Wall Cracks Research Articles

Characteristics of Damage to Rural Houses in the High-Intensity Area of the Jishishan Mw 6.2 Earthquake

On 18 December 2023, a 6.2-magnitude earthquake struck Jishishan, affecting multiple counties and cities in Gansu and Qinghai Provinces. The seismic intensity of the meizoseismal area was VIII, resulting in extensive structural damage and building collapses. A damage assessment was conducted of the epicenter and surrounding high-intensity zones. To understand the typical structures and characteristics of the buildings that were damaged in these high-intensity zones, this study summarizes the characteristics of the damage to typical rural houses, compares the damage of the rural houses across different sites, and analyzes the causes behind these variations. The findings of the study indicate the following: (1) Timber and some brick–timber structures, due to their age, insufficient material strength, and lack of adequate connections between parts of the building, primarily experienced severe damage or total collapse, characterized by through-wall cracks, partial collapses, or complete collapses. (2) Brick–concrete structures predominantly suffered moderate to severe damage due to factors such as improper layout, uneven façades, and inadequate or incomplete seismic measures. The observed damage included significant wall cracks and extensive damage to two-story buildings. (3) Frame structures, mainly used for public facilities like schools, hospitals, and health centers, exhibited strong integrity and excellent seismic performance, resulting in minimal to no damage, with damage largely confined to non-load-bearing components. (4) The amplification effects of seismic waves in thick loess basin areas, slope sites, and the hanging wall effect of faults exacerbated structural damage to rural houses located in certain villages within the high-intensity areas. The results of this study can serve as a reference for post-disaster reconstruction and seismic retrofitting of buildings and contribute positively to enhancing the disaster resilience of rural housing.

Crack Detection and Cost Analyzer Using AI

Crack Vision is an innovative AI-powered Android application developed to detect and analyze wall cracks, providing users with real-time, automated repair cost estimates. By leveraging advanced object detection models such as YOLOv7 and Mask R-CNN, Crack Vision enhances both the accuracy and efficiency of crack detection, enabling precise measurement of crack dimensions, severity assessment, and quick identification of structural issues. This application simplifies the inspection and budgeting process for wall repairs, making it an essential tool for homeowners, contractors, and structural engineers who seek to minimize reliance on manual inspection methods. The core functionality of Crack Vision combines high-resolution image processing with state-of-the-art machine learning algorithms, ensuring reliable detection across various wall types and crack textures. Using computer vision and deep learning techniques, the app can differentiate between structural and non-structural cracks, thereby offering a comprehensive solution for repair prioritization. Furthermore, Crack Vision integrates with a robust backend that stores user data, allowing users to track the condition of their walls over time. This feature aids in preventive maintenance by identifying trends in crack expansion and deterioration, potentially mitigating costly repairs. Additionally, the application provides a detailed breakdown of estimated repair costs based on crack size, depth, and type, empowering users to make informed decisions about necessary repairs. Crack Vision is designed with user-friendliness in mind, featuring an intuitive interface that guides users through the inspection process. The app also supports multilingual options and customizable settings to cater to diverse user needs. In sum, Crack Vision redefines wall crack inspection, offering a rapid, accurate, and accessible solution for both casual users and industry professionals, ultimately contributing to improved building safety and maintenance efficiency

Precursory crater contraction associated with the 2017 eruption of Shinmoe-dake volcano (Japan) detected by PALSAR-2 and Sentinel-1 InSAR

The time series of PALSAR-2 and Sentinel-1 images reveal inflation at the volcanic flank and contraction at the crater for approximately 5 months before the 2017 eruption of Shinmoe-dake volcano, Japan. While the observation of inflation at the volcano’s flank is ubiquitous, few studies have reported crater contraction preceding an eruption. The flank inflation stopped after the 2017 eruption, while the contraction at the crater continued until the 2018 eruption. We found that a pipe-shaped deformation source above sea level best fits the observation preceding the 2017 eruption. Suppose the flux of ejected materials constrains the conduit radius during the previous 2011 eruption. In that case, the amount of deformation of the pipe-shaped deformation source, whether open or closed at its top, is too large to be realistic. Although constraining the conduit radius from the eruption flux overestimates the pressure change of the pipe-shaped deformation source, water-saturated fractures along the volcanic conduit could extend the effective conduit radius of the pressure source. We propose one potential scenario for the mechanism of the crater contraction preceding volcanic eruptions based on the combination of compaction due to cooling by ambient groundwater and material withdrawal within the conduit. The groundwater inflows from the ambient aquifer through cracks in the porous conduit wall, which are generated by conduit expansion during the magma ascent. Decoupling from the conduit wall due to a decrease in volume of the material promotes material instability and crater contraction. The interaction between the groundwater and the magma triggers the 2017 eruption of Shinmoe-dake volcano, as previous studies have reported.Graphical

Local-Global Feature Adaptive Fusion Network for Building Crack Detection.

Cracks represent one of the most common types of damage in building structures and it is crucial to detect cracks in a timely manner to maintain the safety of the buildings. In general, tiny cracks require focusing on local detail information while complex long cracks and cracks similar to the background require more global features for detection. Therefore, it is necessary for crack detection to effectively integrate local and global information. Focusing on this, a local-global feature adaptive fusion network (LGFAF-Net) is proposed. Specifically, we introduce the VMamba encoder as the global feature extraction branch to capture global long-range dependencies. To enhance the ability of the network to acquire detailed information, the residual network is added as another local feature extraction branch, forming a dual-encoding network to enhance the performance of crack detection. In addition, a multi-feature adaptive fusion (MFAF) module is proposed to integrate local and global features from different branches and facilitate representative feature learning. Furthermore, we propose a building exterior wall crack dataset (BEWC) captured by unmanned aerial vehicles (UAVs) to evaluate the performance of the proposed method used to identify wall cracks. Other widely used public crack datasets are also utilized to verify the generalization of the method. Extensive experiments performed on three crack datasets demonstrate the effectiveness and superiority of the proposed method.

A Hybrid Attention Mechanism and RepGFPN Method for Detecting Wall Cracks in High-Altitude Cleaning Robots

A Hybrid Attention Mechanism and RepGFPN Method for Detecting Wall Cracks in High-Altitude Cleaning Robots

The effect of carbon black on degradation of pipe grade black polyethylene in high concentration chlorine dioxide solutions

In this research, the impact of carbon black on the accelerated degradation of pipe grade polyethylene (PE100) exposed to high levels of chlorine dioxide (150 ppm) is examined. Tensile testing reveals a faster degradation rate in black samples compared to neat samples, indicating a detrimental effect of carbon black aggregates on the polymer's aging process. Rheological analysis shows changes in molecular weight and structure due to chemical degradation and chain scission and can be a reliable method for detecting slight changes in molecular structure. Isothermal crystallization shows a slowdown in crystallization kinetics at first, explained by gel-formation due to crosslinking which hinder the crystallization, and then an increase in the kinetics as apparently the chain scission gets dominant again. Neat samples exhibit a higher density of tie molecules, indirectly revealed by much more fibrils in crack wall observed in FESEM images, suggesting better resistance to chemical degradation while the black sample shows a much less fibrillar crack wake and becomes almost completely devoid of any fibrils at later stages of aging. The fibrils, which essentially offer a load-bearing role against the widening and growth of the crack play a key role in resistance to slow crack growth (SCG). Therefore, the higher SCG resistance is expected for neat grade compared to black samples. The study proposes a dual-layer pipe design with a UV-resistant black outer layer and an oxidation-resistant neat inner layer to prolong the lifespan of PE100 pipes by protecting against UV radiation and chemical reactions. This solution offers increased durability, lower maintenance costs, and environmental sustainability benefits.

Investigation of human interaction with an augmented remote operating system for scaling in mining applications

Thanks to the advent of telepresence applications, we can remotely take control and operate industrial machinery. Teleoperation removes operators from hazardous workplaces such as mining and plays an essential role in the safety of workers. In addition, augmented telepresence can introduce information that helps the user understand the remote scene. However, remote operation presents challenges since the information received is more limited than what could be perceived by being physically present, such as accurately judging depth. This study investigates how well operators interact with an augmented remote operation scaling system (AROSS) in a mining context when different computer-generated visual interfaces are provided. The system can achieve five visual interfaces: Disocclusion Augmentation view using selective content removal, Novel Perspective view generation, Lidar view, Right (Original) view, and Left (Original) view. We performed two experiments in a mine-like laboratory to analyze human interaction with the designed prototype by applying a mixed research methodology that used questionnaires, interviews, and observations. This mixed methodology consisted of quality of experience methods to discover the users’ requirements from a technological standpoint and user experience methods (i.e., user-centric approaches). We investigated 10 and 11 users’ interactions in two experimental studies. The first experiment focused on identifying small patterns (e.g., cracks in the mine wall), and the second focused on depth and three-dimensional understanding. We considered the first experiment a feasibility test to understand how to conduct the second experiment. Therefore, we designed the second test to assess the technical readiness of AROSS from the users’ perspective. The overall conclusion yields a comprehensive understanding of users’ perceptions and experiences. The quality of experience results favored Left and Right (Original) views for remote control, indicating a preference among remote operators using natural (Original) views due to their facilitation of environmental comprehension. User experience analysis revealed the reason why other views were less favored and what their potential benefits are. Specifically, Novel Perspective and Lidar views were found helpful for depth perception, and Disocclusion Augmentation view functionality could be enhanced if robot arm position tracking is enabled. These insights inform design recommendations, emphasizing the value of incorporating Disocclusion Augmentation and Novel Perspective views and suggesting improvements to enhance system usability.

Condition assessment of concrete structures using automated crack detection method for different concrete surface types based on image processing

In the inspection and diagnosis of concrete construction, crack detection is highly recommended in the earliest phases to prevent any potential risks later. However, the flaws in concrete surfaces cannot be reliably and effectively identified using traditional crack detection techniques. The suggested algorithm is a supportive tool for agents or authorities to use in crack detection mechanisms to monitor and assess the current condition of buildings or bridges. The researchers aim to establish an intelligent model for automatic crack detection on different concrete surfaces based on image processing technology. Three different concrete surfaces—bridge decks, walls, and concrete cubes—are used to test the model. A subset of the public dataset of bridge decks and walls from SDNET (2018) and 150*150*150 mm of concrete cubes taken from the material laboratory of the faculty of engineering at Ain Shams University are applied to the model. The model F1-score measures are 98.87%, 97.43%, and 74.11% for detecting cracks in bridges, walls, and concrete cubes, respectively. The validation of the applicability of the suggested novel approach is based on a comparison with recent methods for crack recognition. The contribution of this study is that it could be applied to detect cracks efficiently on three different types of concrete surfaces, including uneven concrete surfaces, random noise, voids, dents, colour changes, and stain marks. The proposed method is transparent in its workflow and has a lower computational cost compared with deep learning frameworks. Thus, the outcomes of this model demonstrate its effectiveness in concrete defect field investigation.

Impact of freeze recovery method on high-speed fracture in metal cylindrical shells

The freeze recovery method (FRM) is a crucial approach for investigating the high-speed fracture process of metal cylindrical shells under explosive loading. However, the precise impacts of the recovery process on the deformation and fracture behavior of such shells remain unclear, significantly constraining the widespread application of this method in high-speed fracture studies. This paper quantitatively evaluates the effects of the expansion contour, fracture mode, and damage state of intermediate shells at different stages of fracture development using a crack evolution simulation method and nondestructive crack detection technique. The recovered shell contour can effectively represent the free expansion contour of the shell at the equivalent moment, with an error of less than 4%. Impact induces tensile cracks on the outer wall of the shell, which leads to changes in the local fracture mode. A method of crack elimination and equivalence in the damage statistics of the recovered shell is proposed to address this effect. The recovered shell can characterize the damage evolution during free expansion at the equivalent moment after eliminating the influence of excess tensile cracks. Based on the principle of stress analysis and energy conservation, the formation mechanism of tensile cracks in the outer wall of the shell is explored, and the correlation between tensile cracks and recovery time is elucidated. The study shows that the impact of fracture damage caused by freezing recovery is gradually reduced over time. The improved freezing recovery method based on the hard recovery principle is successfully used to recover the multistage intermediate shell, meeting the demand for obtaining the transient physical model in the high-speed fracture field.

A non-local based microcrack segmentation model optimized for effective high resolution and low-power devices

Concrete is notable for its strength, durability, and versatility, making it a fundamental material in construction. Nevertheless, microcracks occur in concrete structures due to various factors, and the size of these cracks gradually widens over time, which can lead to spalling. Furthermore, the advent of climate change accelerates concrete degradation, posing significant challenges to structural maintenance. Recently, deep learning-based computer vision techniques, especially convolutional neural networks, have shown promising results in detecting and segmenting cracks in concrete walls. In this study, we propose a two-step training strategy for a microcrack segmentation model optimized for high resolution and low-power devices. The first step is to develop a segmentation model that can detect microcracks through high-resolution images. This strategy includes generating a high-quality CrackHQ dataset using super-resolution and a Transformer-based model training process named CegFormer. The second step involves converting the trained model to a lightweight version, increasing inference speed and making it operable on low-power devices. While performing lightweight, the model performance inevitably is degraded. We restored the degraded crack recognition performance by applying knowledge distillation. The experimental results of comparing baseline and CegFormer showed a 12.82% improvement in microcrack recognition performance. The detector optimized for use in actual fields improved recognition performance by up to 5.16% and reduced inference speed by 51.52%. The utilization of the two-step training strategy enabled the optical sensors to accurately detect microcracks even from far fields. Future work will focus on developing crack detectors that take into account both indoor and outdoor environmental conditions, as well as different texture of surface materials, for application in building inspection.

Design and control of a central-rotor type pentacopter design and implementation capable of contacting to and moving on wall surface

This paper explores the design and control of a center-rotor type pentacopter that can move in contact with a wall to accurately measure cracks in a concrete outer wall. A center rotor pentacopter is a type of pentacopter that uses an electric ducted fan (EDF) motor and two axis servomotors at the center of gravity of the quadcopter. Center-rotor pentacopters are characterized by the ability to generate thrust in the desired direction using two servomotors and an EDF motor to maintain a stable attitude without tilting or leaning during flight. In this paper, the mathematical modeling of a center-rotor pentacopter was verified through simulation and compared with a regular quadcopter. We also simulated the thrust of the center rotor to see how much it could withstand when a sudden gust of wind occurred. Then, we built a pentacopter and measured the cracks and calculated the width of the cracks through the actual pentacopter in addition to flight experiments. The results showed the advantages and applicability of the pentacopter.

Methods for Investigate and Resolving Shear-Induced Wall Cracks

Methods for Investigate and Resolving Shear-Induced Wall Cracks

Adaptive Attention-Enhanced Yolo for Wall Crack Detection

With the advancement of social life, the aging of building walls has become an unavoidable phenomenon. Due to the limited efficiency of manually detecting cracks, it is especially necessary to explore intelligent detection techniques. Currently, deep learning has garnered growing attention in crack detection, leading to the development of numerous feature learning methods. Although the technology in this area has been progressing, it still faces problems such as insufficient feature extraction and instability of prediction results. To address the shortcomings in the current research, this paper proposes a new Adaptive Attention-Enhanced Yolo. The method employs a Swin Transformer-based Cross-Stage Partial Bottleneck with a three-convolution structure, introduces an adaptive sensory field module in the neck network, and processes the features through a multi-head attention structure during the prediction process. The introduction of these modules greatly improves the performance of the model, thus effectively improving the precision of crack detection.

Numerical and experimental investigations of cracked light-frame timber walls

This study investigates the impact of sheathing panel cracks on the structural performance of light-frame, modular-based timber buildings, focusing on the racking stiffness and strength of the individual timber walls in the modules. Previous research has investigated such walls for decades and lead to practical design methods in the harmonized European design code, Eurocode 5. Such hand calculation methods are effective for simple geometries but for walls with openings or complex forms, a correct prediction of stiffness and strength is considerably harder to achieve and load levels where cracks initiate are almost impossible to predict. The paper presents both experimental and numerical studies to investigate how significant cracking in sheathing panels affects the load-carrying capacity of various light-frame timber walls. Finite element simulations using Abaqus are conducted to model the cracking of sheathing panels with the extended finite element method. Moreover, an orthotropic elasto-plastic connector model is introduced for the nail joints. The results indicate that significant cracking of the sheathing panels influences the stiffness and the load-carrying capacity of the wall elements and that the crack initiation and propagation is strongly affected by factors such as the location of openings, the shape of the sheathing panels and the type and position of sheathing-to-framing connections. The numerical results presented align satisfactory with the experimental data particularly regarding load levels at crack initiation and propagation. Furthermore, a parametric study investigates how cracks, orthotropic connector properties and vertical constraint of bottom rails influence the racking strength of different timber walls.

Concrete Surface Crack Detection Algorithm Based on Improved YOLOv8.

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.

Automated Geometric Quantification of Building Exterior Wall Cracks Based on Computer Vision

Automated Geometric Quantification of Building Exterior Wall Cracks Based on Computer Vision

The Brittle Fracture of Iron and Steel and the Sharp Upper Yield Point Are Caused by Cementite Grain Boundary Walls

Brittle fractures of iron and steel above twinning temperatures are caused by cementite grain boundary wall cracks. These were revealed by an Atomic Force Microscope (AFM). At temperatures below the ductile–brittle transition (DBT), cracks must propagate longitudinally within cementite walls until the stress is sufficiently high for the cracks to propagate across ferrite grains. Calculations using these concepts correctly predict the stress and temperature at the DBT required for fractures to occur. At temperatures above the DBT for hypoeutectoid ferritic steels, dislocations must be emitted across the walls transversely for plastic deformation to continue. This is responsible for the upper yield point at the elastic limit in these steels followed by a large drop in stress to the lower yield point. Here, the walls completely surround all of the grains. Where the walls are segmented, such as in iron, dislocations can pass around the walls, resulting in a gradual change from elastic to plastic deformation. The Cottrell atmosphere theory of yielding is not supported experimentally. It was the best available until later experiments, including those using the AFM, were performed. Methods are presented here giving yield strength versus temperature and also the parameters for the Hall–Petch and Griffith equations.

Self‐training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation

AbstractThis study proposes a novel self‐training framework for unsupervised domain adaptation in the segmentation of concrete wall cracks using accumulated crack data. The proposed method incorporates Bayesian neural networks for uncertainty estimation of pseudo‐labels, and spatial priors of cracks for screening noisy labels. Experiments demonstrate that the proposed approach achieves significant improvements in F1 score. Comparing the F1 scores, Bayesian DeepLabv3+ and Bayesian U‐Net showed performance improvements of 0.0588 and 0.1501, respectively, after domain adaptation. Furthermore, the integration of Stable Diffusion for few‐shot image generation enhances domain adaptation performance by 0.0332. The proposed framework enables high‐precision crack segmentation with as few as 100 target images, which can be easily obtained at the site, reducing the cost of model deployment in infrastructure maintenance. The study also investigates the optimal number of iterations for domain adaptation based on the uncertainty score, providing insights for practical implementation. The proposed method contributes to the development of efficient and automated structural health monitoring using AI.

Controlling Seepage Flow Beneath Hydraulic Structures: Effects of Floor Openings and Sheet Pile Wall Cracks

Using one opening (filter) within the floors of hydraulic structures is a known technique to relieve the seepage effects on their floors. In this study, a new method to control seepage flow by using two identical filters instead of one was tackled numerically. A comparative analysis of using one versus two filters was conducted for different thicknesses of the permeable stratum, apron size (b), filter length, and sheet pile wall depths. Results indicate that two filters are considerably more effective than using one where the overall uplift force, the maximum potential head, and the hydraulic exit gradient downstream of the floor are reduced to 42–56%, 42–51%, and 66–76%, respectively, compared to one filter, while slightly increasing seepage flow by 1–7%. Many reasons can lead to horizontal openings (cracks) appearing along the sheet pile walls beneath hydraulic structures. The current study tackled their effects on seepage flow for the first time and examined their impact on the floor. A crack in the upstream sheet pile wall can increase total uplift forces by up to 40%, while a crack in the downstream sheet pile wall can increase the hydraulic exit gradient by up to 230%

Slip and stress in block-in-matrix shear zones: 1. microstructure and mineralogy of a serpentine-filled dilational jog

We document the structural setting, microstructure, and mineralogy of a crack-seal-type dilational jog that developed in the stepover between two faults cutting through a phacoid of massive serpentinite, itself embedded within a serpentinite shear zone at the base of the Dun Mountain Ophiolite, New Zealand. Our outcrop and microstructural measurements allow us to constrain the boundary conditions for a numerical model (part 2) that quantitatively explores the relationships between stress, fault slip, and incremental cracking in block-in-matrix shear zones. The dilational jog is c. 3 cm wide and contains hundreds of crack-seal bands, each c. 20–30 μm wide. Internally, the jog comprises two mineralogically distinct crack-seal domains: serpentine-only domains and serpentine-andradite garnet domains. Additionally, individual crack-seal bands have a double-layer structure: in serpentine-only domains each band comprises a thin (<2 μm) layer of chrysotile and a thicker layer (c. 25 μm) of polygonal serpentine/lizardite, whereas each band in serpentine + andradite domains comprises a thinner (c. 5 μm) layer of microcrystalline andradite and a thicker layer (c. 15 μm) of polygonal serpentine/lizardite. Micro-CT analysis shows that the serpentine + andradite domains have conic or ellipsoidal shapes with long axes subparallel to the inferred jog opening direction, and that andradite is smeared along micro-transform surfaces inside the jog. Our conceptual microstructural model invokes jog formation during progressive serpentinization of the host rock. Incremental crack opening along the jog-wall rock interface promotes relatively rapid initial precipitation of chrysotile or andradite at high fluid:rock ratios. As cracks fill and pressure re-equilibrates, relatively slow growth of polygonal serpentine/lizardite is favoured until the cracks are sealed and the cycle repeats. Our observations suggest that the precipitation of andradite (instead of chrysotile) was controlled both by structural boundaries within the jog (e.g., micro-transform surfaces) and by local element transport (e.g., Ca from serpentinizing clinopyroxene grains in the host rocks) to patches of the crack wall.