Surface Cracks Research Articles

Exosomal miR-574-3p from adipose-derived mesenchymal stem modulates CRIM1/BMPs signaling to restrain chondrocytes hypertrophy and inflammatory response in knee osteoarthritis.

Exosomal miR-574-3p from adipose-derived mesenchymal stem modulates CRIM1/BMPs signaling to restrain chondrocytes hypertrophy and inflammatory response in knee osteoarthritis.

Fatigue behavior of CFRP-strengthened butt-welded high-strength steel connections with surface cracks

Fatigue behavior of CFRP-strengthened butt-welded high-strength steel connections with surface cracks

Surface crack analysis and quality enhancement of 30Х13 (AISI 420) martensitic stainless steel gate valve shutters via electrolytic plasma hardening

Surface crack analysis and quality enhancement of 30Х13 (AISI 420) martensitic stainless steel gate valve shutters via electrolytic plasma hardening

Stamping part surface crack detection based on machine vision

Stamping part surface crack detection based on machine vision

The impact of surface cracks and surface roughness in the performance of hard chromium coatings in cold rolling applications

The impact of surface cracks and surface roughness in the performance of hard chromium coatings in cold rolling applications

Experimental study of microwave thawing on the LN2 frozen coals for enhancing coalbed methane extraction

Liquid nitrogen (LN2) fracturing has been studied widely in coal-bed methane (CBM) stimulation. Nevertheless, the thawing effect on the frozen coal has been rarely considered. The thawing behaviors of the frozen coal by microwave were researched using nuclear magnetic resonance (NMR), ultrasonic wave, and infrared thermal imaging. The evolution of the pore structure, temperature, water content, and surface cracks of the coal samples treated by freezing and thawing is discussed. NMR results illustrate that microwave thawing not only improves coalʼs permeability by increasing seepage pores but also removes the water from the coal. On the contrary, air thawing treatment increases the moisture of the coal sample. The losing-water rate of the samples thawed at high power is smaller than that of samples thawed at low power. The microwave thawing treatments generate cracks and reduce the wave velocity of the coal samples, and higher thawing power on the frozen is in more favor of forming macro-cracks under the same input energy. Therefore, microwave thawing on frozen coal can eliminate water blocking damage and provide the flow space for the gas. The study analyzed the feasibility of microwave thawing on the frozen coal and provided a reference method for CBM production.

Wind Turbine Surface Crack Detection Based on YOLOv5l-GCB

As a fundamental element of the wind power generation system, the timely detection and rectification of surface cracks and other defects are imperative to ensure the stable function of the entire system. A new wind tower surface crack detection model, You Only Look Once version 5l GhostNetV2-CBAM-BiFPN (YOLOv5l-GCB), is proposed to accomplish the accurate classification of wind tower surface cracks. Ghost Network Version 2 (GhostNetV2) is integrated into the backbone of YOLOv5l to realize lightweighting of the backbone, which simplifies the complexity of the model and enhances the inference speed; the Convolutional Block Attention Module (CBAM) is added to strengthen the attention of the model to the target region; and the bidirectional feature pyramid network (BiFPN) has been developed for the purpose of enhancing the model’s detection accuracy in complex scenes. The proposed improvement strategy is verified through ablation experiments. The experimental results indicate that the precision, recall, F1 score, and mean average precision of YOLOv5l-GCB reach 91.6%, 99.0%, 75.0%, and 84.6%, which are 4.7%, 2%, 1%, and 10.4% higher than that of YOLOv5l, and it can accurately recognize multiple types of cracks, with an average number of 28 images detected per second, which improves the detection speed.

Distributed AutoML framework for multi‐objective optimization of concrete crack segmentation models

AbstractMonitoring cracks in concrete surfaces is essential for structural safety. While machine vision techniques have received significant interest in this domain, selecting optimal models and tuning hyperparameters remain challenging. This paper proposes a Distributed Automated Machine Learning (AutoML) framework for efficiently designing and optimizing deep learning models for concrete crack segmentation. The framework simultaneously optimizes multiple objectives, including segmentation accuracy and mean intersection over union (mIoU), while minimizing model parameters, depth, and computational complexity. Applied to the optimization of five U‐Net model variations, the framework is compared against non‐dominated sorting genetic algorithm (NSGA‐II), Gene Manipulation Genetic Algorithm (GMGA), grid search, and random search. Results show that the proposed method produces 55% to 177% more optimal models, with 79% to 99% of its models outperforming those generated by alternative approaches. The findings highlight the potential of distributed AutoML for real‐world structural health monitoring applications, especially in edge devices.

MambaU-Light: a lightweight model for bridge surface crack segmentation based on joint distillation of multiple models

MambaU-Light: a lightweight model for bridge surface crack segmentation based on joint distillation of multiple models

GPS Enabled crack detection robot using ESP32-CAM

Ensuring the safety and durability of infrastructure is essential, particularly for roads, bridges, and industrial structures. This research introduces a Crack Detection Robot that leverages Arduino Uno, ESP32, and ultrasonic sensors to identify surface cracks efficiently. The system operates remotely via a Bluetooth module, navigates using a motor driver, and utilizes I2C communication for seamless data exchange between components. Crack detection is achieved through ultrasonic sensing, while the ESP32 manages wireless control.

Effects of piezoceramic patch size, shape, and placement on sensor performance for concrete health monitoring

Abstract Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of concrete structures. Despite the widespread use of piezoelectric materials like Lead Zirconate Titanate (PZT) for monitoring, the effects of PZT patch size, shape, and placement on sensor performance have not been thoroughly examined. This study aims to fill this gap by investigating how these factors influence the sensitivity and accuracy of piezoelectric sensors in concrete health monitoring. The sensitivity of the signal amplitude-frequency relationship to variations in the dimensions of the PZT patches is examined, with a focus on changes in the actuator or receiver side. It was found that changes in the receiver’s PZT patch size significantly affect signal sensitivity, while the actuator’s patch size has a lesser impact. This suggests the need for more precise calibration and sensitivity analysis for receiver sensors. PZT patches of the same size, whether embedded within concrete structures or surface-mounted, produce similar responses at low frequencies (below 50 kHz). However, at higher frequencies (above 50 kHz), the responses diverge markedly. Near the resonance frequency of the surface-mounted patch, significant fluctuations in the recorded signals are observed. After excluding these resonance frequencies, surface-mounted PZT patch receivers and embedded PZT patch actuators effectively detect and monitor surface cracks in concrete beams. This work provides new insights into optimizing sensor design for more reliable SHM systems and demonstrates the potential for improving concrete health monitoring practices through better sensor configurations.

Calculation of steel corrosion rate of reinforced concrete slab based on rust expansion crack.

This study aims to develop a refined calculation model that incorporates the effects of distributed reinforcement on crack propagation, validated through experimental and theoretical analysis, to improve the accuracy and applicability of nondestructive corrosion assessments. In this paper, two reinforced concrete slabs were made to consider the influence of distributed reinforcement on the corrosion process of main reinforcement.An accelerated surface cracking method was used, employing reinforcement electrification. This approach tested the relationship between crack width in reinforced concrete slabs and the corrosion rate of main reinforcement. On the basis of the existing calculation model of the relationship between the corrosion rate of reinforcement and the width of concrete surface crack, combined with the development mechanism of concrete surface crack caused by the corrosion expansion of main reinforcement under the lateral constraint conditions, a calculation model of the surface crack width of reinforced concrete slab and the corrosion rate of reinforcement is established. The comparison shows that the calculation results of the model in this paper can better reflect the test rules, and provide a reference for nondestructive quantitative detection of reinforcement corrosion in concrete structures.

Fire Risks in Using Paraffin as Neutron Radiation Shielding Material

Fire Risks in Using Paraffin as Neutron Radiation Shielding Material

Biodegradation Efficacy of Aspergillus niger and Trichoderma harzianum on Low-Density Polyethylene.

This study investigates the biodegradation potential of two fungal strains, Aspergillus niger and Trichoderma harzianum, on polyethylene plastic bags, addressing the environmental challenges posed by the resistance of the plastic material to degradation. The fungi were cultivated, and their spore suspensions were tested for polyethylene degradation in both the soil and liquid salt media. Degradation was assessed using weight loss measurements, thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). After one month in liquid medium, A. niger induced a 45.62 ± 0.21% weight loss of polyethylene, while T. harzianum achieved a 36.0 ± 0.21% weight reduction. In soil, weight losses of 9.09 ± 0.08% and 10.00 ± 0.18% were observed after two months, respectively. TGA confirmed that the fungus-treated polyethylene samples were less thermally stable than untreated controls, indicating successful biodegradation. FTIR analysis revealed structural changes in the degraded polyethylene, while SEM images demonstrated significant surface alterations, including pitting, roughening, cracks, holes, and fungal colonization. These findings confirm the enzymatic action of fungi in degrading polyethylene into monomeric forms. The study highlights the potential for fungal biodegradation as an environmentally friendly strategy to mitigate plastic pollution. Future studies should characterize the specific enzymes involved and explore genetic engineering to enhance degradation rates.

Real-Time Detection and Quantification of Rail Surface Cracks Using Surface Acoustic Waves and Piezoelectric Patch Transducers.

This paper presents a novel wayside rail monitoring system for real-time detection and quantification of rail surface cracks with sub-millimeter precision. The core innovation lies in mounting piezoelectric transducers on the web of the rail-an unconventional and practical location that avoids interference with wheel passages while enabling continuous monitoring in real-world conditions. Moreover, to directly quantify crack depth, a customized signal processing pipeline is developed, employing surface acoustic waves (SAWs) and incorporating a parallel reference transducer pair mounted on an undamaged rail section for calibration. This auxiliary pair provides a real-time calibration baseline, improving measurement robustness and accuracy. The method is experimentally validated on rail samples and verified through metallographic analysis. This approach enables condition-based maintenance by improving detection accuracy and offers the potential to reduce operational costs and enhance railway safety.

Mutual Influence of Welding Residual Stress Redistribution and Surface Crack Propagation

Mutual Influence of Welding Residual Stress Redistribution and Surface Crack Propagation

AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation

Concrete surface crack detection plays a crucial role in infrastructure maintenance and safety. Deep learning-based methods have shown great potential in this task. However, under real-world conditions such as poor image quality, environmental interference, and complex crack patterns, existing models still face challenges in detecting fine cracks and often rely on large training parameters, limiting their practicality in complex environments. To address these issues, this paper proposes a crack detection model based on adaptive feature quantization, which primarily consists of a maximum soft pooling module, an adaptive crack feature quantization module, and a trainable crack post-processing module. Specifically, the maximum soft pooling module improves the continuity and integrity of detected cracks. The adaptive crack feature quantization module enhances the contrast between cracks and background features and strengthens the model’s focus on critical regions through spatial feature fusion. The trainable crack post-processing module incorporates edge-guided post-processing algorithms to correct false predictions and refine segmentation results. Experiments conducted on the Crack500 Road Crack Dataset show that, the proposed model achieves notable improvements in detection accuracy and efficiency, with an average F1-score improvement of 2.81% and a precision gain of 2.20% over the baseline methods. In addition, the model significantly reduces computational cost, achieving a 78.5–88.7% reduction in parameter size and up to 96.8% improvement in inference speed, making it more efficient and deployable for real-world crack detection applications.

RAAC panels can suddenly collapse before any warning of corrosion-induced surface cracking

śThe collapse of reinforced autoclaved aerated concrete (RAAC) panels has attracted considerable public and academic interest. As detailed experimental data are not yet available and replicating the natural corrosion process requires years or decades, computational modelling is essential to understand under which conditions corrosion remains concealed. The very high porosity of RAAC is widely suspected to be a major contributing factor. However, current corrosion-induced cracking models are known to struggle with capturing the role of concrete porosity. To remedy this critical deficiency, we propose to enrich corrosion-induced cracking modelling with the analytical solution of reactive transport equations governing the precipitation of rust and a porosity-dependent description of diffusivity. With this, the corrosion concealment in RAAC panels is studied computationally for the first time, revealing that RAAC panels can suddenly collapse before any warning of corrosion-induced surface cracking and allowing to map the conditions most likely to result in sudden collapse.

Experimental study on the long-term conductivity of self-support fracture in deep shale reservoirs

Reservoir modification fracturing technology is one of the key technologies for the effective development of deep shale gas reservoirs. But the low initial production and fast decreasing after fracturing deep shale gas wells restrict the efficient development of deep shale gas. Many stratification fracture surfaces and tensile fracture surfaces were prepared by using a high-precision 3D morphological scanner and carving machine. How the flat fracture surfaces’ long-term hydraulic conductivity was affected by temperature and closure pressure was investigated. Additionally, the influence of closure stress and fracture surface type on the long-term hydraulic conductivity of self-supported fracture surfaces was examined by using a self-developed high-temperature and high-pressure fracture conductivity tester. The patterns of change and main control factors of the long-term hydraulic conductivity of fractures in deep shale reservoirs are aimed to be clarified. The results of the study show that the long-term flow infiltration capacity of self-supported fracture surface was affected by the closure stress and temperature. In experiments examining the impact of closure stress on the long-term infiltration capacity of flat plate cracks, there remains a pattern in the infiltration capacity. Initially, the crack infiltration capacity decreases relatively quickly before 20 h. This rapid decrease slows down as time progresses from 20 to 50 h. Beyond 50 h, the infiltration capacity of the cracks essentially stabilizes. When comparing the results under the same proppant conditions, it’s clear that a higher closure stress leads to a more rapid decrease in crack infiltration capacity. Furthermore, the final stable infiltration capacity of cracks in the experimental group subjected to large closure stress is notably smaller compared to that of the group with small closure stress. This indicates that closure stress plays a significant role in determining the long-term infiltration performance of flat plate fractures. In the experiments of supported cracks and self-supported cracks, the higher the temperature, the smaller the inflow capacity, and the decrease is gradually reduced; the conductivity of self-supported cracks decreases rapidly, and at 20 h, the inflow capacity decreases to about 1% of the initial value. Under the same conditions, the infiltration capacity of stratification fracture surface is higher than that of tensioned crack surface. The results of this paper can provide certain reference significance for the design and construction of oilfield site fracturing, and then provide certain contribution to the improvement of oil and gas recovery.

A Review on Resistance‐Based Self‐Sensing of Carbon Fiber‐Reinforced Polymer Subjected to Loads

Carbon fiber‐reinforced polymer (CFRP) composites exhibit remarkable self‐sensing capabilities, where electrical resistance varies with externally applied loads, enabling their application in structural health monitoring (SHM) without additional devices. This review comprehensively analyzes the conductive mechanisms of CFRP, resistance variations under diverse loading conditions, and the electrical responses induced by strain and damage. It also explores optimization strategies for enhancing self‐sensing capabilities and theoretical resistance models. In unidirectional CFRP, resistance changes primarily due to fiber‐to‐fiber contact variations, making it highly strain‐sensitive. Multidimensional CFRP can detect interlayer cracks, impact damage, and multiaxial stresses. Adding conductive fillers below the percolation threshold enhances strain sensitivity, while fiber surface modifications, optimized fiber volume fractions, and improved manufacturing processes further enhance self‐sensing performance. Practical applications demonstrate that surface cracks and internal damages can be monitored via electrical resistance measurements in CFRP structures. By integrating current knowledge and highlighting future research directions, this review provides valuable insights into optimizing CFRP's self‐sensing properties and expanding its use in advanced SHM systems.