Defects In Concrete Structures Research Articles

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.

Time-Series Data Augmentation for Improving Multi-Class Classification Performance

This paper proposes a new approach to classify and evaluate defects in concrete structures automatically. To overcome the limitations of defect detection methods that traditionally relied on expert visual observation, the reflection signal of electromagnetic pulses is extracted as time-series data and used to analyze the propagation characteristics of each defect. This study uses deep learning models to analyze these time-series data and classify defects. Since anomaly detection data has more normal data than anomaly data, data augmentation methods such as Time Warping, Noise Injection, Smoothing, Trend Shifting, etc., were applied to solve the problem of data imbalance and overfitting. Among them, Noise Injection showed the best performance. The generalization performance of the proposed method was evaluated through performance evaluation using LSTM, GRU, and TCN models, and LSTM models showed the highest performance. The study results show that the proposed method effectively classifies defect types in concrete structures and can solve the limitations of existing methods by automatic classification through deep learning models. In addition, it was confirmed that the model's performance could be improved by improving the amount and diversity of data by selecting and applying appropriate data augmentation methods. The contribution of the research is to present a new approach that automates the defect detection and classification task of concrete structures and provides high accuracy and efficiency.

Study on movement measurement for internal defects in concrete structures by noncontact acoustic inspection method using correlation processing

The noncontact acoustic inspection method using acoustic irradiation induced vibration and a scanning laser Doppler vibrometer can detect internal defects in the surface layer of concrete structures, etc, over long distances and without contact. Since this method uses flexural vibration in principle, it can be used as an alternative method to tapping inspection. Therefore, there has been an increase in requests for realizing movement measurement. Thus, an experiment was conducted to verify whether movement measurement was possible by mounting multiple LDVs and sound sources that did not have scanning mechanisms on a moving cart. The experimental results revealed that by using cross-correlation processing between the emitted and received waveforms, it is possible to perform movement measurements at low speeds of about 2 km h−1.

Detection and diagnosis of concrete void defect using percussion-based method combined with convolutional neural network

As a fundamental construction material, concrete commonly encounter void defects, which could significantly impair the integrity of concrete members and seriously weaken their load-bearing capacity. Worse still, void defects typically exist inside concrete members, making it difficult to detect them in real time. Therefore, conducting regular inspections for internal void defects of concrete structures is of great importance to maintain structural health. This paper investigates an integrated method to detect concrete voids based on percussion approach and a convolutional neural network (CNN). Firstly, percussion-induced sound signals were collected from 18 concrete specimens with different precast void defects. Each specimen was tapped 100 times, for a total of 1,800 percussion-induced acoustic signals. Subsequently, continuous wavelet transform (CWT) and Mel frequency cepstral coefficient (MFCC) were introduced to convert the collected acoustic signals into two types of images. The converted images were divided into testing and training datasets according to the ratio of 1:3, and 600 images were input to the CNN as the test dataset and 1200 images as the training dataset. The CNN was then trained and verified by the joint input of CWT and MFCC images to perform concrete voids classification. The result shows that the proposed method presents a satisfying classification accuracy of 100 %. Next, percussion tests under 5 levels of noisy environments were conducted, and the results prove the remarkable anti-noise performance of the methodology by achieving 88.12 % accuracy even at the strongest signal-to-noise ratio of 0 dB. Finally, the superiority of the proposed method was further demonstrated through the accuracy comparison with machine learning (ML) methods, with the accuracy consistently outperforming over 25 %. In conclusion, the proposed method has a great potential for future in-situ applications in concrete void detection.

Experimental and theoretical investigations on the damage evolution of the basalt fiber reinforced concrete under freeze-thaw cycles

Damage and defects in concrete structures are unavoidable due to their complex composition and challenging manufacturing process, which can lead to fatal collapse in service. In the present study, we have conducted a comprehensive investigation on the damage evolution of the basalt fibre-reinforced concrete (BFC) under freeze-thaw cycles, based on experiments and theoretical modelling. We have performed the freeze-thaw cycle experiment on the BFC and measured the curve of the damage-cycling number. Based on the thermodynamic principle, we have derived the expression of the relationship between the damage and the freeze-thaw number. Then, using the attenuation theory as a foundation, we have built another damage model. The experimental data is then fitted using the two established models, and the corresponding parameters are identified. Furthermore, the detailed mechanism of the free-thaw-induced damage has been analysed by the numerical simulation of the thermal stress inside the BFC. These findings cast new light on the deep understanding of the failure of concrete materials, which are beneficial to engineering new materials and new structures in many fields.

Assessment of Internal and External Defects in Concrete Structures using Infrared Thermography

Assessment of Internal and External Defects in Concrete Structures using Infrared Thermography

Deep Learning Inversion of Multi-processed GPR Data to Characterize Subsurface Defects

Ground penetrating radar (GPR) is highly recommended for investing defects in concrete structures, yet interpreting GPR data is hindered by the fact that the images primarily display reflected electromagnetic waves rather than the actual shapes of the defects. In this paper, we introduce a deep learning network that aims to convert GPR B-scan images into permittivity maps of subsurface structures. This network takes three types of data as inputs and outputs the distribution of permittivity. These three types of data are the original data, the data obtained after applying migration to the original data, and the data obtained after processing the original data using a neural network. Each type of data is processed using different methods and contains distinct information, which is collectively used for inverting data. In this study, we validate the effectiveness of the proposed network by the finite-difference time-domain (FDTD) forward modeling data.

Automatic Identification Method of Defects in Concrete Structures Strengthened with Composite Materials Based on a Multi-Scale Model

Due to the inconsistent geometric characteristics of reinforced concrete structures with composite materials, some echo signals have a lower degree of clarity, resulting in problems such as long time, small range, and low accuracy in identifying structural defects. Therefore, in order to improve the accuracy and efficiency of defect identification in composite material reinforced concrete structures, a multi-scale model based on the automatic defect identification method for composite material reinforced concrete structures is proposed. Using the interface connection method, based on continuous distribution coupling and the uniform weighting coefficient calculation method to weigh the interface nodes, we determined the interface connection mode and analyzed the stress–strain relationship. The impact echo method was used to analyze the degree of high-frequency stress wave scattering caused by internal non-uniformity in composite reinforced concrete, and the fast Fourier transform conversion algorithm was used to generate amplitude–frequency curves and resolved thickness or defect depth. The experimental results show that the multi-scale finite element simulation structure of this method is clear and accurate, and although there are errors, they do not affect the simulation results of the structure. The impact echo detection effect is obvious; it can automatically identify defects in composite reinforced concrete structures and quickly and accurately identify defects in different states and positions of composite reinforced concrete, with a recognition accuracy of 98%.

Two-step detection of concrete internal condition using array ultrasound and deep learning

Internal defects in concrete structures pose a great risk and potential hazard to the safety and durability of civil structure. Reliable and accurate detection of the internal conditions of structures is a prerequisite for structural safety assessment. To realize the visualization of the internal condition of concrete structures and the identification of defects, this paper proposes a two-step detection method for the internal condition of concrete structures based on array ultrasound and deep learning. First, to address the limited resolution problems of conventional ultrasound synthetic aperture focusing technology (SAFT) imaging and the time-consuming computation of wavefield reconstruction methods such as reverse time migration (RTM) and full waveform inversion (FWI), an array-SAFT imaging algorithm is introduced to balance the resolution and real-time performance of the inspection results. The variational mode decomposition (VMD) and bilateral filtering methods are used before and after the signal focus imaging for signal noise reduction and image edge processing to further improve the resolution of ultrasound images while satisfying the real-time visualization of the detection results. Secondly, since pre-buried objects such as rebar and internal defects are reflected on the surface in ultrasonic images, a method based on the improved YOLOv5 network target detection model is proposed to achieve reliable identification of internal defects and intelligent interpretation of abnormal features in ultrasonic images. The proposed method is effectively validated in the ultrasonic inspection of concrete specimens with different emulated defects pre-built in the laboratory. Additionally, ultrasonic detection is also implemented in the internal defect detection of tunnel secondary linings.

Application of Ultrasonic Tomography to detect defects in Concrete Structures

Concrete is a material widely used in civil construction for structural elements. Due to this, there is an increasing concern about the knowledge of its deterioration state and safety. This stimulates research for methods that allow the evaluation of finished structures. Through the application of Nondestructive Testing (NDT) methods, it is possible to proceed with an investigation of the concrete structures. The application of NDT can contribute to problems diagnosis and quality control of concrete structures. Ultrasound Tomography (UT) tests are an attractive and feasible research strategy. UT allows mapping of the internal section of the object from projections of the same. The tomography uses an antenna, which in turn utilizes a series of dry contact point transducers that emit shear waves into the concrete structures. From the data collected by the equipment, 3D images of the element's reflected interfaces are generated. The present study aims to detect different anomalies that can be found in reinforced concrete slabs, verifying the potential of ultrasonic tomography for the detection of these anomalies. The study was performed by the technical team of the Structural Tests and Materials Laboratory at the Federal University of Rio Grande do Sul (LEME / UFRGS).

Infrared imaging for detection of defects in concrete structures

Deterioration of concrete structures is an issue that affects the entire world and is brought on by various causes like climatic conditions, aging, attack of aggressive media, etc. Periodic inspections and proper maintenance are crucial to prevent the degradation of structures. Conventionally, the condition of the structures is monitored visually but over a while, non-destructive evaluation (NDE) technologies have gained significance and are expected to be successfully used for the inspection and management of civil structures. Infrared imaging is an effective NDT tool to monitor the health of civil structures. In this study, Infrared Thermography (IRT), an image-based method, is introduced and implemented for the assessment of concrete structures. Experiments on cement concrete cubes were conducted to investigate and enhance the applicability of the infrared thermography technique. The results are quite promising and helps to detect defects like delamination, voids, and cracks, etc. as the IR camera can pick-up the difference in temperature between sound and defective regions. However, the utilization of IRT for inspection poses many challenges but they can be overcome by combining it with other techniques like finite element method, acoustic emission and ultrasonic guided waves. Hence, the infra-red thermography technique is an excellent technology for practically evaluating concrete structures.

Defect Detection in CFRP Concrete Reinforcement Using the Microwave Infrared Thermography (MIRT) Method—A Numerical Modeling and Experimental Approach

This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal horn antenna and the specimen. The present study investigated the application of a 360 W power system that operated at a frequency of 2.4 GHz, specifically focusing on two different operational modes: continuous and modulated. The specimen being examined consisted of a solid concrete slab that was coated with an adhesive layer, which was then overlaid with a layer of CFRP. Within the adhesive layer, at the interface between the concrete and CFRP, there was a defect in the form of an air gap. The study examined three distinct scenarios: a sample without any defects, a sample with a defect positioned at the center, and a sample with a defect positioned outside the center. The subsequent stage of the investigation incorporated experimental verification of the numerical modeling results. The experiment involved the utilization of two concrete specimens reinforced using CFRP, one without any defects and the other with a defect. Numerical modeling was used in this study to analyze the phenomenon of microwave heating in complex structures. The objective was to evaluate the selected antenna geometry and determine the optimal experimental configuration. Subsequently, these findings were experimentally validated. The observations conducted during the heating phase were particularly noteworthy, as they differed from previous studies that only performed observation of the sample after the heating phase. The results show that MIRT has the potential to be utilized as a method for identifying defects in concrete structures that are reinforced with CFRP.

Concrete Subsurface Crack Detection Using Thermal Imaging in a Deep Neural Network

The article discusses how impact actions, such as conflict and warfare, can negatively impact the structural integrity of concrete structures and how detecting hidden defects in concrete structures is difficult without expert knowledge. The paper presents a new technique that combines thermal imaging and artificial intelligence to detect hidden defects in concrete structures. The authors trained an AI model on simulated data and achieved a validation accuracy of 99.93%. They then conducted a laboratory experiment to create a dataset of concrete blocks with and without subsurface cracks and trained a new model, which achieved a validation accuracy of 100%. The article concludes that AI can detect hidden defects and subsurface cracks in concrete structures by classifying thermal images of concrete surfaces.

Nondestructive detection of delamination in painted concrete structures through square pulse thermography

Active infrared thermography (IRT) is effective for detecting and characterizing subsurface defects in concrete structures. Even though painted concrete (PCo) structures have been applied worldwide (especially for buildings) and subsurface defects, such as delaminations, can develop in their core, active IRT-based defect detection in previous studies focused on normal concrete (NCo) structures rather than PCo structures. Hence, the detectability of delaminations in NCo and PCo structures is investigated in the present study by applying square pulse thermography (SPT), a common approach for active IRT. The results demonstrated that delaminations in PCo, under the same parameters, are more detectable in IR images than in NCo structures. In addition, the effect of the width-to-depth ratio of a defect in PCo structures on the detectability during SPT inspections is presented. Interestingly, overheating is evaluated to determine an effective heating regime for SPT surveys on PCo structures. Finally, the empirical thermal diffusivity coefficient of PCo structures is estimated, which has not been previously determined.

Internal Defect Detection of Structures Based on Infrared Thermography and Deep Learning

Rapid and accurate detection of internal defects in bridges has always been a major concern of the management and maintenance departments. In the present study, an intelligent method for the detection of structural internal defects is proposed based on infrared thermography and deep learning. Through theoretical analysis, numerical simulations and laboratory experiments, the classification, localization and quantification of internal defects of concrete structures were achieved with the infrared thermography and deep learning method. The mean average precision for classification and localization of internal defects is 96.59%, the mIoU for pixel-level segmentation is 95.19%, and the average relative error for damage quantification is 0.70%. The feasibility of the trained model is verified with new images, and the results show that the trained model can capture infrared thermal features of internal defects with different sizes and depths. This method has the advantages of low cost, high accuracy, easy operation, and large area scanning of concrete structures, which can provide a good reference for the detection of internal defects of concrete structures.

NDT assessment of a thick-walled reinforced concrete mock-up of NPP concrete structures

Different materials within the Nuclear Power Plants (NPP) concrete structure can undergo ageing process which causes defects in the materials and can lose, partially or totally, their designed function. The typical defects include corrosion of reinforcement steel, delamination, cracks, malfunction of post-tensioning or steel composite systems etc. The testing methods to detect the defects of concrete structures are (i) destructive examinations and (ii) non-destructive testing (NDT). The challenges for assessing the performance of the NPP concrete structures using (NDT) methods include (i) the limited access time (only during the annual overhauls) and (ii) the accuracy and reliability of the available NDT testing devices. To overcome these challenges, a mock-up wall representing a section of the concrete containment of the NPP was built. Simulated defects and errors were embedded inside the wall such as dimensional errors, honeycombing, delamination, and voids in the post-tensioned grouted tendon ducts. Different NDT techniques and combinations were used for assessing the simulated defects of the mock-up wall. These techniques included basic NDT methods as rebound hammer, ultrasonic pulse velocity (UPV) and concrete cover meter and advanced NDT as Ground Penetrating Radar (GPR) and ultrasonic pulse echo (MIRA and MONOLITH). The results of basic NDT techniques as concrete cover meter, rebound hammer and UPV presented the basic characteristic properties of the concrete mock-up wall as concrete cover depth, compressive strength, and quality of concrete. The use of advanced NDT as GPR and MIRA and MONOLITH enabled us to detect and locate the embedded defect and the construction faults inside the mock-up wall. We conclude that the construction of the with simulated defects offers the possibility to investigate accuracy and reliably of the available NDT methods and increases the field experts’ skills providing also an important and very much needed educational platform for future NDE experts.

Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition

Image/video based defect recognition is a crucial task in automating visual inspection of concrete structures. Although some progress has been made to automatically recognize defects in concrete structural images, significant challenges still exist. In this work, we propose a deep convolutional neural network architecture that embeds novel spatial-channel interaction based concurrent attention (SCA) for multi-target, multi-class defect recognition. SCA module stems from novel kernel salient feature (KSF) encoder that captures higher-order features with robust discriminative representation of concrete defects. KSF encoder incorporates kernelized convolution followed by dynamic routing operation to be used as the primary building block. The proposed CSDNet architecture is able to apportion higher weightage in defective regions while suppressing large background area (healthy region), thereby improving recognition performance of overlapping defects in concrete structures. Experimental results and ablation study on four large benchmark datasets show consistent superiority of our proposed network as compared to current state-of-the-art methodologies. This end-to-end trainable architecture can be augmented with unmanned aerial vehicles (UAVs) to monitor health of massive infrastructures to leverage high concrete defect recognition performance.

Automatic detection of defects in concrete structures based on deep learning

The inspection of defects in concrete infrastructure is a costly and time-consuming task. Many image processing techniques (IPTs) have been proposed to replace manual inspection of defects. However, the extensively varying situations in real world (e.g., lighting and shadow changes) pose challenges to the wide adoption of IPTs. To solve these problems, we propose an automated detection method for concrete surface defects based on deep learning. Our model is a one-stage object detection network, which is composed of two parts: the backbone network EfficientNetB0 and the detector. EfficientNetB0 is formed by stacking multiple mobile inverted bottleneck convolution (MBConv) blocks, improving the feature extraction performance while reducing the number of parameters. The detector is inspired by feature pyramid network which extracts feature information from three scales, and fuses low-level features with high-level features via up-sampling operation to improve detection accuracy. The training and testing of the model are carried out on the concrete surface defects dataset. Results show 76.4% and 89.9% average precision (AP) for crack and exposed bars, respectively, with a mean AP of 83.2%.

Feasibility Study of Drone-Based 3-D Measurement of Defects in Concrete Structures

Recognition of defects in concrete structures, identification of cracks, concrete spalling, or other geometrical defects are important tools for structural damage detection. Defects in structures can include cracks, but also missing parts, due to the wear caused by weather or aging phenomena. These last types of defects in structures can be identified using red-green-blue (RGB) cameras, but the level of damage could be difficult to evaluate with 2-D images. In this sense, the application of 3-D reconstruction techniques can be helpful to determine the 3-D dimensions of spalling, swelling of concrete or the presence of visible steel parts of reinforced concrete. The use of drones for this type of measurement is very attractive for reducing the costs and time of measurement campaigns. However, the lack of accurate trajectory information and the vibrations affect the accuracy of 3-D measurements. In this article, a metrological characterization of measurement systems for the evaluation and recognition of defects in concrete structures is presented, starting from the acquisition of 3-D point clouds by low-cost time-of-flight (ToF) sensors, placed on drones. To evaluate the uncertainty of these systems, a mock-up with realistic defects was developed and characterized using a reference 3-D scanner. The 3-D reconstructions obtained via the selected sensors were used to evaluate the discrepancies of the 3-D shape compared to a ground truth model and the uncertainty of the selected scanners. The results show that, for all the defects tested, the standard deviation of the discrepancies between the defect reconstructed using the drone and the ground truth is below 2.5 mm.

Experimental analysis of defects in concrete structures

To evaluate the state of the building's structure and determine whether it is structurally sound, forensic construction is required. The purpose of this study is to highlight some of the research that has been conducted on impacted concrete structures in various building types where destructive testing and non-destructive tests have been conducted. The structural integrity can be evaluated based on the extent of the deterioration from the experimental results. For instance, the cause of the corrode reinforcements was due to inadequate concrete cover, whereas the failure of the concrete wall was due to structures under design, which led to the collapse of the wall. This can be seen in the example of the corroded reinforcements.