Concrete Defects Research Articles

Defect detection using spatial spectral entropy for noncontact acoustic inspection method

Inspection of concrete structures is important for maintaining social infrastructure such as tunnels and viaducts. Therefore, noncontact acoustic inspection method using acoustic irradiation induced vibration and a scanning laser Doppler vibrometer (SLDV) has been devised. Since this method uses flexural resonance, it can be said to be an alternative method for a hammering test. Using our method, internal defects of concrete can be detected up to a depth of about 10 cm from the surface of concrete, even at a distance of about 30 m. However, in a closed space such as an underground cavity, the signal-to-noise (S/N) ratio decreases due to reverberation from the surroundings, making it difficult to detect defects compared to open spaces such as viaducts. This problem can be solved by using spatial spectral entropy (SSE), which extends the concept of spectral entropy to the two-dimensional space of the measurement plane. In other words, by SSE analysis, the resonance frequency of both the defect and the SLDV can be detected and separated because of the difference in the spatial distribution characteristics of the vibration velocity spectrum for each the frequency. Therefore, it is possible to detect a defective part even in a place where the S/N ratio is not sufficient.

YOLOv8-CDD: an improved concrete defect detection method combined CNN with transformer

Abstract Concrete defect detection is a critical task to ensure the safe and stable operation of concrete infrastructure. In order to effectively solve the problems of low efficiency, high cost and poor performance of existing methods, a high-precision concrete defect detection method YOLOv8-CDD (Concrete Defect Detection) combining convolutional neural network and transformer is proposed in this paper. Firstly, based on the features with a large span of concrete defect features, a bot-transformer module that can effectively extract the global information of defect features is proposed to improve the network’s ability to extract global features. Secondly, in order to further strengthen the interaction between defect feature channels and spatial information, a convolutional triplet attention module is introduced into the feature enhancement network to effectively integrate the information of different dimensions of defect features and improve the model detection accuracy. Additionally, in order to enhance the learning of samples with different degrees of difficulty, the introduction of Focaler-CIoU instead of the original boundary regression loss function can optimize the model training process. Finally, the dataset was collected and organized in concrete scenarios from bridge towers, dams, and tunnel corridors, and our method achieved 0.898 average precision, 0.893 average recall, 0.031 average FPR, 0.895 average F1 score, 0.929 mAP50, and 0.731 mAP50:95 on the dataset. The experimental results show that the proposed method achieves the best performance in concrete defect detection.

Study on non-destructive visualization identification of concrete internal cracks based on SH array ultrasound

Ultrasonic nondestructive testing (NDT) technology was one of the most frequently used and fastest-developing NDT techniques in detection field. This method had high detection sensitivity, more accurate defect localization, and was harmless to the human body. With the development of ultrasonic testing technology, modern ultrasonic imaging technology generally had automatic data acquisition and processing functions, which allowed for intuitive and clear recognition of internal defects by the human eyes. The overall purpose of this study was to visualize the cracks in concrete by synthetic aperture focusing technique (SAFT) and full-waveform inversion (FWI) algorithms on the basis of ultrasonic nondestructive testing technology. At present, there have been studies on the use of ultrasonic instruments to detect the internal defects of concrete. However, the accuracy and authenticity of this method have not been compared in previous studies. Therefore, in this experiment, four concrete specimens with different buried depths, angles, shapes, and thicknesses of cracks were designed. Based on SH ultrasonic wave NDT technology, two types of operations were performed on the collected ultrasonic wave data: one was the application of SAFT method, and the other was the first application of FWI method for detecting internal cracks in concrete. The study found that both algorithms were most accurate in determining the burial depth of cracks, with error results not exceeding 10 %. They also showed good recognition effects for the angle, shape, and thickness of the buried cracks. This proved that both identification methods had the capability to recognize internal cracks. The Introview software bundled with the ultrasonic instrument could identify cracks using SAFT directly and quickly. When FWI was used with MATLAB computation, the results were more precise and intuitive. The overall purpose of this study was to detect cracks in concrete by SAFT and FWI algorithms on the basis of ultrasonic nondestructive testing technology. Combining the two crack identification techniques could be of great significance for ensuring the long-term safety and stability of concrete structures, providing strong support for structural maintenance and reinforcement.

Condition assessment of concrete structures using automated crack detection method for different concrete surface types based on image processing

In the inspection and diagnosis of concrete construction, crack detection is highly recommended in the earliest phases to prevent any potential risks later. However, the flaws in concrete surfaces cannot be reliably and effectively identified using traditional crack detection techniques. The suggested algorithm is a supportive tool for agents or authorities to use in crack detection mechanisms to monitor and assess the current condition of buildings or bridges. The researchers aim to establish an intelligent model for automatic crack detection on different concrete surfaces based on image processing technology. Three different concrete surfaces—bridge decks, walls, and concrete cubes—are used to test the model. A subset of the public dataset of bridge decks and walls from SDNET (2018) and 150*150*150 mm of concrete cubes taken from the material laboratory of the faculty of engineering at Ain Shams University are applied to the model. The model F1-score measures are 98.87%, 97.43%, and 74.11% for detecting cracks in bridges, walls, and concrete cubes, respectively. The validation of the applicability of the suggested novel approach is based on a comparison with recent methods for crack recognition. The contribution of this study is that it could be applied to detect cracks efficiently on three different types of concrete surfaces, including uneven concrete surfaces, random noise, voids, dents, colour changes, and stain marks. The proposed method is transparent in its workflow and has a lower computational cost compared with deep learning frameworks. Thus, the outcomes of this model demonstrate its effectiveness in concrete defect field investigation.

Ambient vibration-based quantitative assessment on tunnel lining defect using laser Doppler vibrometer

Automated and information-based inspection methods are urgently needed for the tunnel’s daily maintenance as the number of tunnels continues to increase. A remotely non-contact detection method is proposed using a laser Doppler vibrometer based on ambient vibration. It introduces a dimensionless quantitative index called Fundamental Frequency Deterioration Degree (FFDD) based on the fundamental frequency. The FFDD is developed from damage mechanics and vibration mechanics, enabling a more accurate assessment. To validate this method, laboratory ambient vibration detection tests were conducted on cubic concrete samples considering strength, cavity, and cracks under compression. The PDV-100 laser Doppler vibrometer was used for ambient vibration collection. Results demonstrate a strong correlation between concrete defects and the fundamental frequency. The relationship between FFDD and concrete defects validates its accuracy. The method was applied in situ to detect and assess grouting defects in a newly constructed metro tunnel, further validating its use in real-world scenarios.

Concrete Defect Localization Based on Multilevel Convolutional Neural Networks.

Concrete structures frequently manifest diverse defects throughout their manufacturing and usage processes due to factors such as design, construction, environmental conditions and distress mechanisms. In this paper, a multilevel convolutional neural network (CNN) combined with array ultrasonic testing (AUT) is proposed for identifying the locations of hole defects in concrete structures. By refining the detection area layer by layer, AUT is used to collect ultrasonic signals containing hole defect information, and the original echo signal is input to CNN for the classification of hole locations. The advantage of the proposed method is that the corresponding defect location information can be obtained directly from the input ultrasonic signal without manual discrimination. It effectively addresses the issue of traditional methods being insufficiently accurate when dealing with complex structures or hidden defects. The analysis process is as follows. First, COMSOL-Multiphysics finite element software is utilized to simulate the AUT detection process and generate a large amount of ultrasonic echo data. Next, the extracted signal data are trained and learned using the proposed multilevel CNN approach to achieve progressive localization of internal structural defects. Afterwards, a comparative analysis is conducted between the proposed multilevel CNN method and traditional CNN approaches. The results show that the defect localization accuracy of the proposed multilevel CNN approach improved from 85.38% to 95.27% compared to traditional CNN methods. Furthermore, the computation time required for this process is reduced, indicating that the method not only achieves higher recognition precision but also operates with greater efficiency. Finally, a simple experimental verification is conducted; the results show that this method has strong robustness in recognizing noisy ultrasonic signals, provides effective solutions, and can be used as a reference for future defect detection.

Effect of light source wavelength on surface defect imaging in deep-water concrete dams

This study explores the relationship between surface defect imaging in deep-water concrete dams and the wavelength of the light source, aiming to improve concrete defect image quality by varying the wavelength. The underwater optical imaging model of concrete dam surfaces was developed by applying the principle of object reflection and the underwater optical propagation model. This model reflects the effect of different light wavelengths on underwater concrete defect imaging. Through a self-designed underwater optical darkroom test platform, defect image acquisition tests were conducted under various color light conditions. The results reveal the significant influence of light source wavelength on images captured in both air and water. Furthermore, under the same initial illuminance, blue light significantly enhanced the average gray value, sharpness, contrast, and the number of feature points in underwater defect images, exhibiting increments of 2.97 times, 0.48 times, 2.70 times, and 2.95 times, respectively, compared to white light. These findings can serve as a reference for the selection of color-light for the non-destructive testing of surface defects in actual underwater concrete dams.

A deep transfer learning-based algorithm for concrete surface defect detection

The integrity and safety of concrete structures are crucial in modern civil engineering. Traditional image-defect detection methods are significantly affected by light and noise. Methods based on deep learning, particularly convolutional neural networks (CNNs), offer new solutions for automated defect detection. This paper introduces a concrete surface defect detection algorithm based on deep transfer learning. Our model utilizes the ResNet50 architecture as its backbone and incorporates Atrous Spatial Pyramid Pooling (ASPP) to adapt to the diversity of defects and enhance recognition accuracy. In addition, the use of a self-attention mechanism effectively enhances the model’s focus on and analysis of key defect areas, improving its ability to perceive and select features for defects of various sizes, aiding precise localization and classification under complex conditions. Image preprocessing techniques such as resizing, conversion to grayscale, histogram equalization, and noise addition further enhance the robustness of the model in diverse real-world scenarios. On the publicly available COncrete DEfect BRidge IMage Dataset, our model achieved a mean average precision (mAP@0.5) of 0.90, showing a 3.4% improvement over the current best baseline method. Through transfer learning, it reached 0.92 on our custom dataset, significantly outperforming existing methods. These results demonstrate the effectiveness and superiority of the proposed model for detecting concrete defects. Additionally, we evaluated the impact of different architectures on model performance. Compared to traditional models using the VGG16 as the backbone, the ResNet50 architecture used in this study reduces parameter count by 5.41 times while improving performance by 6.1%.

Mechanical properties of steel fiber-reinforced geopolymer concrete after high temperature exposure

Adding a certain amount of steel fibers can overcome the brittleness defects in geopolymer concrete and improve the mechanical properties, which has a broader application prospect. However, the research on the material properties of steel fiber-reinforced geopolymer concrete (SFGPC) after high temperature exposure is still very limited. Therefore, tests on 114 cubes and 57 prisms after exposure to high temperatures were conducted in this paper. The main variables were temperature (20 °C, 200 °C, 400 °C, 600 °C, and 800 °C), steel fiber volume content (0, 1 %, and 2 %), and concrete type (SFGPC and steel fiber-reinforced ordinary Portland cement concrete (SFOPC)). The results indicated that SFGPC with a steel fiber content of 1 % at 800 °C experienced a 53.6 % reduction in compressive strength, a 58 % reduction in splitting tensile strength, a 93.9 % reduction in elastic modulus, a 45.9 % reduction in toughness index, and a 274.5 % increase in peak strain, compared with room temperature. The 1 % fiber content enhanced the compressive strength by 10.3 %−36.5 %, the splitting tensile strength by more than 16 %, the peak strain by 9.6 %−32.7 %, and the toughness index by 24.4 %−64.6 % compared to the absence of fiber content. Based on the room temperature model, a constitutive model of SFGPC under uniaxial compression after high temperatures was developed.

Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete.

The search for structural and microstructural defects using simple human vision is associated with significant errors in determining voids, large pores, and violations of the integrity and compactness of particle packing in the micro- and macrostructure of concrete. Computer vision methods, in particular convolutional neural networks, have proven to be reliable tools for the automatic detection of defects during visual inspection of building structures. The study's objective is to create and compare computer vision algorithms that use convolutional neural networks to identify and analyze damaged sections in concrete samples from different structures. Networks of the following architectures were selected for operation: U-Net, LinkNet, and PSPNet. The analyzed images are photos of concrete samples obtained by laboratory tests to assess the quality in terms of the defection of the integrity and compactness of the structure. During the implementation process, changes in quality metrics such as macro-averaged precision, recall, and F1-score, as well as IoU (Jaccard coefficient) and accuracy, were monitored. The best metrics were demonstrated by the U-Net model, supplemented by the cellular automaton algorithm: precision = 0.91, recall = 0.90, F1 = 0.91, IoU = 0.84, and accuracy = 0.90. The developed segmentation algorithms are universal and show a high quality in highlighting areas of interest under any shooting conditions and different volumes of defective zones, regardless of their localization. The automatization of the process of calculating the damage area and a recommendation in the "critical/uncritical" format can be used to assess the condition of concrete of various types of structures, adjust the formulation, and change the technological parameters of production.

The Influence of Concrete Age on the Accuracy of Pile Integrity Testing on Bored Piles Foundation

Abstract Bored pile foundations are defined as deep foundations in the form of an elongated tube with a specific diameter made of reinforced concrete installed using the drilling method and in-situ casting concrete. The cast in-situ process in bored holes with a deep elevation below the ground surface can potentially cause integrity problems, such as voids, segregation, fracture, necking, bulging, and other concrete defects. Currently, many non-destructive test methods are available to ensure the quality of the bored pile; one is the Low Strain Integrity Testing method, also known as Pile Integrity Testing (PIT). ASTM recommends that PIT should be carried out no sooner than 7 days after casting or the strength of the concrete has reached a minimum of 75,0% of its design strength. In fact, the PIT test is carried out before the concrete’s age and/or its strength is achieved, considering the limited construction time. This could lead to misinterpretation of the PIT test results and inaccurate identification of the bored pile defect locations. The study aims to determine the effect of concrete age on the accuracy of bored pile defect locations on a laboratory scale. The test model consisted of four bored piles with a diameter of 0,3 meters and a depth of 2,0 meters, which were cast using reinforced concrete with a design strength of fc’ 30 MPa. The PIT was performed at <7, 14, and 28 days of concrete age, and then the confusion matrix method was used to create a binary classification model to determine the accuracy of the defect location. The defect locations were visually observed and validated by dismantling the test model after 28 days of casting. The results showed that the accuracy of identifying the location of concrete defects was not affected by the age of the concrete but was determined by the compressive strength of the concrete during testing. Referring to the F-score, bored piles with concrete strength >75,0% of the design accurately identify defect locations above 84,21%. On the other hand, bored piles with concrete strength <75,0% design resulted in accuracy below 66,67%.

Automatic detection and classification process for concrete defects in deteriorating buildings based on image data

ABSTRACT Defects in buildings accelerate the deterioration of building conditions and threaten the structural safety of buildings. Building condition assessments are essential to identify and assess building defects. However, with the vast number of buildings, individual inspections are nearly impossible, highlighting the need for innovative solutions. Therefore, this study presented a safety inspection process that automatically detects and classifies defects using image data that can be easily acquired at a low cost and confirms the location of defects through 3D modeling. A CNN model is developed to detect defects in images of aging buildings, and the proposed process is validated through a case study to demonstrate its effectiveness. The case study focused on the effectiveness and convenience improvement of the proposed process compared to the existing methods of inspecting the exterior of buildings. The effectiveness of the proposed process was reviewed through the case study, and it was confirmed that more efficient safety inspections could be supported using only image data compared to the existing methods. In particular, the safety inspection process of this study is expected to be applied in practical use within a short period since it is an easily utilizable method for inspectors.

Uncertainties quantification for damage localization in concrete based on Bayesian method

The presence of defects in concrete can diminish load-bearing capacity of structures, giving rise to potential concerns regarding safety and durability. Thus, a method that enhances the sensitivity, resolution and robustness of damage localization is critically necessary to assess the condition of concrete structures. This research presents a damage localization method based on Bayesian probabilistic fusion, and uncertainties from measurement and identification process are considered and quantified. The likelihood function is constructed based on the hyperbola-based damage localization method, and the posterior distributions of unknown parameters are calculated via Bayesian theorem combined with measurement data. Furthermore, a meso-level finite element model is established, wherein the concrete medium is considered as a three-phase composite material consisting of polygonal aggregates, mortar matrix and interface transition zones. Owing to the meso-level modeling, the propagation behavior of stress waves within concrete and complicated interactions between stress waves and concrete internal structures can be better characterized. Finally, the damage information, time-difference-of arrival, is extracted from the response signals and the efficiency of the proposed method is verified numerically. The numerical results demonstrate that the proposed probabilistic fusion method outperforms the conventional hyperbola-based method in terms of achieving high spatial resolution and resilience in damage localization.

Intelligent Diagnosis of Concrete Defects Based on Improved Mask R-CNN

Intelligent Diagnosis of Concrete Defects Based on Improved Mask R-CNN

Development and Validation of a Novel Surface Defect Index (SDI) Method for the Effective Quality Evaluation of Concrete Surfaces

Concrete defects have a significant impact on concrete constructions. These defects should be considered not only aesthetic defects but also structural defects. In this study, a novel Surface Defect Index (SDI) method was developed to quantify the defect volume according to liquids’ penetrating properties by applying ready-mixed plaster (RMP). The SDI refers to the volumetric proportion of all apparent and unapparent defects in a given area of concrete, and it is expressed as a percentage of the total volume affected by defects. The proposed SDI method was validated and tested under various controlled defect configurations. Regardless of the specific characteristics of each defect configuration, the SDI method consistently demonstrated a high level of consistency, repeatability, and reproducibility, with coefficients of variation (CVr and CVR) below 5% and with correlation coefficients of R2 = 0.9968. The method succeeded in assessing the surface quality levels through the SDI, demonstrating a significant correlation between this index and the volume of defects. The proposed index was tested on real concrete surfaces, affirming its efficacy in accurately quantifying the volume of surface defects; thus, it can provide an important metric for quality control. Moreover, it provides an excellent evaluation of the quality of concrete surfaces.

Multi-type classification and 3D visualization of internal defects in concrete plates based on a hybrid method of deep learning algorithm and bending mode analysis for acoustic vibration signals

In this paper, a hybrid method combining the deep learning algorithm and bending mode analysis of acoustic vibration signals is proposed for the multi-type classification and 3D visualization of internal defects in concrete plates. A novel deep learning model termed fully convolutional network based on principal component analysis and attention-embedded long-short term memory (PCA-ALSTM-FCN) is established. The PCA-ALSTM-FCN model successfully classifies multi-type internal defects, and the two-dimensional (2D) defect contour maps are generated based on the predicted state by the trained model. The defect depths are calculated according to the analytical formula of bending vibration mode of acoustic vibration signals. Combining the 2D defect map and depth information, the three-dimensional (3D) visualization images of defects are obtained. The average recognition accuracy of PCA-ALSTM-FCN model for different types of defects reaches 94.8 %, and the defect depth calculation error range is approximately 10 %–20 %. The experimental results show that the proposed method in this paper can accurately distinguish different types of internal defects and effectively locate the 3D position of defects in concrete.

A critique on “Modeling the spontaneous imbibition of non-Newtonian fluids into the fractal porous media of tight reservoirs”

In February 2022, this journal published the paper “Modeling the spontaneous imbibition of non-Newtonian fluids into the fractal porous media of tight reservoirs” (Zhu et al., 2022), which develops and validates an analytical model for the capillary absorption of (non-)Newtonian liquids in sandstone, founded on a fractal-based bundle-of-tubes model. This critique – a post-publication review – expresses concerns on that model development and validation. It is shown that agreement between calculated and measured absorbed moisture masses and moisture front heights is only attained with two strongly distinct sets of material and fluid properties, a dichotomy that is however not disclosed in the paper. It is moreover revealed that the input material and fluid properties and the output absorbed moisture masses and moisture front heights often defy physical plausibility. Detailed scrutiny of the mathematical model complementarily exposes concrete defects in the calculation of the ‘sample’ cross-section. It is displayed that this cross-section calculation is exclusively governed by the material properties, implying that the measured cross-section can (almost) never be equated. It is furthermore established that the cross-section calculation assumes the equality of the areal and volumetric porosity, which is incorrect for a bundle-of-tubes with tortuous capillaries. Additionally it is demonstrated that the cross-section calculation merges two conflicting relations between the porosity and the fractal properties. All these concerns bring about doubts concerning the reliability of (Zhu et al., 2022)'s model development and validation, and the authors are hence invited to placate these concerns in their reply.

A Review of Waterborne Polymer–Cementitious Composite Repair Materials for Application in Saline Soil Environments: Properties and Progress

The properties of a large number of concrete infrastructures in China are deteriorating year by year, raising the need for repairing and strengthening these infrastructures. By introducing waterborne polymers into a cement concrete system, brittle cracks and easy bonding performance defects of concrete can be compensated for to form a long-life, semi rigid, waterborne polymer-modified cementitious repair material with a promising development prospect. This paper investigates the modification effect of polymer emulsions on ordinary cement mortar. Our research mainly focused on the physical and mechanical properties, durability, microstructure and application status of waterborne polymer-modified cementitious composites. Literature studies show that with the increase in waterborne polymer content (0 wt%~20 wt%), the performance of cement mortar significantly improves, which in turn expands its application range. Compared with ordinary cement mortar, the introduction of waterborne polymers blocks some of the pores in the cement to a certain extent, thus improving its permeability, freeze–thaw resistance and durability. Finally, this paper describes the application of waterborne polymer–cementitious composites in western saline soil environments, as well as discusses the prospects of their development.

The cyclic load effect on the elasticity and plasticity deformation of high-strength reinforced concrete elements

This article investigates the degradation of stiffness in high and standard-strength concrete due to the influence of repeated loading and considers the non-linear behavior arising from localized or natural defects of concrete. The article also refers to the models from existing literature that evaluate the characteristics of concrete by enhancing its physicochemical properties through the application of natural and synthetic fibers. The impact of the service load, which influences the change in concrete stiffness, is illustrated using the elastic–plastic concrete model. The elastic–plastic model of concrete, combined with experimentally determined data such as elastic and residual deformations, simplifies the analysis of concrete beams or surface elements. This model enables static solutions of inelastic materials based on the classical theory of elasticity. The degradation of element stiffness in this model is described by the local increment of the rotation angle. Based on the model we can assume a constant stiffness along the length of the beam before as well as after exceeding the value of the cracking moment. In the case of cyclic loads, it can be assumed that residual deformations have the greatest influence on the displacement values, which in the calculation model are described by the component of the angle of rotation in the crack, describing permanent displacements.

A Review of Research on Detection Methods for Internal Defects in Concrete

This article introduces the commonly used detection methods for internal defects in concrete; A review was conducted on the research progress of impact echo method and infrared thermal imaging method. The principles of impact echo method and infrared thermal imaging method were studied, and the problems existing in existing methods were pointed out. The results indicate that the detection methods for internal defects in concrete have made certain progress in both theoretical research and engineering applications. However, further improvement and refinement are still needed. The most important step in the improvement plan is to determine the damage situation of reinforced concrete components. From multiple perspectives, non-destructive testing is undoubtedly the main development object for detecting concrete damage in the future.