Cracks In Concrete Structures Research Articles

The Smart Nervous System for Cracked Concrete Structures: Theory, Design, Research, and Field Proof of Monolithic DFOS-Based Sensors.

The article presents research on the performance of composite and monolithic sensors for distributed fibre optic sensing (DFOS). The introduction summarises the design of the sensors and the theoretical justification for such an approach. Lessons learned during monitoring cracked concrete are summarised to highlight what features of the DFOS tools are the most favourable from the crack analysis point of view. Later, the results from full-size laboratory concrete specimens working in a cracked state were presented and discussed in reference to conventional layered sensing cables. The research aimed to compare monolithic sensors and layered cables embedded in the same reinforced concrete elements, which is the main novelty. The performance of each DFOS nondestructive tool was investigated in the close vicinity of the cracks-both the new ones, opening within the tension zone, and the existing ones, closing within the compression zone. The qualitative (detection) and quantitative (widths estimation) crack analyses were performed and discussed. Finally, the examples of actual applications within concrete structures, including bridges, are presented with some examples of in situ results.

Effect of Superabsorbent Polymers on the Self-Healing Properties of Pre-Damaged Concrete

Cracks in concrete structures reduce bearing capacity, durability, and safety. This paper reveals the effect of superabsorbent polymers (SAP) on the self-healing property of pre-damaged concrete based on mechanical properties tests, mercury intrusion porosimetry, scanning electron microscopy, and energy dispersive spectrometer. The experimental results show that SAP reduces the mechanical properties of the SAP-modified concrete under the same W/C ratio, but a small amount of SAP can improve the later strength under the 0.35 W/C ratio. The addition of SAP increased the volume of small capillary pores (<20 μm) and large capillary pores (>100 μm) and significantly reduced the former with an increase in age. The SEM-EDS analysis indicates that the water released by SAP promotes the formation of C-S-H gel. AFt in the cracks is the main reason for self-healing, and the addition of CO2 to produce calcium carbonate is another one. Furthermore, this study finds that SAP-modified concrete has the best self-healing effect and the densest structure when the W/C ratio is 0.35 and the SAP content is 0.25%. The self-healing performance makes the SAP-modified concrete have broad application prospects.

Strain patterns of short span reinforced concrete beams under flexural loading: A comparison between distributed sensing and concrete damaged plasticity modelling

Strains beyond the elastic limit of concrete cause cracks in concrete structures. This study analysed distributed optical fibre sensor (DOFS) network strain data to identify excessive strain readings, locations and strain patterns of four reinforced concrete (RC) beams. The strain variation inside the RC beams was investigated using a concrete damaged plasticity (CDP) based finite element analysis (FEA) model. The measured bottom surface and bottom rebar strain were considered for investigation. A three-point bending arrangement was used for the loading, and an optical backscatter reflectometer was used to measure the strain. By visual inspections, it has been found that the onset of hairline cracks at the locations of the higher strain readings. The maximum load applied was 16 kN and has not shown any yielding in strain reading acquired from bottom rebars. FEA results showed admissible agreement with the measured lower surface strain and lower rebar strain. Therefore, when developing a robust SHM system, a CDP-based FEA simulation is a valuable tool for data mining of strain data. Furthermore, the strain data extracted from CDP models can be used to train artificial intelligence based SHM models.

Numerical simulation of effective diffusivity in concrete with random microcracks

Cracks in concrete structures can provide preferential transport pathways for the ingress of aggressive media. Therefore, it is important to understand the relationship between cracks and the durability properties of concrete. Numerical modeling is an efficient and practical means of investigating the effect of cracks, especially microcracks, on the diffusivity of concrete. In this study, a modeling scheme was developed to predict the effective diffusivity of cracked concrete. Numerical samples containing randomly overlapping and nonoverlapping microcracks were generated, and the effects of microcrack geometrical parameters were investigated. The results showed a critical area fraction of microcracks in the diffusion process, which was related to the aspect ratio, orientation, and tortuosity of microcracks. The effective diffusivity was found to be closely related to the crack orientation and tortuosity. Moreover, the tortuosity of nonoverlapping microcracks was observed to be greater than that of overlapping microcracks at the same area fraction.

Investigation on electrochemical repair of reinforced concrete structure cracks and their bonding performance

The durability failure of reinforced concrete structures will occur under chloride ion erosion. Electrochemical repair technology can effectively improve the durability of reinforced concrete structures and prolong the service life of structures. However, the electrochemical repair technology may also cause hydrogen embrittlement and porosity change in reinforced concrete structures. The electrochemical test of reinforced concrete block with precast crack was carried out in this work. The results show that the bond loss of reinforced concrete is small when the electric flux is small. The electrochemical repair technique without controlling the electrical parameters results in a significant decrease in the bonding strength. When 5 A/m2 current was used to carry out constant-current electrification for 4 weeks, the maximum loss of bonding force of the specimen was up to 61.2 %.

A Critical Review of Existing Test-Methods for External Sulfate Attack.

External sulfate attack (ESA) of cementitious materials has been studied worldwide for a very long time. This physical/chemical interaction between sulfate ions and the cement hardened elements affects the long-term durability of concrete structures: cracking, spalling or strength loss of concrete structures. To study these damaging phenomena, some standardized and non-standardized accelerated aging tests are used to evaluate the performance of cements in sulfate-rich environments. However, these existing methods do not adequately predict field performance and some shortcomings or deficiencies still exist: change of degradation mechanisms when using high concentrations of sulfate, variable boundary conditions and small specimens compared to the real concrete structures. In this work, a critical review of some existing test methods and foreign national standard methods for ESA are presented, analyzed, and discussed. This results in some proposed recommendations for improving these methods to meet the needs of structure managers.

Monitoring depth and width of cracks in underwater concrete structures using embedded smart aggregates

This work devises a method to monitor the width and depth of cracks in underwater concrete structures employing active sensing technology based on embedded smart aggregates (SAs). To this end, an experimental platform is designed. Two SAs are pre-embedded in the underwater concrete beam to enable the excitation and acquisition of signals. The time-domain method analyzes the sensor signals, and wavelet packet decomposition is utilized to calculate the sensor signal energy. The sensor signal energy gradually decays with an increase in the depth of cracks in the concrete beams. However, as the width of the cracks increases, the main channel for stress wave propagation changes, causing the sensor signal energy to first decay and then increase. This paper also defines a damage index based on the depth of cracks in the underwater concrete beams to describe the crack depth damage.

Nonlinear ultrasonic evaluation of damaged concrete based on mixed harmonic generation

Nonlinear wave mixing provides a potential method to evaluate localized micro-damage in structures. The wave mixing technique has some unique advantages over the nonlinear ultrasonic technique based on the higher harmonic generation, such as frequency selectivity, which can separate harmonic components generated due to instrumentation. In this paper, we numerically studied the frequency mixing response induced by the nonlinear interaction of two primary Rayleigh waves in a concrete material. The nonlinearity considered in the present study is due to crack-wave interaction, which is often called contact acoustic nonlinearity (CAN). A limited number of experimental studies were conducted on concrete prisms to evaluate surface cracks. Both numerical and experimental results illustrate the generation of the second- and third-order combined harmonics, which can be utilized to detect and localize subsurface cracks in concrete structures. The newly discovered third-order combined harmonics, which result from the mutual interaction between the primary Rayleigh waves and their induced second-harmonic waves, can be exploited to characterize micro-damage in various applications.

Bond-based Cosserat peridynamic simulation of early-age fracture in cement-based materials

In this study, a coupled chemo-thermo-mechanical bond-based Cosserat peridynamic model is proposed for early-age concrete. The model can consider effect of cement particle size and relieve Poisson's ratio limitation, which is suitable to simulate the initiation and propagation of crack in early-age concrete structures. Numerical simulations mainly focus on the temperature evolution, variation of hydration degree, and mechanical behavior for early-age concrete. And they verify the importance of value of Poisson's ratio and effect of cement particle size on mechanical properties and crack evolution of early-age concrete structures. Numerical results show the great potential of the proposed model in modeling fracture behaviors of early-age concrete.

Automated wall‐climbing robot for concrete construction inspection

AbstractHuman‐made concrete structures require cutting‐edge inspection tools to ensure the quality of the construction to meet the applicable building codes and to maintain the sustainability of the aging infrastructure. This paper introduces a wall‐climbing robot for metric concrete inspection that can reach difficult‐to‐access locations with a close‐up view for visual data collection and real‐time flaws detection and localization. The wall‐climbing robot is able to detect concrete surface flaws (i.e., cracks and spalls) and produce a defect‐highlighted 3D model with extracted location clues and metric measurements. The system encompasses four modules, including a data collection module to capture RGB‐D frames and inertial measurement unit data, a visual–inertial navigation system module to generate pose‐coupled keyframes, a deep neural network module (namely InspectionNet) to classify each pixel into three classes (background, crack, and spall), and a semantic reconstruction module to integrate per‐frame measurement into a global volumetric model with defects highlighted. We found that commercial RGB‐D camera output depth is noisy with holes, and a Gussian‐Bilateral filter for depth completion is introduced to inpaint the depth image. The method achieves the state‐of‐the‐art depth completion accuracy even with large holes. Based on the semantic mesh, we introduce a coherent defect metric evaluation approach to compute the metric measurement of crack and spall area (e.g., length, width, area, and depth). Field experiments on a concrete bridge demonstrate that our wall‐climbing robot is able to operate on a rough surface and can cross over shallow gaps. The robot is capable to detect and measure surface flaws under low illuminated environments and texture‐less environments. Besides the robot system, we create the first publicly accessible concrete structure spalls and cracks data set that includes 820 labeled images and over 10,000 field‐collected images and release it to the research community.

Vision-based concrete crack detection using a hybrid framework considering noise effect

Diagnosing surface cracks of concrete structures has been a critical aspect of assessing structural integrity. Existing diagnosis technologies are time-consuming, subjective, and heavily dependent on the experiences of inspectors, which leads to low detection accuracy. This paper aims to resolve these challenges by proposing a vision-based automated method for surface condition identification of concrete structures, consisting of state-of-the-art pre-trained convolutional neural networks (CNNs), transfer learning, and decision-level image fusion. For this purpose, a total of 41,780 image patches of various concrete surfaces are generated for the development and validation of the proposed method. Each pre-trained CNN is employed to establish the predictive model for the initial diagnosis of surface conditions via transfer learning. Since different CNNs may generate conflicting results due to differences in network architectures, a modified Dempster-Shafer (D-S) algorithm is designed to conduct decision-level image fusion to improve the crack detection accuracy. The superiority of the proposed method is validated via the comparison with single CNN models. The robustness of the proposed method is also verified using the images polluted with various types and intensities of noise, with satisfactory outcomes. Finally, this hybridised approach is applied to the analysis of images of concrete structures captured in the field, through an exhaust search-based scanning window. The results show that it is capable of accurately identifying the crack profile with wrong predictions of limited areas, demonstrating its potential in practical applications.

Localization of Cracks in Concrete Structures Using an Unmanned Aerial Vehicle.

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24–84 mm and 8–48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95–91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

Water Transport through Cracked Concrete Structures—Effect of Mixture Proportion on Separating Crack Geometry and Permeability

The increase in fluid transport due to separating cracks can lead to significant deterioration in the durability of reinforced concrete structures. Besides reinforcement and stress state, concrete mixture proportion has a significant effect on crack geometry. In this study, we investigated concrete mixtures with different aggregate size and shape, aggregate gradation, cement type and water-to-cement ratio with regard to crack geometry and resulting water permeation. Besides surface-crack width and length, we determined inner-crack width variation over depth and tortuosity by X-ray micro-computed tomography. Furthermore, we conducted permeation tests for each specimen. Among the mixture components tested, aggregates have the strongest effect on crack geometry and flow rate. Increasing aggregate size results in increasing tortuosity and decreasing flow rate. Furthermore, the replacement of round with angular aggregates results in slightly higher flow rates for a given crack width.

Natural Resources Exploitation in Sulfate-Resisting Portland Cement Manufacturing: Towards Quality Improvement and Reduction of Environmental Impact.

Sulfate-resisting (SR) Portland cement is commonly used in building works to improve concrete’s durability against external sulfate attack. This attack is considered a very serious chemical aggression that causes damage and cracking of concrete structures. These special cements have a very particular mineralogical composition, C3A ≤ 3% and (2C3A + C4AF) ≤ 20%, which makes the cementitious matrix resistant to sulfate attack. This kind of product is very difficult to manufacture since low alumina (C3A) necessitates the use of a high kiln temperature in order to keep a sufficient liquid phase necessary to maintain the stability of the cement manufacturing process. In this context, this study aims to optimize SR Portland cement raw meals using natural materials collected from different regions in Tunisia, mainly ordinary limestone, siliceous limestone, black marl, grey marl, iron ore, and natural fluorapatite. The collected specimens were characterized by an X-ray fluorescence spectrometer in order to determine its elemental chemical composition. The optimization of the SR Portland cement raw meal combinations was done by means of a calculation tool based on the chemical composition of each used raw material and the variation of burning modules (LSF, SIM, and ALM). It has been found that natural fluorapatite integration (0%–15%) in raw mix preparation leads to the raw meals required for the SR Portland cement standard (C3A ≤ 3% et 2 C3A + C4AF ≤ 20%). Moreover, it was shown that the estimated SR raw meals ensure the cement manufacturing process stability (acceptable burning modules “LSF = 100; SIM = 3; ALM = 0.91 and sufficient liquid phase) and decrease the CO2 emissions in cement production.

CNN Based on Transfer Learning Models Using Data Augmentation and Transformation for Detection of Concrete Crack

Cracks in concrete cause initial structural damage to civil infrastructures such as buildings, bridges, and highways, which in turn causes further damage and is thus regarded as a serious safety concern. Early detection of it can assist in preventing further damage and can enable safety in advance by avoiding any possible accident caused while using those infrastructures. Machine learning-based detection is gaining favor over time-consuming classical detection approaches that can only fulfill the objective of early detection. To identify concrete surface cracks from images, this research developed a transfer learning approach (TL) based on Convolutional Neural Networks (CNN). This work employs the transfer learning strategy by leveraging four existing deep learning (DL) models named VGG16, ResNet18, DenseNet161, and AlexNet with pre-trained (trained on ImageNet) weights. To validate the performance of each model, four performance indicators are used: accuracy, recall, precision, and F1-score. Using the publicly available CCIC dataset, the suggested technique on AlexNet outperforms existing models with a testing accuracy of 99.90%, precision of 99.92%, recall of 99.80%, and F1-score of 99.86% for crack class. Our approach is further validated by using an external dataset, BWCI, available on Kaggle. Using BWCI, models VGG16, ResNet18, DenseNet161, and AlexNet achieved the accuracy of 99.90%, 99.60%, 99.80%, and 99.90% respectively. This proposed transfer learning-based method, which is based on the CNN method, is demonstrated to be more effective at detecting cracks in concrete structures and is also applicable to other detection tasks.

Comparative Study of Various Image Processing Techniques to Detect Cracks in Concrete Structures

Studies in the past have used image processing techniques on quality crack images obtained from open-source databases. The results obtained by using these techniques on such images yielded convincing results. But in an ideal situation, the environment in which the image is captured is often complex and dynamic, which does not guarantee good quality crack images. This study aims to compare the performance of four different image processing techniques used to detect concrete cracks when any crack image taken from a standard camera is used. The image processing techniques that have been considered are namely Wavelet transform, Min-Max Gray Level Discrimination, Super Pixel Segmentation and Nine Element Matrix method. The Otsu segmentation method is used to binarize the image after the images are preprocessed using these techniques, followed by the use of morphological operations to remove noisy spots. It was observed that Min-Max Gray Level Discrimination performed relatively better than the other techniques.

Automatic segmentation and quantification of global cracks in concrete structures based on deep learning

Automatic detection and quantification of all cracks on the surface of concrete structures are of great importance for evaluating structural safety and damage. Firstly, based on the electric driving platform, a method of close-range scanning and shooting is presented to obtain high-resolution panoramas of the surface of concrete structures. Then, based on convolutional neural networks, an automatic segmentation method suitable for panorama cracks is proposed. Without considering complex scenes, the method achieved crack segmentation at 51.34 frames per second for 512 × 512 images, with 97.79% mean intersection-over-union. Finally, an automatic quantification method suitable for panorama cracks is given through proposed crack matching and property calculation methods. The properties and corresponding distribution of panorama cracks, including branch cracks, are calculated automatically. The average relative error of the maximum crack width value calculated by the proposed crack width calculation method is merely 3.87% (22.57 μm), indicating high accuracy.

Detecting cracks in concrete structures with the baseline model of the visual characteristics of images

AbstractA method based on the baseline model of the visual characteristics of images (BMVCI) is proposed to detect cracks in concrete structures. BMVCI refers to the model, which consists of images of the noncrack areas of a concrete structure with cracks or images of the noncrack state of a concrete structure. Compared with the performance of edge detection (ED) methods for detecting cracks in concrete structures, this baseline model expands the quasi‐distance between the edges of cracks and the image background; thus, the crack detection accuracy is effectively improved. Additionally, the discriminative threshold of cracks is quantitatively determined with BMVCI, which avoids the influence of artificial interference when determining the abovementioned threshold used for ED methods. Meanwhile, compared with the methods based on artificial intelligence, such as deep learning (DL), the calculating efficiency of the proposed method is higher because the proposed method converts the high‐dimensional image data into low‐dimensional digital features for training. With the same small size set of training samples, the accuracy of the crack detection of the proposed method is higher than that of the methods based on the framework of DL. In this study, Gaussian convolution is applied to generate the visual characteristics of images, and then a kernel principal component analysis‐based method is implemented to establish the BMVCI. The basic idea of novelty detection is applied to detect cracks in concrete structures. Finally, an experiment on concrete structures is designed and applied to demonstrate the effectiveness of the proposed method.

Crack Texture Feature Identification of Fiber Reinforced Concrete Based on Deep Learning.

Structural cracks in concrete have a significant influence on structural safety, so it is necessary to detect and monitor concrete cracks. Deep learning is a powerful tool for detecting cracks in concrete structures. However, it requires a large quantity of training samples and is costly in terms of computational time. In order to solve these difficulties, a deep learning target detection framework combining texture features with concrete crack data is proposed. Texture features and pre-processed concrete data are merged to increase the number of feature channels in order to reduce the demand of training samples for the model and improve training speed. With this framework, concrete crack detection can be realized even with a limited number of samples. To accomplish this aim, self-made steel fiber reinforced concrete crack data is used for comparison between our framework and those without texture feature mergence or pre-processed concrete data. The experimental results show that the number of parameters that need to be fitted in the model training and training time can be correspondingly reduced and the detection accuracy can also be improved.

Discrete-element modelling of concrete behaviour under uniaxial compressive test

This paper presents a three-dimensional (3D) simulation of the concrete behaviour in a uniaxial compressive test using discrete-element modelling (DEM). The aim of this paper is to validate the numerical model developed and to study the cracking initiation and failure process in order to better understand the fracture behaviour of concrete. The particles were distributed using an algorithm that is based on sieve test analysis. The parameters were set up in order to validate the numerical model with the experimental result. It was observed that the 3D model is in line with the laboratory test in term of stress-strain response and macroscopic cracks development. Once the bond between the spheres was broken, it led to the formation of microscopic cracks, which were not visible in laboratory tests. From the observation recorded during the testing, it is clear that DEM is capable of capturing concrete behaviour both quantitatively and qualitatively. At the yielding point in the concrete the strain energy is released in the form of kinetic energy, frictional slip energy, energy of dashpot and local damping. Hence, it can be concluded that DEM can be used to study the random nature of the cracking and fracturing of concrete structures.