Crack Monitoring Research Articles

Concrete crack pixel-level segmentation: a comparison of scene illumination angle of incidence

Previous research has demonstrated how angled and directional lighting can enhance the detection of concrete cracks in low-light environments and outperform diffused lighting alternatives. This paper investigates the effect of different angles of incidence of directional lighting for concrete crack pixel-level segmentation. Five directional lighting datasets of cracked concrete slabs were captured, each using an angle of incidence of 10, 20, 30, 40, and 50 degrees, respectively. A directional lighting crack segmentation algorithm was applied to each lighting angle dataset. Algorithm output comparisons with ground truths revealed that the directional lighting method performed best on the 50-degree lighting dataset, obtaining a recall, precision, and F1 score of 68%, 81%, and 74%, respectively. However, qualitative analysis of the segmentation outputs on a sub-image scale revealed that towards the edges of the images, the segmentation performance of 30-degree lighting was significantly better, with results closely matching those of the ground truth. This research highlights that the lighting angle of incidence can increase the performance of directional lighting concrete crack segmentation depending on defect position. The results from this work have the potential to improve low-light environment concrete crack detection and monitoring.

Machine Learning-Enabled Acoustic Emission Signal Processing for real-time crack growth monitoring in heterogeneous structures

Heterogeneous structures pose unique challenges for structural health monitoring, demanding advanced techniques for early detection of crack propagation. Acoustic Emission (AE) is a non-destructive testing technique that relies on the detection and analysis of stress-induced high-frequency acoustic waves. It is commonly used for identifying and monitoring the development of cracks, defects, or other structural anomalies. However, raw AE data is often noisy and contains background noise and interference, which make them not suitable for reliable real-time crack monitoring applications. This study integrates AE data with machine learning algorithms to provide a proactive and accurate system for continuous crack propagation monitoring. The performance of the proposed approach for features extraction to derive relevant information from the AE signals, data labeling for training machine learning models, and the deployment of these models for real-time monitoring, are discussed.

Comparison between acoustic emission sensors and piezoelectric patches for damage detection in concrete beams

Acoustic emission (AE) sensing is an established technique to monitor crack nucleation and propagation in different types of structural elements and for different damage scenarios. Although effective for crack monitoring in civil infrastructure, off-the-shelf AE sensors can be expensive, discouraging their installation in large numbers for assessing the global structural performance or monitoring multiple assets in the infrastructure network. For this reason, this paper explores the use of piezoelectric patches as an alternative low-cost solution to AE sensors. These patches are considerably cheaper, do not require external power supply and can be easily attached to existing structural elements or embedded into new structures. Moreover, thanks to the direct and inverse piezoelectric effect, the same set of patches can act both as sensors and actuators, meaning that they can be used to “passively” monitor crack propagation over time, as well as to locate and characterize damage in ageing structures at a certain time by using the patches as emitters and receivers of waves. In this framework, this paper proposes a comparison between traditional AE sensors and piezoelectric patches. The comparison is performed on a set of three concrete beams subjected to four-point bending loading, each equipped with two grids of sensors, i.e. 8 AE sensors and 8 patches. The beams are tested under increasing monotonic loading, from zero-load uncracked condition up to collapse.

Online detection and identification of cathode cracking in Lithium-ion battery cells

Cyclic charging and discharging of Lithium-ion (Li-ion) battery cells lead to the contraction and expansion of the battery electrodes. These contractions and expansions result in the development of internal stresses within the electrodes, further culminating in the growth of cracks. Typically, the cracks in anodes lead to an increase in the surface area hence facilitating a faster SEI layer growth, and a lot of research to model such faults has already been conducted in this area. However, when it comes to cracks in the cathode, the research is still a little under-explored. Not detecting the potential cathode crack growth may lead to quick degradation of battery cells which results in capacity fade or resistance growth. If these kinds of faulty batteries are not detected, it may result in hazardous scenarios like battery fires even during nominal usage. Therefore, the real-time monitoring of these cathode cracks is essential for health-conscious battery operation. This paper is an attempt to design such real-time monitoring algorithm that can detect and identify crack growths in the cathode. The algorithm is developed by fusing a variation of the Single Particle Model (SPM) capturing Lithium concentration dynamics in the cathode and an empirical model capturing crack growth — in conjunction with real-time feedback-based coupled filters. The coupled filter consists of two filters working in cascade where the first filter generates a primary residual based on cathode SPM and terminal voltage feedback. Subsequently, the second filter utilizes this primary residual as feedback in conjunction with the empirical crack growth model — ultimately producing an estimate of the crack growth. This estimated crack growth is used to detect and identify the cathode cracking mechanism. Proposed approach is tested with experimental as well as simulation studies, illustrating its effectiveness.

Enhancing Agricultural Efficiency: Deep Learning-Based Soil Crack Detection for Water Irrigation

The escalating demand for agricultural precision and environmental monitoring underscores the necessity for effective soil crack detection methods. This study explores the feasibility of employing a Raspberry Pi-powered camera system and deep learning image recognition to detect soil cracks and control agricultural irrigation. The purpose is to develop a soil crack detection system using deep learning techniques, sustain plant growth process, increase productivity, and optimize water irrigation practice. Our approach leverages TensorFlow to craft a convolutional neural network tailored specifically for execution on a Raspberry Pi 3B+. A dataset comprises manually captured images and is trained with the InceptionV3 model categorized into crack or nocrack classes. The accuracy is achieved ranging from 97% to 99%. These results underscore deep learning image recognition models on Raspberry Pi for cost-effective soil crack monitoring and controlling the plants watering system.

Conduction thermography for non-destructive assessment of fatigue cracks in metallic materials

Metallic materials are widely used in many applications of mechanical and aerospace engineering. Due to severe environmental and loading conditions, components and structures can experience cracking phenomena. In this regard, different non-destructive techniques were developed in the last years to detect and monitor cracks that can propagate during the actual loading conditions.This work deals with the application of the conduction thermography as a non-destructive technique for the offline control and inspection of different notched metallic specimens characterized by short fatigue cracks. The potentials of the technique have been demonstrated considering a low-cost set-up consisting of a power supply of low current and limited voltage, and a microbolometer sensor. Moreover, to further simplify the inspection, the specimens were not black coated. Quantitative results, in terms of crack lengths, have been obtained using a new procedure of data analysis and then compared to those provided by thermoelastic stress analysis, a well-established technique adopted for the detection and monitoring of cracks.

Crack monitoring in concrete beams under bending using ultrasonic waves and coda wave interferometry: the effect of excitation frequency on coda

Crack monitoring in concrete beams under bending using ultrasonic waves and coda wave interferometry: the effect of excitation frequency on coda

Experimental Study of Structural Crack Monitoring for Ocean Platform Based on a Hybrid Fiber Sensing Technique

Experimental Study of Structural Crack Monitoring for Ocean Platform Based on a Hybrid Fiber Sensing Technique

Particle filter for fatigue crack growth prediction using SH0 wave on-line monitoring

Fatigue crack is one of the main failure modes of pressure vessels. Online monitoring and predicting methods of crack growth play an important role in the operation of important pressure vessel. The SH0 wave is non-dispersive, and it is not disturbed by internal media of pressure vessel and very sensitive to cracks, therefore it is suitable for fatigue crack growth monitoring. Moreover, fatigue crack growth in industry is affected by material properties, loads, which usually shows some uncertainty. And the particle filter (PF) is well suited to deal with prediction problems affected by uncertainty. Hence, the prediction method of crack growth based on SH0 wave monitoring and PF is proposed (short for SH0-PF). The basic theory of crack monitoring method using SH0 wave is introduced, and the signal feature extraction using the damage index is studied. The state equation characterizing the fatigue crack growth is established by Paris model, and the observation equation is established based on the normalized correlation moment damage index according to monitoring signal using SH0 wave. The prediction reliability of the fatigue crack growth applying SH0-PF is verified by experiment with the single edge notched specimen. The experimental results indicate that the prediction accuracy of SH0-PF is better than that of the traditional Paris model.

Crack monitoring for municipal sewer pipes using DFOST

The distributed fiber optic sensing technique (DFOST) provides an effective solution for long-term deformation monitoring of sewer pipelines under operation conditions due to occurrence of cracks. However, optical fibers attached on the pipeline wall might fail to capture all cracks as they are usually highly spatially scattered, resulting in misjudgment of pipe structural conditions. To address this challenge, this paper presents a novel deployment approach of distributed fiber optic monitoring system for pipeline cracks. First, the working principle of optical frequency domain reflectometry (OFDR) is explained. Second, the production of concrete pipe specimens with different optical fiber sensors deployment schemes is shown in detail. Finally, a comprehensive analyse of the results of visual inspection and fibre optic monitoring is completed. To evaluate the performance of this approach, experimental data from various cases were considered. The conclusion of this paper provides theoretical and empirical support for the application of distributed fiber optic sensors for pipeline cracks monitoring.

Monitoring fatigue cracks in riveted plates using a sideband intensity based nonlinear ultrasonic technique

Aluminum structures are routinely used in aircraft due to their lightweight and corrosion resistance properties. Multi-layered aluminum plates are generally joined by rivets forming regions which are prone to fatigue crack formation in an aircraft. Therefore, the detection and monitoring of fatigue cracks at rivet joints in aluminum structures are crucial for ensuring flight safety. In this study, piezoelectric sensors were utilized to generate and detect Lamb waves on aluminum plates with rivet joints to investigate the feasibility of a newly developed Sideband Peak Count (SPC) technique for detecting fatigue cracks around these joints. To overcome the limitations of existing SPC-I (Sideband Peak Count – Index) and SPI (Sideband Peak Intensity) techniques in capturing harmonic and modulating wave frequencies due to material nonlinearity, a comprehensive index, the Sideband Intensity Index (SII) is introduced. Comparative analysis with existing SPC-I and SPI techniques confirm the effectiveness of the SII technique. This investigation shows that the SII technique significantly improves the detection capability of initial fatigue cracks around rivet joints on aluminum plates. This study offers a more efficient method for detecting critical fatigue cracks in rivet joints.

In-situ graft fibrous PPy@PEI enables sensitive and all-seasoned crack monitoring for ancient architectures based on flexible pressure sensors

The non-destructive, real-time, and automatic monitoring for the development of cracks in ancient architectures is crucial to the preservation of precious ancient heritages. Here, light-weight crack sensing constructed with ultra-thin and high-performance flexible pressure sensors is proposed to fulfill this demand. The signal drift and poor reversibility commonly observed in widely-reported sensors based on elastomer matrix, which originates from the creep and relaxation behaviors, are addressed using piezoresistive polypyrrole-grafted polyetherimide fibers. The corresponding flexible piezoresistive sensors exhibit great sensitivity (9.45 kPa−1 in 0 ∼ 1 kPa and 3.48 kPa−1 in 3 ∼ 10 kPa), outstanding static stability (5.13 % signal drift after 48 h under 1 kPa), dynamic durability (4.72 % degradation after ∼30,000 cycles) and excellent robustness across an ultra-wide temperature range of −70 ∼ 150 ℃. Based on this highly reliable pressure sensor, the proposed non-destructive crack sensors integrated into an Internet of Things (IoT) platform display outstanding stability during their service in the Forbidden City. By virtue of the low energy consumption, one lithium battery can power the monitoring system for more than one year.

A silicone rubber packaged distributed optical fiber sensing tape for strain-crack monitoring based on OFDR technique

A novel silicone rubber (SR)-packaged distributed optical fiber sensor (DOFS) tape based on optical frequency domain reflectometry (OFDR) technique was developed to effectively monitor substrate cracking. The analytical strain distribution of this sensor tape is derived based on the current strain transfer model and then used to determine the parameter sensitivity to provide a scientific basis for the selection of packing parameters and bond length. A calibration test of the sensor tape on steel plates was performed, and the feasibility of the sensing tape for accurately measuring the substrate strain and crack width was demonstrated. A test is conducted to monitor adjacent double cracks, and the test results show that the sensor tape has the ability to identify crack positions with crack spacings greater than 40 mm. The sensor tape is applied to the monitoring of concrete cracking induced by corrosion, and the test results demonstrate that the DOFS sensing tape has advantages for monitoring large cracks. Both the analytical and experimental results show that the novel SR-packaged DOFS tape is effective and feasible.

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures.

Value of information from fatigue monitoring of the monopile foundation of an offshore wind turbine

Value of information (VoI) allows quantifying benefits of adopting structural health monitoring (SHM) even before implementation by altering the prior risk of structural failure using the probabilities of damage detection from SHM data. The VoI is the reduction in the expected failure cost when SHM data becomes available. In this study, an optimized time of SHM deployment for fatigue crack growth in an offshore wind turbine (OWT) monopile foundation is achieved by maximising VoI. The prior probabilities of failure are obtained from stochastic analysis of dynamic responses to random wind and waves characteristic of North Sea environment. Short-term stress time histories are statistically processed to determine the probability distribution functions of their extrema and ranges and statistical extrapolation techniques are then implemented to analyse the long-term probability distributions of axial stresses. The long-term stress ranges and numbers of cycles are determined by the rainflow count and used in fatigue reliability analysis. The probability of detecting a fatigue crack by SHM is modelled and used for calculating the OTW failure risk after repair is conducted based on SHM outcome. The optimal deployment time of the fatigue crack monitoring system for the analysed 5-MW OWT is determined as 21 years past erection.

Based on an Overview of Crack Detection

This Crack detection, as an important part of concrete structural health monitoring, aims to reflect the stress state and damage degree of the structure. Traditional concrete crack detection mainly relies on manual visual identification, which has low detection efficiency and accuracy. In addition, manual visual detection has problems such as being greatly affected by lighting conditions, being unable to cover overhead locations such as bridge towers and high piers, and being highly subjective. In recent years, in order to solve the above problems, researchers at home and abroad have developed concrete crack detection equipment based on digital image technology, such as inspection vehicles equipped with high-resolution cameras, unmanned aerial vehicles, crawling robots, and so on. Meanwhile, efficient and accurate crack detection algorithms are the basis for realizing accurate crack identification. How to balance the detection speed and accuracy has been the focus of academic attention. This paper reviews the research progress of concrete crack detection equipment based on digital image technology at home and abroad in recent years, including camera platform, calibration method, preprocessing, traditional and deep learning algorithms, crack feature extraction, image stitching, and 3D representation and monitoring of cracks. In addition, the deficiencies in the research are summarized.

Hole-edge crack monitoring in attachment lug with large bolt hole based on guided wave and circular piezoelectric sensor array

The crack damage monitoring of aircraft structures is very significant for ensuring aircraft safety, reducing maintenance costs and extending service life. Due to the extreme service environment, the attachment lug is prone to initiate crack damage at the hole edge, which leads to crack propagation and fracture failure. Structural health monitoring technology based on piezoelectric guided wave has been widely studied, promoting the development of crack monitoring. However, at present, research on hole-edge crack damage monitoring of attachment lugs still needs to be further carried out. It is difficult to monitor small cracks at the initial stage of crack propagation, and the accuracy of crack monitoring needs to be improved. By focusing on the accuracy of the crack monitoring in the attachment lug, a crack damage monitoring method based on the circular piezoelectric sensor array is proposed in this paper. Combined with damage alarming and localization imaging, this method comprehensively evaluates the hole-edge crack damage monitoring situation and improves the monitoring effect. The method is verified by an experiment in attachment lug, and this verification includes small crack monitoring and crack propagation monitoring. The experimental results demonstrate that this method can achieve correct damage alarming results, and the maximum localization error of crack damage is only 3.02 mm, which provides a research idea for the accurate monitoring of crack damage at the hole edge.

Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation

The stability of wind turbines is closely related to the economic benefits of wind energy. To improve the stability of the wind turbine, a comprehensive image diagnosis method based on artificial intelligence method called ‘Multivariate Information Perception You Look Only Once’ (MIP-YOLO) is proposed. MIP-YOLO is an improved algorithm based on YOLOv8 that can classify, detect, segment and evaluate the crack damage level, and can be utilized for monitoring surface cracks on wind turbine blades. To improve the detection capability of small, relatively weak targets such as cracks, Multivariate Information Perception and C2TR modules are put forward. Aim at enhancing the ability of extracting objects with edge features, the Haar wavelet attention (HWA) and C2fGhost modules are proposed. In order to make the model extract features better, C2CBAM module is put forward in this paper. For purpose of solving the problem that some samples in the dataset may have poor quality, wise-IOU is introduced into the model. The detection performance of the proposed method is tested using wind turbine's blade images with cracks taken by Unmanned Aerial Vehicles (UAV). The experiment shows that MIP-YOLO can realize the fault diagnosis of blade effectively and improve the economic benefit of wind energy.

Ultrasonic testing and Monitoring of Hydrogen blistering and Hydrogen-induced Cracking of LPG Storage Tanks

Due to the presence of various corrosive chemicals in LPG (liquefied petroleum gas, or propane) storage tanks, it is very likely to cause different degrees of corrosion in the tank and derive various defects. This article analyzes the main characteristics and types of defects found in storage tanks and provides an overview of ultrasonic testing and monitoring of defects such as hydrogen blistering and hydrogen-induced cracking.

Mock-up performance evaluation study for crack reduction of blast furnace slag powder concrete mixed with expansive and swelling admixtures

In this study, the drying shrinkage and crack reduction characteristics of blast furnace slag concrete mixed with expansive and swelling admixtures were investigated. Basic performance experiments were conducted using different mixtures of ground granulated blast-furnace slag (GGBS), with calcium sulfoaluminate as the expansive admixture and bentonite and hydroxypropyl methyl cellulose (HPMC) as swelling admixtures. Bentonite outperformed HPMC as a swelling admixture. Specimens were then prepared for mock-up tests to evaluate the drying shrinkage of blast furnace slag concrete with different combinations of bentonite, a hardening accelerator, and a self-healing agent. The addition of bentonite and calcium phosphate as a self-healing agent in small quantities reduced the drying shrinkage of the specimens, thereby reducing cracks. The cement mixture composed of 30% GGBS, 1% bentonite, and 1% calcium phosphate (30-E1-I1) showed the optimal performance among the specimens. Further, crack monitoring was performed in concrete made with ordinary Portland cement and optimal mixture 30-E1-I1. No cracks were observed for the optimal mixture. This shows that GGBS concrete can be used in practical and field applications, subject to mid- and long-term tests for cracking.