Crack Mapping Research Articles

Nondestructive evaluation of select bridges in Iowa, USA using high-speed and ground-coupled robotic NDE platforms

This paper intends to provide a detailed assessment of the condition of two select bridge decks in the state of Iowa, USA. The field-data collection was obtained by a novel data collection system called “Infratek insight” which consists of the High Definition (HD) and High Speed (HS) platforms. The data collection took place in two phases. Phase one (HS) used air coupled NDE sensors at traffic speed while phase two (HD) utilized ground coupled NDE sensors using robotic deployment, autonomous navigation and driving systems to eliminate location variability. The data acquisition during both phases was subject to real-time data quality control by a custom data acquisition software that was developed by Infratek. Manual chain dragging was also performed at select areas to inspect the bridge deck condition and to validate data obtained by the insight system. A suite of NDE technologies were utilized on the High-Speed (HS) and High Definition (HD) platforms including Impact Echo (IE), Ultrasonic Surface Waves (USW), Electrical Resistivity (ER), Ground-Coupled GPR, Air-Coupled GPR, Surface Laser Profilers, 360-Degree Visual Imaging, Infrared thermography (IR), High-Definition Line Scan Imaging and Automated Crack Mapping with Artificial Intelligence Engines, LiDAR and Automated High-Speed Sounding (Chain Drag). ER and GPR are used to assess the corrosion potential throughout the bridge deck. GPR is also used to determine cover depth. IE, Chain Drag and IR are the primary delamination evaluation tools, and USW enables evaluation of concrete quality and degradation. Results indicated a high degree of correlation between the HD and HS platforms and also between individual sensors. The results and maps produced by the HS and HD platforms were utilized in quantifying repairs and providing the asset owner with data-driven recommendations on future rehabilitation actions based on best practices and owner specific criteria.

Center Atom Model for Strain Mapping of Void and Crack of Atomic Lattice Image

Center Atom Model for Strain Mapping of Void and Crack of Atomic Lattice Image

Integrated pixel-level CNN-FCN crack detection via photogrammetric 3D texture mapping of concrete structures

Although multiple learning-based crack detection systems show promising results in detecting cracks with pixel accuracy on individual images, few effectively enable inspection of larger structures. This paper thereby proposes an advanced inspection reporting system based on an integrated CNN-FCN crack detection system applied on the texture space of a footing, enabling crack inspection and display for larger structures. The system, a Convolutional Neural Network (CNN) and a Fully Convolutional Network (FCN), segments cracks at the pixel-level on the texture space, acquired from a 3D model created with photogrammetry techniques. Firstly, the trained CNN is employed to detect crack patches, then imported to the trained FCN system to segment cracks at the pixel-level, and a crack map is then generated which is projected onto a 3D model. This system indicates promising results for footing textures as represented by: Accuracy (99.88%), Precision (82.2%), Recall (90.2%), and F1 Score (86.01%).

High‐resolution real‐time multipurpose tunnel surface 3D rendering

AbstractTunnel surveys making use of photogrammetric three‐dimensional (3D) tunnel reconstruction reach resolutions in the millimeter range. Classical big data visualization approaches display point clouds only, neglecting this considerable resolution difference between structure and texture. The article suggests a data structure that separates structural and textural resolution by a regular grid on the unwrapped design surface for 3D, combined with a UV mapping technique as regularly used in computer graphics. For real‐time rendering of huge multiscale data sets, the result of photogrammetric commercial‐off‐the‐shelf reconstructions is transformed into a proprietary hierarchical data structure. It facilitates to only load currently relevant parts of the tunnel surface from the hard drive, and only upload and render currently adequate levels‐of‐detail onto the graphics card for seamless exploration of high‐resolution geometric and image 3D tunnel data of arbitrary length. The solution allows for smooth interactive analysis and annotation such as crack identification and mapping, inventory, deformation assessment, and dimensional measurements. Aspects of data generation are addressed and information is given about the data structure, showing examples from entire tunnel 3D representations to demonstrate the smooth behaviour of the real‐time rendering of huge data volumes in various scales on standard graphics hardware.

A Novel Image-based Measurement of Crack Characteristics with Tree Structure

This study proposes a novel image-based measurement of crack properties in concrete construction. The method first retrieves the crack skeleton and profile from the crack map generated by pre-processing the original image. Then, the crack skeleton is abstracted into a tree structure, and small edges from the crack trunk are removed to calculate the length of the crack. Finally, Euclidian distance transform is applied on the crack profile to calculate the width of the crack. The proposed method can identify crack properties automatically and enhance stability, durability, and safety evaluation of the concrete construction. Validity and accuracy are tested by experiments on crack images of real concrete construction.

SMART SKY EYE System for Preliminary Structural Safety Assessment of Buildings Using Unmanned Aerial Vehicles.

The development of unmanned aerial vehicles (UAVs) is expected to become one of the most commercialized research areas in the world over the next decade. Globally, unmanned aircraft have been increasingly used for safety surveillance in the construction industry and civil engineering fields. This paper presents an aerial image-based approach using UAVs to inspect cracks and deformations in buildings. A state-of-the-art safety evaluation method termed SMART SKY EYE (Smart building safety assessment system using UAV) is introduced; this system utilizes an unmanned airplane equipped with a thermal camera and programmed with various surveying efficiency improvement methods, such as thermography, machine-learning algorithms, and 3D point cloud modeling. Using this method, crack maps, crack depths, and the deformations of structures can be obtained. Error rates are compared between the proposed and conventional methods.

Detection of cracked teeth using a mechanoluminescence phosphor with a stretchable photodetector array

Cracked tooth syndrome (CTS) is an incomplete fracture of a human tooth that commonly arises from chewing hard foods. Although it is a very common syndrome, CTS is often difficult to diagnose owing to the common small size of tooth cracks. Conventional techniques for the detection of cracks, such as transillumination and radiographic methods, are inaccurate and have poor imaging resolution. In this study, we devise a novel method for the in vivo detection of tooth microcracks by exploiting the mechanoluminescence (ML) phenomenon. ZrO2:Ti4+ (ZRT) phosphor particles are pasted onto suspected regions of tooth cracks and emit cyan-colored light as a result of masticatory forces. Then, a stretchable and self-healable photodetector (PD) array laminated on top of the phosphor particles converts the emitted photons into a photocurrent, which facilitates the two-dimensional mapping of the tooth cracks. Because of the high photosensitivity of the PD, intense ML and small size of ZRT phosphor particles, it is possible to image submicron- to micron-sized cracks with high resolution. Furthermore, the uniqueness of this technique over the conventional techniques stems from the application of a simple optical phenomenon, i.e., ML, for obtaining precise information regarding the locations, depth, and length of tooth cracks.

Improving Slope Stability in Open Cast Mines via Machine Learning based IoT Framework

Slope stability has been a matter of concern for most geologists, mainly due to the fact that unstable slopes cause a greater number of accidents, which in turn reduces efficiency of mining operations. In order to reduce the probability of these slope instabilities, methods like tension crack mapping, inclinometer measurements, time domain reflectometry, borehole extensometers, piezometer, radar systems and image processing systems are deployed. These systems work efficiently for single site slope failures, but as the number of mining sites increase, dependency of one site slope failure on nearby sites also increases. Current systems are not able to capture this data, due to which the probability of accidents at open cast mines increases. In order to reduce this probability, a high efficiency internet of things (IoT) based continuous slope monitoring and control system is designed. This system assists in improving the efficiency of real-time slope monitoring via usage of a sensor array consisting of radar, reflectometer, inclinometer, piezometer and borehole extensometer. All these measurements are given to a high efficiency machine learning classifier which uses data mining, and based on its output suitable actions are taken to reduce accidents during mining. This information is dissipated to nearby mining sites in order to inform them about any inconsistencies which might occur due to the slope changes on the current site. Results were simulated using HIgh REsolution Slope Stability Simulator (HIRESSS), and an efficiency improvement of 6% is achieved for slope analysis in open cast mines, while probability of accident reduction is increased by 35% when compared to traditional non-IoT based approach.

In situ crack mapping of large-scale self-sensing concrete pavements using electrical resistance tomography

This study aims to validate the large-scale application of self-sensing concrete in airport runway pavements and to use electrical resistance tomography (ERT) for characterizing spatially distributed damage during accelerated pavement testing. This self-sensing concrete not only retains the expected mechanical properties of typical airport pavements, but it can also sense deformation and strain. First, sensing properties were encoded in concrete pavements by modifying the cement-aggregate interface with multi-walled carbon nanotube (MWCNT) thin films. MWCNT thin films were spray-coated onto dried fine and coarse aggregates, and the film-coated aggregates were directly used for concrete casting. Second, an ERT algorithm was implemented for spatial conductivity mapping of self-sensing concrete pavements. Extensive laboratory tests were conducted on different sized specimens for characterizing their spatial damage detection performance. Last, a full-scale concrete airport pavement slab was cast with self-sensing concrete patches at locations where damage was expected. A heavy vehicle simulator was employed for accelerated pavement testing to induce cracks, while ERT measurements were collected at periodic intervals during testing. The results confirmed that the severities, locations, and patterns of cracks could be identified from the reconstructed ERT conductivity maps. Furthermore, subsurface damage features were identified prior to these cracks propagated and became visible on the surface.

A Computational Study of the Shear Behavior of Reinforced Concrete Beams Affected from Alkali-Silica Reactivity Damage.

In this study, an investigation of the shear behavior of full-scale reinforced concrete (RC) beams affected from alkali–silica reactivity damage is presented. A detailed finite element model (FEM) was developed and validated with data obtained from the experiments using several metrics, including a force–deformation curve, rebar strains, and crack maps and width. The validated FEM was used in a parametric study to investigate the potential impact of alkali–silica reactivity (ASR) degradation on the shear capacity of the beam. Degradations of concrete mechanical properties were correlated with ASR expansion using material test data and implemented in the FEM for different expansions. The finite element (FE) analysis provided a better understanding of the failure mechanism of ASR-affected RC beam and degradation in the capacity as a function of the ASR expansion. The parametric study using the FEM showed 6%, 19%, and 25% reduction in the shear capacity of the beam, respectively, affected from 0.2%, 0.4%, and 0.6% of ASR-induced expansion.

Detection and maintenance of cracks by novel algorithm using IoT

The most significant task aimed at maintaining the bridge is the “bridge deck crack inspection”. Conventionally, the inspector identifies the cracks utilizing eyes and notices the crack location manually. Nevertheless, accuracy of inspection outcome is less because of “subjective nature of human judgment”. And Suggested a “crack inspection system” which utilizes the mobile robot equipped with the camera to gather bridge deck pictures. And in this model, “Laplacian of Gaussian (LoG) algorithm” is utilized to identify cracks & “global crack map” is achieved by localization of robot and “camera calibration”. Ensuring that robot gathers entire bridge deck pictures, the “path planning algorithm” on the basis of genetic-algorithm will be improved. And the “path planning algorithm” identifies the solution that reduces the count of turns and moving distance. The evaluation is done on the suggested method by both experiments & simulations.

Feature-Based Point Cloud-Based Assessment of Heritage Structures for Nondestructive and Noncontact Surface Damage Detection

Assessment and evaluation of damage in cultural heritage structures are conducted primarily using nondestructive and noncontact methods. One common deployment is laser scanners or ground-based lidar scanners that produce a point cloud containing information at the centimeter to the millimeter level. This type of data allows for detecting surface damage, defects, cracks, and other anomalies based only on geometric surface descriptors using a single dataset, which does not rely on a change detection approach. Moreover, geometric features are not influenced by color, which is essential for heritage structures because they are nonuniform in color due to anthropologic and environmental effects (e.g., painting or moisture). In this work, a damage detection method developed based on local geometric features is evaluated and expanded for crack detection within the example fresco walls of Sala degli Elementi in the Palazzo Vecchio. The workflow’s performance is then compared in a qualitative manner to that of manual crack mapping results identified using images.

A fast image-stitching algorithm for characterization of cracks in large-scale structures

Visual inspection of concrete cracks has been widely used in structural health monitoring (SHM). Capturing highresolution images is an effective method to visualize a complete crack, but it is difficult to show a whole crack from a single high-resolution image. One feasible method is using image stitching technique to stitch several images into a complete crack map. However, the current image stitching method is a computationally intensive process. Numerous images are required to cover large-scale structures with sufficient resolution, this can be computationally prohibitive. To address this problem, an improved image stitching method for crack damage evaluation is proposed, which can quickly stitch the crack images without affecting the quality of the stitching or the resulting images. Rather than first stitching the images together and then determining the crack maps, we propose to first develop the crack maps for the individual images and then stitch them together. The proposed method reduces the number of redundant matching points between the original images by combining their characteristics during image stitching, so it can reduce the calculation time without affecting the quality. Also, the results will not be influenced by the image stitching seam, which can reduce the complexity of the algorithm. Several experimental results are provided in this article to demonstrate that the proposed method can reduce the calculation time without affecting the quality of image stitching and have better robustness than the current method in use.

Characterisation of joint properties through spatial mapping of cracks in fatigue specimens, extracted from the linearly friction welded steel coupon

Linear friction welding (LFW) is widely used for the fabrication of high-value components in the aero-industry. The versatility of use and high integrity of joints produced by LFW, suggest that this technology could contribute greatly to sustainable engineering of the future. For example, in near-net-shape manufacturing, LFW is predicted to offer a substantial reduction in material waste and processing time, in addition to offering the capability of bonding material pairs currently difficult to fusion weld. LFW of steel components is seldom researched in published literature, with little information available on fatigue strength and fracture behaviour of such joints. This may hamper the confidence needed to popularise LFW in wider industry. Thusly, authors conducted a discovery-oriented fatigue study, followed by metallography and micro-hardness tests, conducted on a 100Cr steel LFW coupon and corresponding batch of parent material. The weld coupon was dissected into a set of fatigue specimens, to allow mapping of the joint's internal properties through individual fatigue estimates and fracture morphology of each specimen in the set. A prototype fatigue machine was constructed, designed to deliver independent displacement and force controlled bending or torsional loadings, representing complex real-world conditions more accurately than common, uniaxial tests. The hour-glass shape of fatigue specimens allowed for targeting strictly the weld region, inducing fracture at the weakest material section, which was found to lay far outside the immediate contact interface. Referencing distances from fracture points to the theoretical weld interface plane in each fatigue trial, resulted in discovery of a macroscopic pattern. Interpolated to a crack initiation plane, this pattern is proposed to reflect the heat-affected zone, across the greater LFW coupon. Authors also studied crack paths and suggest a link between fracture direction, residual stresses in joint and angular orientation of the crack initiation plane in respect to the theoretical weld interface.

Mechanical performance and environmental impacts of self-compacting concrete with recycled demolished concrete blocks

In the past decades, reducing and recycling of construction and demolition waste (CDW) has been gaining an increasing attention in infrastructure development for both economic and environmental benefits. Recycled concrete aggregate (RCA) from demolishing of old structures is one major type of construction waste. Many researchers have studied the resultant physical and mechanical properties of concrete made with RCAs. In a continued effort of material recycling, this study explored the feasibility of using demolished concrete blocks (DCBs) in the production of concrete. Compared to conventional RCA, DCB is characterized by its relatively large size. In this study, DCBs were used with self-compacting concrete (SCC) to produce DCB-filled-concrete (DCBFC) since SCC can easily fill up the voids between DCBs. The compression tests conducted in the study indicated that concrete with only SCC and DCB had low compressive strength – which, however, exhibited an increasing trend as the DCB was replaced with natural rocks. Among the three types of DCBFC, DCBFC60 (i.e., 60% DCBs with 40% natural rocks by mass) exhibited the highest compressive strength that was comparable to the compressive strength of SCC. Additionally, three reinforced concrete (RC) beams made of SCC with different proportions of DCBs and natural rocks were tested in the bending loading mode to characterize their flexural strength and cracking resistance potential. While the bending behavior was similar to normal RC beams, the yielding and ultimate moments of SCC beams increased with an increase in the natural rock content, which was consistent with the test results from the compression tests. Similarly, crack mapping also indicated an increase in more tortuous and secondary cracks with an increase in the ratio of the natural rock content. Life cycle assessment also yielded significant environmental benefits with the use of DCBs over conventional concrete.

Robust pixel-wise concrete crack segmentation and properties retrieval using image patches

Crack identification is an essential task in periodic inspection and maintenance of buildings. The application of deep learning based computer vision techniques is increasingly popular, but suffer from challenges of insufficient performance on highly diverse field inspection scenarios as well as a requirement for large amounts of labeled training data. To address these limitations, this paper proposes a robust crack segmentation approach using image patches to detect and support further accurate retrieval of crack properties for integrity assessment. In the proposed approach, a local region-based active contour model is integrated with a convolution neural network and several post-processing morphological operations to derive a segmented crack map. Experimental validation shows significant improvement in terms of accuracy and robustness over previous work. Data labeling requirement is also comparatively lower. This paper enhances the current concrete inspection process, and lays the foundation for more data efficient methods of crack segmentation to be explored.

Comparison of crack segmentation using digital image correlation measurements and deep learning

Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on discontinuous fields. Secondly, detected crack patterns can serve as inputs for predictive empirical models to obtain the level of damage to a body. The aim of this paper is to compare the performance of two approaches for crack segmentation on grayscale images acquired from two experimental campaigns on stone masonry walls. In the first approach, a threshold is applied to the maximum principal strain map calculated using post-processed DIC results. In the second approach, a deep convolutional neural network is used. The two methods are compared in terms of standard segmentation criteria, namely precision, dice coefficient and sensitivity. It is shown that the precision and dice coefficient obtained from the deep learning approach are much higher than those obtained from the threshold method (by almost 47% and 34%, respectively). However, the sensitivity computed from the deep learning method is slightly (~4%) lower than the threshold method. These results show that the deep learning method can better preserve the geometry of detected crack patterns, and the prediction in terms of pixels belonging to a crack is finally more accurate than the threshold method.

Influence of link spacing on concrete shear capacity: experimental investigations and finite element studies

This paper investigates the influence of varying the spacing of shear reinforcement on the response of six geometrically identical reinforced concrete beams. Each beam was designed with differing longitudinal and transverse reinforcement configurations to provide improved understanding of the importance of providing sufficient shear reinforcement following guidance provided in Eurocode 2. Three different cases of varying transverse reinforcements were tested: one without the presence of shear reinforcement, one with shear reinforcement non-compliant to the design provisions and one where the design of the beams fully complies with the design guidance. In order to verify the experimental results, an open source digital image correlation software was utilised to allow for a computational crack mapping, along with the use of nonlinear finite element analysis software to enable a more thorough investigation of the individual beam response. The results obtained highlighted that shear links non-compliant to Eurocode 2 consequently led to unpredictable modes of failure, whereas beams containing shear links at the required amount and spacings displayed significant improvements in the mode of failure and ductility. Beams with the omittance of shear links resulted in the lowest load capacity, due to the sudden, brittle shear failure.

Investigation of Cracks Observed on a Skewed Bridge Constructed Using Self-Propelled Modular Transporters

This study investigates full-depth deck cracks observed on a skewed bridge, which was constructed using self-propelled modular transporters (SPMTs). To investigate the causes, three main factors associated with structural design, material design, and construction are reviewed. Specifically, the bridge performance under thermal and construction loading is investigated by conducting a nonlinear finite-element analysis. The analysis parameters include bridge deck geometry, boundary conditions, bearing details, and skew angles. The loading conditions mainly include thermal and differential deck movements induced during an SPMT move. The results are compared to a crack map created from two site visits. It is concluded that skewed decks are more susceptible to cracking than straight decks due to an asymmetric expansion and contraction. Additionally, semi-integral abutment designs in a skewed bridge increase the extent of cracking in the skewed corners of the abutment ends. It is recommended that a semi-integral abutment design be carefully considered when designing a skewed bridge and that bearing and expansion joint details need careful attention for accelerated bridge construction.

Digital Image Correlation for Cement-based Materials and Structural Concrete Testing

This paper presents the application of a low-cost digital image correlation (DIC) system for automated crack mapping. The system employed makes use of a conventional digital camera, a remote image recording controller, and an open-source digital image correlation MATLAB software Ncorr. To showcase the potential of the proposed system, two case studies involving general structural and material testing were undertaken. In the first series, the DIC system was used to study the fracture processes in a reinforced concrete beam and to investigate the influence of key input parameters on the spatial quality of strain maps obtained from the DIC analysis. In the second series, the application of the DIC method was expanded to assist in the study of complex multiple micro-crack formations in ductile cement composite testing. It is shown that the strain maps obtained from the DIC technique have a close resemblance to the actual crack patterns