and the overall spring-back angle of Al-TRB becomes larger with an increasing bending angle. When the transition zone of Al-TRB is centered and the length of the transition zone is certain, as the length of the equal-thickness zone increases, the spring-back angle of the thin zone is larger, while the spring-back angle of the thick zone is smaller. Under the premise of a certain total length of Al-TRB and the length of the transition zone, the larger the length proportion of the thin zone, the larger the overall spring-back angle of Al-TRB, and the larger the length proportion of the thick zone, the smaller the overall spring-back angle of Al-TRB. In addition, a slight metal flow phenomenon exists during three-point bending, which shows that the metal in the bending region will flow to the thick zone, and the metal at the edge will flow to the thin zone. At the same time, there are localized thickening and thinning phenomena in Al-TRB. This study is helpful because it provides theoretical guidance for designing molds for the actual production of Al-TRB parts for automotives. [ABSTRACT FROM AUTHOR]

White etching layers (WELs) and brown etching layers (BELs), formed on the upper layer of bearing raceways generally lead to surface crack initiation and propagation and significantly affect the stable operation of precision bearings. In this study, the microstructure features of WELs and BELs from two failure bearings have been characterized and analyzed. The BEL mainly consists of quenched martensite with higher hardness values. The bainite simultaneously exists at the boundary of the BEL/matrix. Owing to the relative roll and slip, temperature increases and rapid decreases are the main formation mechanisms of the BEL in this study (thermal-induced effect). The WEL can be only found on one sample, and elongated and coarse grains are found in this region. It can be speculated that the WEL may originate from the surface region temperature increasing again and then slowly decreasing. Cracks can initiate at the boundaries of the WEL/BEL/matrix. The crack propagation behaviors are significantly affected by the properties of the WEL and BEL microstructure. It is difficult for the cracks to propagate from a softer WEL into a harder BEL. Thus, the depth of cracks in the WEL is shallower. However, because of the brittle nature of quenched martensite in the BEL, cracks can easily propagate downward under contact stress. Thus, the depth of cracks can exceed 100 μm easily. The formation mechanisms of the WEL/BEL and crack propagation behaviors have been further proved and discussed in this study. [ABSTRACT FROM AUTHOR]

The rail industry is constantly facing challenges related to safety with regard to the detection of surface cracks and internal defects within rail tracks. Significant focus has been placed on developing sensor technologies that would facilitate the detection of flaws that compromise rail safety. In parallel, robot automation has demonstrated significant advancements in the integration of sensor technologies within end effectors. This study investigates the novel integration of an ultrasonic sensor within a robotic platform specifically for the application of detecting surface cracks and internal defects within rail tracks. The performance of the robotic sensor system was assessed on a rail track specimen containing sacrificial surface cracks and internal defects and then compared against a manual detection system. The investigation concludes that the robotic sensor system successfully identified internal defects in the web region of the rail track when utilising a 60° and 70° wedged probe, with a frequency range between 4 MHz and 5 MHz. However, the surface crack investigation proved that the transducer was insensitive to the detection of cracks, possibly due to the inadequate angle of the wedged probe. The overall outcome of the study highlights the potential that robotic sensor systems have in the detection of internal defects and characterises the limitations of surface crack identification to assist in enhancing rail safety. [ABSTRACT FROM AUTHOR]

In applications of piezoelectric actuators and sensors, the dependability and particularly the reliability throughout their lifetime are vital to manufacturers and end-users and are enabled through condition-monitoring approaches. Existing approaches often utilize impedance measurements over a range of frequencies or velocity measurements and require additional equipment or sensors, such as a laser Doppler vibrometer. Furthermore, the non-negligible effects of varying operating conditions are often unconsidered. To minimize the need for additional sensors while maintaining the dependability of piezoelectric bending actuators irrespective of varying operating conditions, an online diagnostics approach is proposed. To this end, time- and frequency-domain features are extracted from monitored current signals to reflect hairline crack development in bending actuators. For validation of applicability, the presented analysis method was evaluated on piezoelectric bending actuators subjected to accelerated lifetime tests at varying voltage amplitudes and under external damping conditions. In the presence of a crack and due to a diminished stiffness, the resonance frequency decreases and the root-mean-square amplitude of the current signal simultaneously abruptly drops during the lifetime tests. Furthermore, the piezoelectric crack surfaces clapping is reflected in higher harmonics of the current signal. Thus, time-domain features and harmonics of the current signals are sufficient to diagnose hairline cracks in the actuators. [ABSTRACT FROM AUTHOR]

Type II inorganic clathrates consist of cage-like structures with open frameworks, and they are considered promising materials due to their unique properties. However, the difficulty of synthesizing phase-pure and continuous films has hindered their application in practical devices. In this report, we demonstrate the synthesis of type II SiGe clathrate films through the thermal decomposition of a Na-deposited amorphous SiGe film on a sapphire substrate in a high vacuum. The as-prepared films of type II SiGe clathrates showed uniform growth and were evaluated for their structural and optical properties. Morphological studies conducted using a scanning electron microscope showed the presence of cracks on the film surface. [ABSTRACT FROM AUTHOR]

This study explores the comparative fatigue performance of decarburized surfaces in railway components, emphasizing rolling contact fatigue, crack propagation, and acoustic emission. The investigation entails the examination of two grades of railway steels, namely R260 and U71Mn, to analyze crack and surface characteristics subsequent to fatigue testing employing a Twin Roller Machine. The purpose is to discern the impact of decarburization on the fatigue life of these materials. The results reveal distinct patterns in crack propagation and acoustic emission between decarburized and non-decarburized surfaces, providing valuable insights into the fatigue behavior of railway components. This comparative analysis contributes to a nuanced understanding of the material's response to cyclic loading. [ABSTRACT FROM AUTHOR]