Crack Monitoring Research Articles

Digital image correlation based crack monitoring on masonry arch bridges

Digital image correlation based crack monitoring on masonry arch bridges

Monitoring soil cracking using OFDR-based distributed temperature sensing framework

Soil cracking induced by extreme drought represents a widespread natural phenomenon occurring across the earth surface, capable of triggering multiple weakening mechanisms within surface soils, potentially leading to the instability and failure of slopes and agricultural infrastructures. This study proposes an innovative geophysical monitoring framework for detecting field soil cracking by combining the actively heated fiber-optic (AHFO) method and distributed fibre optical sensing (DFOS) based on optical frequency domain reflectometry (OFDR) technique, referred to as AH-OFDR framework. Laboratory calibration tests, field monitoring tests, numerical simulations, and sensitivity analyses were employed to comprehensively evaluate the feasibility, effectiveness, and limitations of the AH-OFDR framework for soil crack monitoring. Laboratory calibration confirmed that the DFOS-OFDR technique achieves a minimum spatial resolution and readout accuracy of 1 mm, along with a temperature measurement accuracy of ±0.1 °C. Field monitoring verified that the AH-OFDR framework can accurately detect soil cracks ranging in width from 0.01 m to 0.12 m. Additionally, numerical simulations not only validated the effectiveness of the AH-OFDR framework across a broader range of crack widths, from 0.01 m to 0.50 m, but also established a quantitative relationship between temperature changes and the spatial distribution of crack positions and widths. Notably, a critical crack width threshold of 0.30 m was identified within the AH-OFDR framework, significantly impacting the prediction of soil crack widths. Sensitivity analysis demonstrated the remarkable crack detection capabilities of the AH-OFDR framework, irrespective of the soil crack width and spacing. The AH-OFDR framework holds substantial potential as an innovative and high-resolution observational method for advancing our understanding of diverse geological and hydrogeological processes.

METHODOLOGY FOR THE APPLICATION OF THE DIGITAL IMAGE CORRELATION (DIC) FOR INVESTIGATING RC BEAMS

The article discusses the improvement of the digital image correlation (DIC) method for analyzing deformations in RC beams, specifically focusing on the importance of zero-strain verification. This step is critical for ensuring high measurement accuracy, as it helps identify and minimize systematic and random errors. Before starting the research, system calibration is conducted, which includes the assessment of background noise and stability that influence the results. The study shows that proper sample preparation, pattern creation, and control of external factors allow for obtaining reliable data. The application of DIC enables remote monitoring of cracks and evaluation of the stress-strain state of structures. It has been established that this method is useful not only for scientific experiments but also in practical engineering, contributing to the increased reliability of structures.

High-Sensitivity RFID Tag Sensor With Coupled Ring Resonators for Multiposition Crack Monitoring

High-Sensitivity RFID Tag Sensor With Coupled Ring Resonators for Multiposition Crack Monitoring

An Integrated Approach for Structural Crack Monitoring: Combining Plastic Tell Tales and Image Analysis for Enhanced Structural Health Evaluation

Structural Health Monitoring (SHM) calls for the development of the ideas of safety and main ability of civil structures. The most critical of the aspects explored in the paper is the safety monitoring of the structural configuration of any civil engineering work. Supervision of any sign of an odd shift from the usual state of structure enables one to prevent and or counter severe loss. It also depends on other environmental parameters such as load, nature of a seasonal parameter and the type of soil. Within this research project the Al-Beruni Academic Block of Sarhad University of Science and Information Technology is taken to monitor the cracks using Glass Tell-Tales, to collect data on cracks of buildings and during seismic activity, observe, study the cracks that are present.

Study of three-dimensional crack extension in turbine discs of short-life turbojet engines

Abstract A simulation study on the 3D crack extension of short-lived turbojet engine turbine discs was carried out using FRANC3D 3D fracture mechanics software combined with ABAQUS finite element software. By establishing a 3D geometric model of the turbine disc, the crack extension process is simulated by using the sub-model method, and the crack extension behavior and failure mode are analyzed to predict the remaining life of the cracked wheel disc. The results show that the crack expansion path matches well with the crack failure phenomenon of the turbine disc under the actual working conditions; the turbine disc crack is dominated by Type I (open Type) crack expansion, and the stress intensity factors of Type II and Type III are negligible; the crack expansion rate increases slowly with the increase of crack depth, and it rises steeply at the critical value of the fracture failure; and the residual life of the turbine disc is 32 cycles. This study provides empirical analyses for crack monitoring and life prediction of turbine discs and is of great significance for engine maintenance and reduction of end-of-life costs of turbine components.

Deformation-fractional change in resistance model of SMA fiber reinforced mortar beams

Superelastic shape memory alloys (SMA) are metallic alloys that can enable components with self-recovery of cracks and deformation when combined with cement-based materials. Meanwhile, the monitoring method based on fractional change in resistance (FCR) can achieve self-monitoring of components throughout the entire loading process. In this study, the relationship between crack recovery characteristics and FCR of SMA fiber reinforced mortar beams subjected to flexural cyclic loading was analyzed. The beams with SMA fiber volume fractions of 0.1 %, 0.3 %, 0.5 %, and 1.0 % were tested under three-point bending loads. During the loading process, the FCR of the specimens were monitored by the four-electrode method. Finally, the deformation-FCR model for the whole process of the specimens was established to realize the real-time monitoring of the loading state of mortar beams. The results show that SMA fibers can enhance the flexural strength of mortar beams by a maximum of about 64 %, reduce residual deformation, and improve deformation self-recovery ability. The mid-span deflection recovery rate and crack size recovery rate generally increase with higher fiber volume fraction, which can maintain a better deformation recovery effect at the early stage of cracking and gradually decrease with the expansion of cracks. There is a close relationship between the FCR and the degree of crack development that the FCR increases with crack expansion and the magnitude of FCR decreases with increasing fiber content. The established first-order logarithmic fitting model can well describe the relationship between the FCR and cracking, which can provide reference for crack monitoring.

A multi-scale deep residual network-based guided wave imaging evaluation method for fatigue crack quantification

Abstract As a promising structural health monitoring technology, guided wave (GW) imaging is gaining increasing attention for crack monitoring of aircraft structures. However, actual fatigue crack propagation is a complex dynamically evolving process affected by various variabilities. It is still challenging to accurately track and quantify the dynamic fatigue crack propagation with GW imaging methods. Therefore, in order to achieve more accurate fatigue crack quantification, this paper proposes a multi-scale deep residual network-based GW imaging evaluation method. A convolutional neural network (CNN) is utilized to evaluate the entire pixel distribution of GW imaging maps to fuse damage-related information from multiple GW monitoring paths. By designing multi-scale convolutional kernels and deep residual learning, a robust quantitative image feature extraction is ensured with the dynamic evolution process of fatigue crack growth and the performance degradation is avoided as the CNN goes deeper, thereby improving the quantification accuracy. The method is validated on a fatigue test of landing gear beams, which are important load-carrying aircraft structural components. The results demonstrate that the proposed method can extract multi-scale crack length-related features and accurately track fatigue crack propagations. For batch specimens, the maximum quantification error is reduced from the original 6.1 mm to 1.6 mm, marking a significant improvement.

Strain concentration applied on crack inspection based on marker-based and homography matrix algorithm

ABSTRACT The detection and identification of ship damages have become a significant area of research. This study proposes an image-based structural crack monitoring method that marks the crack locations in the form of strain concentrations by analyzing the overall strain of the structure. Most existing studies focus on using fixed cameras for structural crack monitoring. However, during the actual operation of structures, continuous monitoring around these structures is challenging. To overcome this limitation, this paper implements the transformation from non-fixed camera images to fixed images using a marker-based approach and homography matrix algorithm. In laboratory-scale structural experiments, the algorithm is capable of locating structural crack positions by observing the behavior of strain concentration. Additionally, this study explores the application of markers in structures and verifies the capability of marker attachment methods in crack inspection.

Crack monitoring and repair model of SMA composite material based on strain transfer

In the process of using composite materials, crack damage is unavoidable. These cracks may cause a decrease in the structural performance of the material and may even pose a threat to the safety of the entire structure. In this paper, the shape memory alloy (SMA) is embedded into the composite material, and the strain transfer effect of the interface layer is considered. The crack monitoring and repair model of SMA composite material based on strain transfer are established using SMA resistance-sensing characteristics and SMA constitutive model. Based on this monitoring and repair model, the crack monitoring behavior of SMA under different initial states and temperature conditions is discussed, and the crack repair behavior of SMA under specific initial states and temperature conditions is also discussed. The research results show that the change in SMA resistance and the strain of the composite material crack are linearly segmented, and as the temperature increases, the composite material crack is gradually repaired. This study can provide theoretical guidance for the further engineering application of SMA composite material crack monitoring and repair theory.

A fatigue crack prediction method based on inductive semi-supervised learning and Lamb-wave monitoring for orthotropic steel bridge deck

Orthotropic steel bridge deck suffers from fatigue cracking due to its orthotropic configuration and cyclic vehicular load. Hence, fatigue crack monitoring and prediction are crucial for addressing this issue. The Lamb-wave technique has potential in monitoring the growth of fatigue cracks, whereas most of the existing signal processing methods show poor performance in the crack prediction. To improve the accuracy and reliability of fatigue crack prediction, an inductive semi-supervised learning (ISL) approach is proposed in conjunction with Lamb-wave monitoring. The presented method is first verified by conducting a fatigue test of orthotropic steel bridge deck loading by three load conditions which would lead to different types of cracks, where lightweight Lamb-wave monitoring devices were adopted. The approach was further applied in field monitoring to validate its effectiveness in real engineering structures. The results demonstrate that the proposed method can recognize the initiation of fatigue cracks by giving a predicted time interval in which the real initiation moment locates. Similar increasing trends are found between the predicted and real crack lengths during the growth of cracks, while a relative fluctuate is existing in the predicted results, and the prediction accuracy of crack lengths can be regarded as 2 mm. For bridge field monitoring, the real fatigue crack length was also well predicted by the proposed method, exhibiting an average error of 0.82 mm in comparison with the real values. In general, the research findings emphasize the adaptability of the proposed method for fatigue crack prediction in practical engineering.

Monitoring of a fatigue test on a full-size railway steel bridge with acoustic emission

Within the frame of the Rail4Future project (https://www.rail4future.com) co-funded by the Austrian Research Promotion Agency, a railway steel bridge was removed from the track and transported to a workshop in one piece. Artificial defects were introduced at different sections of the bridge structure as per advice of the scientific project partners in order to initiate fatigue cracks in the course of the subsequent fatigue test. One objective of the fatigue test was to identify suitable measurement and testing techniques for monitoring of fatigue crack growth. Among other techniques utilised by different project partners, acoustic emission monitoring was applied by TÜV AUSTRIA. This contribution describes the preparation as well as the performance of the acoustic emission monitoring and the results obtained. It is shown that this non-destructive testing technique is suitable for fatigue crack monitoring.

Supporting the infrastructure operators with customer specific AE-monitoring solutions

Cost-effective and efficient monitoring of infrastructure is crucial for planning maintenance and repairs while ensuring the safety of the structure. Acoustic emission is a method particularly suitable for long-term monitoring. However, the necessary extended monitoring period incurs high costs for the operator, primarily due to the exceptionally high costs of measuring equipment. The need for a tailored monitoring system for infrastructure applications is evident. TÜV AUSTRIA is developing a monitoring system called RISE, which represents a customized solution for steel bridge monitoring. The innovative online monitoring solution RISE assists experts with structural monitoring. Due to its compact design, low power consumption, and easy installation, the measuring system is specifically designed for continuous monitoring. Specifically, RISE is introduced for the hotspot monitoring of railway steel bridges. The passage of trains triggers a material response that can be recorded by the measuring system. This is used to perform crack monitoring on bridges and to have a tool for assessing crack activity and the degree of damage. This enables targeted planning of repairs and maintenance activities. Reducing the operating costs of the structure and ensures it save operation. The system is being developed to function robustly and provide a high level of automation. A demonstration of AE-monitoring of active cracks on a main bridge girder was done during a fatigue test in cooperation with the TU Graz. The girder was removed from a deconstructed bridge.

Dual channel visible graph convolutional neural network for microleakage monitoring of pipeline weld homalographic cracks

When using a single sensor to monitor early microleakage of nuclear power pressure pipeline leakage, there are problems such as low monitoring accuracy and poor early warning reliability due to the limitations of the monitoring range and weak difference between the leakage signals. To address these challenges, this paper proposes a dual channel visible graph convolutional neural network (DCV-GCN). Firstly, the acoustic emission time-series data of each channel are truncated and divided, and the significant frequency bands are selected based on the envelope spectrum. On this basis, the sequence group is averaged to obtain the graph structure sequence. Then, the limited penetrable visibility (LPV) graph construction algorithm is used to calculate the adjacency matrix, and the important nodes is reserved according to the eigenvector centrality. Furthermore, the inverse ratio of the distance from the sensor in each single channel to the center of the crack is used as the fusion weight, and the adjacency matrices are merged after normalization to transform the construction of the graph structure dataset. Finally, the dataset is input into the graph convolutional neural network, and the effectiveness of the method is verified by carefully designing three homalographic cracks. The results show that the proposed method can effectively extract the distinguishing features with similar frequency components and similar leakage rates, and the recognition accuracy of different leakage states can reach 98.56 %. In addition, through ablation experiments and different parameter strategy settings, the operating mechanism is explained, which can provide a reference for monitoring and analysis by industrial technicians.

Advanced concrete pavement internal crack monitoring using wave response variation and deep learning

This paper presents an in-depth investigation into internal crack monitoring in concrete pavement through the application of wave response variation (WRV) and deep learning techniques. The study examines wave scattering in both homogeneous (HM) and inhomogeneous (IHM) media, validating WRV by analyzing forward scattering due to interval vertical cracks through laboratory tests and analytical solutions. It compares various laboratory tests with experimental and finite element (FE) results. The analysis reveals that shallower cracks result in peaks at lower impulse frequencies on the WRV curve, while deeper cracks correspond to peaks at higher frequencies, as observed from both forward and incident waves. The study also finds that the complexity of IHM, influenced by random aggregate size and distribution, significantly affects WRV patterns, with larger aggregates causing greater energy attenuation in forward waves compared to smaller aggregates. Machine learning (ML) techniques are employed to predict cracks and clarify the impact of aggregate information on WRV. This research establishes a framework for understanding internal damage in IHM using ML technology. This study enables effective monitoring of internal vertical cracks, such as reflective cracks in bridge decks, pavements, and airport runways.

Structural strain and crack monitoring based on the fluorescence response of graphene quantum dots

Graphene quantum dots (GQDs), a novel nanomaterial, offer new avenues for structural health monitoring owing to the remarkable fluorescence response. In this study, the GQDs-epoxy resin composites were fabricated and applied to structural surface. Tests including tension, compression, bending, and fatigue were conducted to explore the fluorescence response of composites to structural strain and cracks. Results show that composites exhibited a heightened fluorescence sensitivity to structural strain. During the tensile tests of coated steel specimens, the fluorescence intensity of composites exhibited positive and negative correlation responses with increasing strain. Whereas in the compression test of coated concrete specimens, solely increased fluorescence intensity with rising strain was observed. Composites demonstrated excellent crack characterization on concrete and steel structures, whether penetrating or developing cracks. The fluorescence response of composites to cracks was based on the fluorescence enhancement at the crack site, and fluorescence intensity was approximately inversely proportional to crack width.

A deep learning framework for predicting slab transverse crack using multivariate LSTM-FCN in continuous casting

Accurate and timely predictions of transverse cracks in slabs are crucial for ensuring efficient and high-quality production in continuous casting. However, the accumulation of substantial amounts of complex, multi-dimensional time series data during the casting process considerably affects the modeling performance. In order to increase the prediction accuracy, this paper introduces a novel prediction framework for slab transverse cracks (PF-STC), which comprises three stages: data preparation, data processing, and model training. First, a synchronized scheme is developed for real-time data tracking to achieve one-to-one matching between the process data in the data preparation phase. Second, the data preprocessing stage tackles the multidimensionality of time series data through normalization and mean aggregation downsampling. Finally, in the model training phase, a multivariate long short-term memory-fully convolutional network (MLSTM-FCN) algorithm is utilized to improve the predictive performance. Experimental results on a real dataset from a steel manufacturing company demonstrate that the PF-STC framework outperforms recent state-of-the-art methods, reducing the number of parameters by 49.97% and training time by 33.83%. The PF-STC achieves an AUC score of 0.9435, with accuracy, precision, recall, and F1 scores of 0.9163, 0.8657, 0.8286, and 0.8467, respectively. The proposed PF-STC is poised to provide support for online transverse crack monitoring, with the application demands of monitoring production processes for the steel industry.

Innovative device for crack monitoring and deflection analysis in structures: Improved accuracy, safety and efficiency

It is of the utmost importance to ensure the structural integrity of buildings in order to guarantee the safety of their occupants. Conventional techniques for monitoring cracks and conducting flexural analysis during loading and unloading tests frequently prove inadequate in terms of accuracy and safety. This study introduces ES1293890 U, an innovative dual-purpose device designed for the precise remote monitoring of cracks and comprehensive flexural analysis. The device is integrated seamlessly into structural health monitoring (SHM) and building information modelling (BIM) systems, thereby providing a holistic approach to structural assessment.The ES1293890 U device offers superior accuracy, with a resolution of 0.01 mm and a maximum deviation of 0.007 mm, which makes it a more effective alternative to existing commercial solutions. The utilisation of recycled and biodegradable materials has the effect of reducing production costs, thereby facilitating the undertaking of more frequent and economically viable testing and monitoring. This enhanced affordability is of paramount importance for the maintenance of building safety and structural integrity through regular assessments.A noteworthy attribute of the ES1293890 U is its capacity to conduct remote measurements, obviating the necessity for professionals to enter potentially hazardous test areas. This not only ensures the safety of personnel but also enables continuous and uninterrupted monitoring, with real-time data capture facilitating timely and informed decision-making.In comparison to traditional gauges, the ES1293890 U exhibits enhanced performance in the monitoring of slab deflections, attributable to its elevated accuracy and comprehensive data acquisition capabilities. This device represents a substantial advancement in structural assessment technology, providing a reliable and efficient tool for improving building safety and integrity. The ES1293890 U's innovative approach and cost-effective design have the potential to revolutionise structural monitoring practices, making it an invaluable asset to both the scientific community and the construction industry.

Sensing Skin Technology for Fatigue Crack Monitoring of Steel Bridges: Laboratory Development, Field Validation, and Future Directions

A significant number of steel bridges are vulnerable to fatigue cracks, and the timely discovery of damage is critical in ensuring safety and continuous operations. Despite various crack monitoring methods available in the field of structural health monitoring (SHM) to empower real-time feedback, few commercially-available technologies are applicable to the task of discovering new cracks because of the highly localized nature of the sensors. The authors have developed a sensing skin constituted from soft elastomeric capacitors (SECs). An SEC is a large-area strain gauge that transduces strain into a measurable change in capacitance. It can be easily deployed over large surfaces, and thus can be used to discover new fatigue cracks. The technology has been developed and characterized in a laboratory environment over the last decade. It has been recently deployed in the field on a bridge located in Kansas, USA. The aim of this paper is to present and discuss technological updates that were necessary to enable field deployment, with the objective of supporting the field deployment of the cSEC and other SHM technologies. In particular, it reviews the following SEC technology modifications: 1) corrugation of the surface of the dielectric, creating a corrugated SEC or cSEC, to improve sensing performance; 2) development of a dedicated wireless data acquisition system; and 3) improvement of the data fusion algorithm to account for higher signal contamination in the field. After this review, challenges in conducting field deployment are examined. Lastly, a discussion is provided on a path to commercialization.

Destructive Testing of the Pinkabach Railway Bridge – Large scale Testing of Sensor Systems for Fatigue Crack Monitoring and System Identification

The work presents experiments carried out on an out-of-service steel railway bridge, the so called Pinkabach bridge. The objective was to test advanced and novel sensor systems, i.e. Distributed Fiber Optic Sensing (DFOS) and Acoustic Emission (AE), for the monitoring of fatigue cracks, both for crack initiation and subsequent crack growth, in steel railway bridges. Also, sensor systems for overall system identification, i.e. Tiny Motion (TM), Distributed Acoustic Sensing (DAS) and dynamic response analysis, have been tested. The Pinkabach bridge was a single span plate girder railway bridge with a span width of 21,5m that was taken out of service and transported to a secure environment that allowed for destructive testing with permanent access to the structure itself under controlled conditions. By harmonic excitation of the structure at its first resonance frequency, cyclic stress levels comparable to the stress levels during train passage were generated and it was possible to emulate the fatigue load from decades of train traffic within the limited period of the experiments. Crack initiation and growth happened at two locations of the flanges and crack growth was monitored till the crack length was half of the flange width of the main girder. To end the experiments a static load test showed the remaining capacity of the damaged structure. A conventional sensor system was used for validation and comparison with calculated predictions based on linear fracture mechanics, used for the design of the experiments. An overview over the whole set of experiments as well as (intermediate) results and findings on the applicability of the novel sensor systems are presented in this paper. The experiments are a collaborative work of multiple scientific and industrial partners and have been carried out as part of Area 3.1 of the COMET Project Rail4Future, funded by the Austrian Research Promotion Agency FFG.