Types Of Cracks Research Articles

Crack and failure behaviors of sandstone subjected to dynamic loads visualized by micro-computed tomography

Microscopic dynamic failure behaviors of rocks are significant to rock engineering, which is still insufficiently understood. This study combines split Hopkinson pressure bar (SHPB) and micro-CT (computed tomography) to explore the microscopic failure characteristics of sandstone under impact loading. SHPB is responsible for the dynamic test, and micro-CT is responsible for pre- and post-test inspections. The results show that the pores and defect influence the dynamic strength but do not alter the overall trend of increased strength with a higher impact level. The dynamical crack development is then analyzed. Three types of cracks (i.e. I-, Y-, and H-type) are identified to describe the crack development. When rock is simply fractured, only I-type crack exists due to tensile failure, and it grows irregularly. As the strain rate increases, I-type crack is transformed into Y- and H-type crack due to shear failure. Crack coalesces at that moment, and the complexity increases along the impact direction. The coalescence occurs preferentially in the area with more pores, and around a third of pores are involved, where the maximum contribution area is in the middle of sample. Microcracks are formed inside the rock blocks, and rock grains and fragments fill in the cracks. The dynamic crack development is accompanied by microcracks, while rock grains and fragments result from the development of these microcracks. In addition, the influence of a semi-penetrating defect perpendicular to the impact direction is investigated. The defect can impede stress transfer and concentrate energy consumption. The findings are expected to enhance understanding of rock dynamics and support rock engineering development.

Classifying flexible pavement defects using hybrid machine learning approach

The transportation infrastructure sector significantly impacts a country’s gross domestic product (GDP), particularly in developing nations striving to manage and maintain road networks as valuable assets. While asset generation is integral, the more intricate challenge lies in effective maintenance. Pavement monitoring, a crucial component of pavement maintenance and management systems (PMMS), evaluates defect severity, road maintenance prioritization, and maintenance types. To enhance road health monitoring, the present study introduces a hybrid machine learning (ML) method, integrating support vector machine (SVM) and convolutional neural network (CNN). The proposed semi-automated detection system aims to reduce human supervision in traditional surveys, thereby cutting down the cost of pavement distress maintenance The research utilizes data collected by the authors from Ahmedabad city, Gujarat, following Indian road congress (IRC) guidelines for defect selection. Training involves 1,000 images for each crack type, with testing on 100 images. Results indicate that the SVM-CNN model achieves 87% accuracy in training and 91% accuracy in testing for road defect classification, showcasing its efficiency in pavement maintenance and management. The system presents the potential to significantly enhance the efficiency of road maintenance processes, making it a valuable asset for developing nations striving for a more streamlined approach to road network preservation.

Review of Pavement Crack Types and Their Significance

This essay examines the significant importance of pavement distress detection, specifically in the capacity to identify and categorize various forms of cracks, in order to uphold road safety. This study explores the impact of technical breakthroughs on the detection and classification of pavement cracks, leading to notable improvements in road maintenance and accident prevention. The essay additionally explores a range of studies that indicate a connection between pavement conditions and traffic safety, including the association between pavement age and the likelihood of accidents, as well as the influence on the severity of crashes. This analysis serves to illustrate the paramount significance of maintaining pavement integrity in order to ensure traffic safety. The essay foresees future progress in crack detection techniques, highlighting their crucial role in the construction of safer road networks and the improvement of public safety.

Modulation of Photoinduced Phase Segregation and Stress-Driven Nanoscale Cracking in Hybrid Halide Perovskite Solar Cells.

The negative effect of photoinduced halide segregation (PIHS) on the properties of hybrid halide perovskites poses a major obstacle for its future commercial application. Therefore, the in-depth understanding of halide-ion segregation and its causes is an urgent and intractable problem. When PIHS reaches a certain threshold, it will aggravate the deterioration of the film surface morphology and form nanoscale cracks. Herein, the formation mechanism and types of cracks are revealed by exploring the stress distribution in the film. Using the femtosecond time-resolved transient absorption spectroscopy, the ultrafast formation of the iodine rich phase is observed, which appears earlier than the bromine rich phase. In addition, the introduction of organic ligand didodecyldimethylammonium bromide can significantly inhibit PIHS and improve the surface morphology of the film, which can promote the device efficiency from 9.63 to 11.20%. This work provides a novel perspective for the exploration of the PIHS.

Impact of gas diffusion layer architecture on the performances of Cu-based electrodes for CO2 electroreduction to ethylene in flow reactors

Electrocatalytic CO2 reduction reaction (CO2RR) is a promising strategy to convert CO2 into fuels and building blocks for chemicals, exploiting renewable energy sources. Flow cells using gas diffusion electrodes (GDEs) instead of simple H-cells are required to achieve higher production rates. In a GDE, CO2 diffuses through a porous gas diffusion layer (GDL) and reaches the active sites located in the catalyst layer; the GDL is a crucial component that can affect the performance of the GDE. In this work, we aimed to establish the parameters of the GDL that influence the selectivity toward the different CO2 reduction products in an alkaline flow reactor. We employed commercial Cu nanoparticles as electrocatalyst and selected a range of market-available GDLs: the structure, composition, and type of cracks of the GDL greatly influence the Faradaic Efficiency (FE) of CO, H2, and ethylene (C2H4). With the emphasis on C2H4, it was found that using a crack-free GDE is essential to get higher FEC2H4. At the same time, larger and more abundant cracks are detrimental to C2H4 production and promote hydrogen evolution: for instance, the FEC2H4 varies in the wide range of 13–32%, from the highly cracked to the crack-free GDL. The presence of both sulfur and oxygen in the microporous layer (MPL) of the GDL helps shift the CO2RR selectivity toward CO. The (in)stability of the GDEs at high current density has been correlated with a change in the wetting properties of the GDE and a loss in hydrophobicity.

Three-dimensional numerical simulation on failure mechanical characteristics of fissured sandstone specimens under true triaxial conditions

Understanding intermediate principal stress influences on deformation characteristics and failure mechanism of fractured rock from deep engineering often necessitates long term on-site detection and complex experimental research, although very limited efficacy can be obtained. With presently available experimental data on conventional triaxial cuboid noncoplanar fissured sandstones, this study further investigated the effects of differential stress (σ2 − σ3) and rock bridge inclination angle (β30°, β60°, β90°, β120°, and β150°) on their damage evolution processes and failure modes based on 3D discrete element methods (PFC3D). Numerical simulation results indicated that the peak strength of the fissured sandstone under the same state of horizontal principal stress shows an overall increasing pattern with the increase of the inclination angle of the rock bridge, while its peak strain displays a decreasing and then increasing trend. Besides, in this work, five failure modes, nine crack types and two kinds of stress–strain curves are summarized which are significantly affected by the horizontal principal stress, rock bridge inclinations or cooperative interaction. Differential stress value of 15 MPa is an important threshold for determining the residual strength of the stress–strain curve from present to absent. The cracking and force chain evolution processes of five failure modes were analyzed in order to discover the corresponding failure mechanisms. Finally, differential stress and flaw inclination influences on rock mechanical parameters and failure behavior have significant implications for strategies of the design, operation, and maintenance of rock engineering under severe conditions in the underground.

AI-based technology to prognose and diagnose complex crack characteristics of railway concrete sleepers

Railway concrete sleepers are key safety-critical components in ballasted railway tracks. Due to frequent high-intensity impact loadings from train-track interaction over irregularities together with hostile environmental conditions, complicated characteristics of various crack patterns can incur on railway concrete sleepers, which will decrease their durability and service life overtime. Early warning of those cracks can help railway engineers to plan and schedule for renewal and maintenance timely and effectively. This study thus explores the artificial intelligence application of YOLOv5OBB (YOLOv5 with Oriented Bounding Box output) in the identification and classification of cracks in railway sleepers into three distinct types: longitudinal, transverse, and inclined, based on their specific crack angles, which have not been investigated in the past. The identification of crack angles is the novelty of this study. Recognising the various types of cracks is critical, given their varying causes and degrees of severity. Current corrective maintenance methods pose considerable safety risks to workers and exhibit low efficiency, underscoring the need for a more autonomous and efficient solution. This study marks a significant stride towards revolutionising railway maintenance, evidenced by an impressive mAP (Mean Average Precision) of 0.72 for crack detection and a 92% accuracy rate for angle detection. These promising results substantiate our study's potential to pioneer advancements in railway infrastructure maintenance.

Fatigue crack growth of WC–Co cemented carbides: a comparative study using small indentation flaws and long through-thickness cracks

The fatigue crack growth behavior of a submicron-grained WC–Co hardmetal is investigated by artificially introducing small flaws by means of sharp indentation. Similar fatigue testing is also conducted on notched specimens with long through-thickness cracks for comparison purposes. The use of controlled small indentations flaws is shown to be a valid and successful approach for studying and describing crack growth behavior under cyclic loading for the material under consideration. This statement is based on the similitude found in fatigue mechanics and mechanisms between both crack types. Regarding the former, accounting of the indentation-induced residual stresses is key to rationalize the experimental findings. Concerning the latter, inspection of crack-microstructure interaction as well as fracture surfaces permit to discern similar features and scenarios, at both meso- and micrometric length scales. Results from this research yield an immediate practical implication, as indentation techniques may then be proposed as an alternative testing route for investigating fatigue crack growth behavior of hardmetal grades where sharp indentation is capable to induce well-developed radial crack systems.

A CNN-Based Crack Analysis Model for Metal Specimens

In the field of engineering, the combination of deep learning and traditional simulation has become a research hotspot. In healthy structure monitoring problems, strain sensors are spaced and spatially arranged to collect strains. However, methods such as strain gauges can only capture single-point strains, and in many cases these more discrete strain information is insufficient to determine the damage of the target. In this paper, we combine the strain field with deep learning methods to propose a novel damage detection method based on strain image inversion to perform crack information of workpieces. The method defines the type and location of cracks on the specimen as labels, a large number of simulated strain images as datasets, and uses CNN networks to train the model. The results show that the obtained crack inversion model for metal specimens can achieve 93.99% accuracy.

Classification of Solar Cell Cracks Using Deep Learning

This study evaluates the efficacy of a Deep Learning model in classifying solar cell images with and without cracks, crucial for early detection and maintenance of photovoltaic systems. The model demonstrates high overall accuracy (94%) and sensitivity (91%), indicating its proficiency in recognizing images with cracks while minimizing false positives. Receiver Operating Characteristic (ROC) analysis supports the model's robust discrimination between positive and negative cases, with an Area Under the Curve (AUC) of 0.93. Despite promising results, opportunities for improvement include dataset expansion to encompass diverse solar cell conditions and types of cracks. Real-world deployment considerations, such as integration into automated monitoring systems, pose challenges requiring further research. The study underscores the importance of ongoing development to enhance model performance for practical application in solar cell inspection and maintenance.

Iron ore grade's impact on uniaxial compression behavior and acoustic emission characteristics

The mechanical properties of iron ore are influenced by various factors, including its grade, which consequently impacts stope stability and equipment efficiency in the mining process. In order to explore the influence and mechanism of grade on these properties, handheld XRF testing was conducted to determine the grade of iron ore samples ranging from 28% to 40%. Subsequently, the acoustic emission method was employed to acquire signals during the uniaxial compression tests. Additionally, scanning electron microscopy was utilized to examine the fracture surface, for performing a microscopic analysis of the influence mechanism of grade on the mechanical properties of iron ore. The results revealed that lower-grade iron ore exhibited larger transverse deformation, with a corresponding dominance of tensile failure. This observation aligned with the AF-RA value (Parameters of reaction crack type), which depicted a shift in the types of microcracks generated during the compression process from tensile cracks to shear cracks with the increasing grade. The content of gangue minerals diminishing as the grade increased. Consequently, the location of the cracks transitioned from the primary failure Through the gangue minerals to the primary microcracks in metallic minerals, resulting in a higher occurrence of acoustic emission events during the early loading stage and the development speed of microcracks accelerates during the loading process. The dominant frequency (Parameters of reaction crack size) of the acoustic emission signal increased from 150 kHz at a grade of 28% to 264 kHz at a grade of 40%, accompanied by a linear decrease in the proportion of low-frequency signals. The b value (indicator for quantifying the degree of damage) demonstrates a decrease with increasing grade. Overall, the content of metal minerals in the iron ore increased with the grade, resulting in polymerization. However, the influence of crack propagation on the overall strength of iron ore remained relatively minor. Macroscopically, the uniaxial compressive strength increased almost linearly with the iron ore grade.

Hypergravity prompt thermal crack in 1060 aluminium slat

High-speed rotation instruments that apply gradient hypergravities can induce local variations in structure and thus induce cracking. Rotation-induced hypergravity is a kind of gradient force, and it has a gradient value that depends on distance and local weight. Conventional mechanical testing applies a constant force or periodic force instead of true gradient hypergravity to rotating components. In this study, a dedicated instrument was designed and assembled, and a special sample of 1060 aluminium slats with multiple thinned necks was used to reveal the difference between gradient hypergravity and conventional constant forces. Hypergravity caused more significant damage to the aluminium slat, and the ultimate crack strength was approximately 10% of the strength observed under the constant force applied at the same temperature. All the samples cracked on the inner neck which bears the highest hypergravity. Two types of cracks were categorized based on the relative extension: type Ⅰ mode cracks occurred in the centre of the neck with long extension before cracking under hypergravity more than 3.5 MPa, while type Ⅱ cracks occurred in the root of the neck without long extension before cracking under hypergravity less than 3.5 MPa. Combination of hypergravity and torsional force in high-speed rotation system prompt cracking and exhibit similar microstructure with that under high constant force. Typical cracking and its microstructure under the effects of hypergravity and temperature provided a convenient and practicable method for evaluating service safety of commercial rotating machines for industrial applications and raw alloys.

Comparative Study of Numerical Methods for Predicting Crack Propagation in Reinforced Concrete Hollow Core Slabs

Hollow core slabs are commonly used in prefabricated building construction and are calculated and constructed using gravity loads. The dead loads of the structure are reduced by using this slab system. Different criteria, such as the initiation and propagation of cracks in the hollow core slab, are used to analyze these slabs. Fracture mechanics is the basis for studying crack propagation. The present study was conducted to analyze the propagation of cracks in hollow core slabs under common loading conditions using the finite element method and numerical modeling to analyze cracks in reinforced concrete members. This research is theoretically conducted using finite element software. This research takes into account the potential varieties of cracks in concrete slabs. Slabs should consider all three types of cracks, which are shear, flexural, and flexural-shear cracks obtained from the reference test. Two Contour integral and XFEM methods are used to analyze cracks in Abaqus software. To validate and control the modeling process, the laboratory results of the research of Ibrahim N. Najma, Raid A. Daudb, and Adel A. Al-Azzawi. December 2, 2019, has been used. The outcomes of this study showed that the probability of the formation of a flexural-shear crack in the slab is higher than in other crack forms.

The mode I fracture toughness and acoustic emission characteristics of hot dry rock under temperature effects

The application of liquid nitrogen fracturing technology exhibits promising potential in the exploitation of hot dry rock (HDR). Therefore, studying the variation in granite's fracture characteristics under rapid cooling with liquid nitrogen is highly significant. In this study, the mode I fracture characteristics and acoustic emission (AE) characteristics of thermally treated granite under liquid nitrogen cooling were investigated experimentally. The results indicate that as the temperature increases, both the natural cooling group and liquid nitrogen cooling group exhibit a gradual decrease in granite's fracture toughness, and the cooling effect of liquid nitrogen will further damage the granite. When heated from 25 °C to 600 °C, the natural cooling fracture toughness decreased from 1.089 MPa·m1/2 to 0.1631 MPa·m1/2, with a reduction of 85.0 %; the liquid nitrogen cooling fracture toughness decreased from 1.089 MPa·m1/2 to 0.1493 MPa·m1/2, with a reduction of 86.3 %. Additionally, both the peak load and fracture energy are reduced; intragranular fracture behavior is observed within the granite, resulting in a rougher fracture surface. The integration of a crack classification method based on the rise angle (RA) and average frequency (AF) of AE parameters with the kernel density estimation (KDE) method effectively validates the fracture mechanism of semi-circular bend (SCB) specimens. Meanwhile, based on the AE results during granite fracturing, a method is proposed to discriminate crack types using RA and AF thresholds.

Evaluation of Cherts in Gumushane Province in Terms of Alkali Silica Reaction

Alkali–silica reaction (ASR) occurs when alkali oxides coming from the cement composition in concrete come together with reactive silica and moisture coming from the aggregate. Additional maintenance and repair costs caused by the development of ASR in concrete cause the cost to increase. However, thanks to the measures taken against ASR in the early period, it contributes to the creation of sustainable and durable concrete structures. The article investigated the usability of cherts with eight different chemical compositions as aggregates in ready-mixed concrete plants in Gümüşhane. Cherts have been investigated for alkaline reactivity and are intended to be used largely as a source for concrete plants. ASR length changes of the samples prepared according to ASTM C1260 standard were determined after 3, 7, 14, and 28 days of the curing period. Microstructural examination, ultrasonic P-wave velocity, and bending and compressive strength experiments supporting ASR were carried out. The obtained test results were compared between the reference (limestone) sample and each other, as well as with the values specified in the standards. The compressive and bending strength values of the samples increase depending on their ASR. It was observed that the crack structures and types increased depending on the increase in the crack values.

Crack pattern identification in cementitious materials based on acoustic emission and machine learning

The cracking patterns in cementitious materials before failure are closely related to acoustic emission (AE) monitoring signals. Traditional rise angle-average frequency analysis methods rely on empirical judgment for boundary line determination, lacking effective automated recognition methods to distinguish between tensile, shear, and mixed crack types. This paper presents an unsupervised crack-type identification model based on ensembled clustering with support vector machine (SVM) and a supervised crack-type classification model using graph neural networks (GNN). The study achieves automatic annotation of four crack signal patterns by ensembling the results of k-means++ and GMMSEQ clustering and combining them with SVM. Subsequently, with labeled data obtained, the study encodes AE signals into graphs and uses a GNN for automatic crack-type classification. The effectiveness of these methods is validated on AE test data from cementitious materials. The findings show that the mutation values of AE event interval functions can accurately pinpoint the material fracture moment, and the AE spectrum’s mid-frequency band is associated with cement–river sand interface damage. Different compositions and loading methods in cementitious materials complicate the association between signals and damage. The ensembled clustering and SVM methods can automatically distinguish different types of cracks and provide quantifiable boundary lines. With horizontal visibility graph (HVG) encoding, the supervised GraphSAGE model accurately identifies various crack types, achieving an average accuracy of up to 97.5% on the test dataset. The proposed AE time–frequency domain characterization method offers new analytical tools for understanding the damage evolution process in cementitious materials, promoting the application of machine learning in the recognition of AE signal patterns.

SS-GAN based road surface crack region segmentation and forecasting

Automatic segmentation of crack regions is the major and complex task in an intelligent transportation system. In this proposed work, we have developed a Semantic Segmentation based Generative Adversarial Network model (SS-GAN). Various conditional factors such as the number and type of vehicles traveled during the months and environmental conditions like temperature, and precipitation will be additionally fed into the proposed SS-GAN model to segment and forecast the respective months’ road crack images. In-order to forecast similar crack images of appropriate months with better performance, we have introduced new loss functions additionally. A semantic segmentation model is developed to extract the required semantic features of the crack regions which helps to segment road crack images more accurately. Finally, the proposed model concatenates the extracted features of both discriminator and semantic segmentation models to classify types of cracks with better performance. SS-GAN model is trained using the Crack ForeCast (CFC) dataset in an end-to-end and paired translation model. A comprehensive analysis of experimental results is carried out to measure the performance of the proposed SS-GAN model. Various metrics are used to evaluate the quality of semantic feature-based segmentation for the forecasted crack images. The obtained results prove that the SS-GAN model showcases better performance than other state-of-the-art models.

Effects of fissure length and angle on the fracture modes of 3D printed teeth model: Insights from DIC-based fracture tests and meshless numerical simulations

To investigate the influences of teeth fissure properties on their failure modes, 3D Printing technology is used to prepare the teeth models. The strain distributions of the teeth model surfaces at each moment of the loading processes are obtained by the DIC technique. And the progressive failure processes as well as the stress distributions of the teeth models are simulated by the improved Smoothed Particle Hydrodynamics (SPH) Method. Experimental results show that under the action of the steel ball, the teeth models mainly produce two types of cracks: The tensile cracks along the pre-existing fissures and the shear cracks along both sides of the teeth model. The existence of prefabricated fissures greatly reduces the peak strength of the teeth models. Compared with the circumstances containing no pre-existing fissures, the peak strength of d = 1 cm, d = 2 cm and d = 3 cm decreases by 22.33%, 31.79% and 18.94%, respectively, and the peak strength of θ = 30°, θ = 45°, θ = 60° decreases by 10.78%, 44.01% and 34.3%, respectively. Numerical results show that the initiations of tensile cracks are induced by the high tensile stress concentrations at the pre-existing fissure tips, while the shear cracks are caused by the high tensile stress concentrations in the low tensile stress concentration areas after the initiation of tensile cracks. The research results can provide some references for the understandings of teeth failure mechanisms as well as the applications of SPH method into teeth crack propagation simulations.

Crack _ PSTU: Crack detection based on the U-Net framework combined with Swin Transformer

Cracks serve as a significant indicator of aging infrastructure. While the surfaces of traditional infrastructures are visually inspected manually, this approach is labor-intensive, highly subjective, and often surpasses the capabilities of available inspection personnel. Although some researchers have employed traditional image processing and machine learning techniques to address these challenges, the presence of irregular crack shapes, complex lighting conditions, and limitations in detecting only a single type of surface crack complicate automatic crack detection. Recent research indicates that deep learning methods are increasingly leading in image-based feature extraction, object detection, and pixel attribute analysis. This paper introduces a method, Crack_PSTU (Pre-trained Swin Transformer U-Net), which leverages the U-Net framework and Swin Transformer model for infrastructure crack classification and detection tasks. Specifically, we utilize a pre-trained Swin Transformer network as the encoder and implement the decoder using convolution, pooling, and other operations, creating a "U"-shaped model architecture. Our experiments encompassed 11,298 crack images from varied scenes, with 9603 designated for training and 1695 for validation. The findings reveal that our approach surpasses other algorithms for this dataset.

Study on mechanical properties and acoustic emission characteristics of mudstone-clay composites under uniaxial compression

In the western mining area of China, mudstone-clay composite roofs are commonly found in coal seam roadway. This kind of roadway surrounding rock has poor self-stabilization ability and is difficult to support. Therefore, through a series of uniaxial loading and acoustic emission (AE) experiment, this study explores the mechanical properties of mudstone-clay composites (MCCs) considering different thickness, horizon, quantity, and moisture content of clay layers. The research results show that: First, if the clay meets water, the bearing and deformation resistance of the MCCs are significantly weakened. The thicker the clay layer and the more quantity, the smaller uniaxial compressive strength and elastic modulus of MCCs. Second, the AE energy characteristics in different combinations of specimens are disparate. For the MCCs with hydrous clay and thicker mudstone, the AE energy peaks mostly appear at the beginning and end of the stress step in the post-peak stage. Third, the moisture state of the clay layers significantly influences the MCCs' failure mode and crack type. If the clay is exposed to water, the failure mode of MCCs changes from tension-shear joint failure to single tension failure. Finally, the tearing effect of clay lateral displacement on mudstone is dominant to mudstone cracking. The bonding effect of clay prompts the rock blocks on both sides of the mudstone fissures and clay layer form a triangular structure. In the post-peak stage, the periodic instability and rebalance of the triangular structure lead to a stepped decline in the stress-strain curves of the MCCs.