Inspection Of Concrete Structures 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.

2D Analytical Modelling for Ultrasonic Inspection of Concrete Structures: Effects of Scatterers Position Correlation

Ultrasonic Non-Destructive Testing (NDT) simulation plays an important role in designing inspections of concrete structures. The concrete structural complexity (presence of different heterogeneities: aggregates, porosities, microcracks …) leads to dispersion and attenuation of the ultrasonic waves, which requires an accurate modelling of the waves scattered from heterogeneities. For that purpose, studying the mean coherent wave characterized by its attenuation and phase velocity is of great interest. Two major approaches (numerical or analytical) exist for modelling the coherent wave. Numerical methods are more efficient but still limited in the 3D case by their computation time. Therefore, semi-analytical homogenization methods are considered a practical solution for a fast 3D simulation. Nevertheless, they may encounter several limitations in estimating the attenuation in dense heterogeneous materials such as concrete. Our goal is to explain and handle these prediction errors. Since the scattered ultrasonic field depends strongly on the obstacles’ positions in concrete, a particular focus is put on the pair correlation function describing the radial distribution of scatterers’ positions around a fixed scatterer (mean concentration at a distance r from this scatterer). Existing homogenization models are employed for simulating coherent wave propagation in concrete use by simplification of a uniform distribution. In order to improve the analytical models, the effects of scatterers’ positions correlation are investigated. In the present work, we propose to evaluate precisely the existing homogenization model that is the most suitable for high scatterers concentration. A comparison between this model and a 2D finite element code under development in the CIVA NDT simulation software is presented.

A heavy-load wall-climbing robot for bridge concrete structures inspection

Purpose The purpose of this paper is to present a wall-climbing robot platform for heavy-load with negative pressure adsorption, which could be equipped with a six-degree of freedom (DOF) collaborative robot (Cobot) and detection device for inspecting the overwater part of concrete bridge towers/piers for large bridges. Design/methodology/approach By analyzing the shortcomings of existing wall-climbing robots in detecting concrete structures, a wall-climbing mobile manipulator (WCMM), which could be compatible with various detection devices, is proposed for detecting the concrete towers/piers of the Hong Kong-Zhuhai-Macao Bridge. The factors affecting the load capacity are obtained by analyzing the antislip and antioverturning conditions of the wall-climbing robot platform on the wall surface. Design strategies for each part of the structure of the wall-climbing robot are provided based on the influencing factors. By deriving the equivalent adsorption force equation, analyzed the influencing factors of equivalent adsorption force and provided schemes that could enhance the load capacity of the wall-climbing robot. Findings The adsorption test verifies the maximum negative pressure that the fan module could provide to the adsorption chamber. The load capacity test verifies it is feasible to achieve the expected bearing requirements of the wall-climbing robot. The motion tests prove that the developed climbing robot vehicle could move freely on the surface of the concrete structure after being equipped with a six-DOF Cobot. Practical implications The development of the heavy-load wall-climbing robot enables the Cobot to be installed and equipped on the wall-climbing robot, forming the WCMM, making them compatible with carrying various devices and expanding the application of the wall-climbing robot. Originality/value A heavy-load wall-climbing robot using negative pressure adsorption has been developed. The wall-climbing robot platform could carry a six-DOF Cobot, making it compatible with various detection devices for the inspection of concrete structures of large bridges. The WCMM could be expanded to detect the concretes with similar structures. The research and development process of the heavy-load wall-climbing robot could inspire the design of other negative-pressure wall-climbing robots.

Real Time Quality Detection and Automatic Recognition of Concrete Structures: Image Processing Algorithm Based on Deep Learning

The safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. This algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set; then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. The maximum detection time is 366ms and the shortest is 213 ms. The successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value in engineering practice. This research has a broad development prospect.

Automated detection and segmentation of internal defects in reinforced concrete using deep learning on ultrasonic images

Periodic inspection of concrete structures for detecting internal defects through nondestructive imaging is recommended for ensuring structural safety and integrity. Ultrasonic imaging is one of the emerging and widely accepted techniques in nondestructive diagnostics of concrete structures. However, manually screening the ultrasonic images to detect and localize anomalies over large areas especially those from public infrastructure is tedious and prone to misjudgments. This work proposes a Region-based CNN to automatically detect, localize, and segment those signatures corresponding to defects from the noisy ultrasonic images with multiple features. This network replaces the conventional RoI pooling adopted in RCNNs with RoIAlign. To optimize the model for real-life complexity and noise levels, the network is trained on real experimental data. The ultrasonic image dataset has been generated by applying the imaging technique based on the Synthetic Aperture Focusing Technique on reflected waveform data obtained on various specimens with installed defects. The proposed network has been trained on the labelled image dataset containing signatures corresponding to multiple flaws like debonded rebars, delaminations, and planar crack-like defects in concrete members. The predictions of the trained model over the test datasets detect and masks the pixels corresponding to defects in ultrasonic images with a best mAP of 0.98. Segmentation results of the developed model upon testing are found superior to other state-of-the-art defect detection networks.

Active Infrared Thermography applied for concrete structures inspection in Nuclear Power Plants

Due to the new requirements that are being demanded of the Nuclear Power Plants for their License Renewal (LR), Life Extension (LE) and Long Term Operation (LTO), it is necessary to verify the integrity of several civil structures, such as the containment building, used fuel building and cooling towers, among others. In general, the objectives of Non-Destructive Test methods (NDT) for the inspection of concrete structures in nuclear power plants can be arranged into: location of the reinforcement and tendons, determination of the state of the concrete matrix, determination of the corrosion of the metallic armor and detection of cracks, voids, inclusions and delaminations in the matrix of the structure. For this, Tecnatom, based on the experience acquired in the field of NDT, has developed a set of techniques for determining the structures condition including surface hardness testing, ultrasound (transverse wave pulse-echo and transmission speed), electromagnetic methods (Ground Penetrating Radar - GPR) and non-contact techniques such as Infrared Thermography (IRT). The method and technique best suited for a given inspection has to be selected depending on the objective sought, the typical defectology of the component, the characteristics of the structure and the conditions of the inspection itself. In this work it is presented an example of the development and in-field application of the IRT method for the inspection of a concrete structure in a nuclear power plant to detect possible delaminations caused by the corrosion of the metallic reinforcement.

Diagnosis of durability-related problems in concrete structures through comprehensive analysis and non-destructive testing: a case study

ABSTRACT The present study investigated concrete durability problems at the National University of Sciences and Technology,Pakistan, through comprehensive analysis and non-destructive testing. During the visual inspection of concrete structures, different deterioration mechanisms such as alkali-silica aggregate reaction (ASR), carbonation, abrasion and drying shrinkage were found. After visual inspection, the data on ingredients used in the construction of various structures were collected, revealingthat different brands of cement used in construction contained high alkali-equivalent content. Fine aggregates were used from two different quarries during construction. The available literature and X-ray Diffraction (XRD) results showed that both the quarries’ aggregates have the potential for alkali-silica reactivity. Furthermore, the XRD results of gel-type material collected from mapped cracking indicated the presence of delhayelite, similar to alkali-silica gel, suggesting ASR. The XRD curves of the whitest powder collected from various sites showed the presence of sodium sulfate and calcium carbonate peaks, indicating salt hydration attacks. The carbonation rate in concrete ranges from 4.33 to 5.77 mm/year. The results of the rebound hammer and pulse velocity test indicated that concrete used in parking has low strength which is the main cause of abrasion. Based on these findings a few recommendations have been presented to avoid durability-related problems in future structures.

Leveraging Saliency in Single-Stage Multi-Label Concrete Defect Detection Using Unmanned Aerial Vehicle Imagery

Visual inspection of concrete structures using Unmanned Areal Vehicle (UAV) imagery is a challenging task due to the variability of defects’ size and appearance. This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. Our method, coined the Saliency-based Multi-label Defect Detector (SMDD-Net), combines pyramidal feature extraction and attention through a one-stage concrete defect detection model. The attention module extracts local and global saliency features, which are scaled and integrated with the pyramidal feature extraction module of the network using the max-pooling, multiplication, and residual skip connections operations. This has the effect of enhancing the localisation of small and low-contrast defects, as well as the overall accuracy of detection in varying image acquisition ranges. Finally, a multi-label loss function detection is used to identify and localise overlapping defects. The experimental results on a standard dataset and real-world images demonstrated the performance of SMDD-Net with regard to state-of-the-art techniques. The accuracy and computational efficiency of SMDD-Net make it a suitable method for UAV-based bridge structure inspection.

PVDF-based coatings with CNT additions for strain monitoring of mortar substrates subjected to bending

Sensing coatings are rapidly entering the field of non-destructive tests. While cement-based composites are proving an excellent interaction with new/recent structures, polymer-based coatings, already employed for structural retrofitting purposes, can provide a valuable alternative. This study investigated the production, application, and use of poly(vinylidene fluoride) (PVDF) coatings. A 10w/v% PVDF-to-solvent ratio became the best trade-off between electrical conductivity and bond strength with the substrate. Different concentrations of Carbon Nanotubes (CNT) were investigated: 0.05, 0.10, 0.25, 0.50, and 0.75% by weight of PVDF. The conductive PVDF-CNT composites were brushed on the casted mortar beams with screws embedded as electrodes. The mortar beams and attached polymer coatings were then subjected to bending stress. The Gauge Factor was obtained by comparing the substrate’s strain with the coating’s electric response. The sensing intervals in the Fractional Change of Resistance-strain curves varied in relation to the CNT concentration. For instance, adding 0.50w/v% of CNT gave the highest sensitivity up to 0.2‰ strain, followed by a lower – still sufficient – gauge factor. PVDF-based coatings with CNT additions of 0.25 and 0.75w/v% witnessed a comparable sensing performance in the same strain limits, abruptly increasing and finally stabilizing to a low gauge factor. In contrast, both 0.05 and 0.10w/v% resulted in a low monitoring potential overall. The varying sensing zones experienced by the coating were attributed to the microscopical behavior of CNT within the PVDF matrix. In conclusion, the results highlighted the potentiality of polymeric coatings for sensing, monitoring, and inspection of concrete structures.

Non-Destructive Inspection of Concrete Structures by Noncontact Acoustic Inspection Technology Using Acoustic Irradiation Induced Vibration

Non-Destructive Inspection of Concrete Structures by Noncontact Acoustic Inspection Technology Using Acoustic Irradiation Induced Vibration

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 concrete crack segmentation model based on transformer

Routine visual inspection of concrete structures is essential to maintain safe conditions. Therefore, studies of concrete crack segmentation using deep learning methods have been extensively conducted in recent years. However, insufficient performance remains a major challenge in diverse field-inspection scenarios. In this study, a novel SegCrack model for pixel-level crack segmentation is therefore proposed using a hierarchically structured Transformer encoder to output multiscale features and a top-down pathway with lateral connections to progressively up-sample and fuse features from the deepest layer of the encoder. Furthermore, an online hard example mining strategy was adopted to strengthen the detection of hard samples and improve the model performance. The effect of dataset size on the segmentation performance was then investigated. The results indicated that SegCrack achieved a precision, recall, F1 score, and mean intersection over union of 96.66%, 95.46%, 96.05%, and 92.63%, respectively, using the test set.

Automated concrete crack evaluation using stereo vision with two different focal lengths

Surface cracks in concrete structures are an important indicator of the soundness of a structure. Stereo vision, consisting of two identical cameras, has been suggested to quantify crack characteristics using derived depth information. However, because high measurement resolution is required, zoom lenses are often used, making simultaneously crack localization and characterization difficult. This study presents a framework for the use of stereo vision employing one wide-angle lens and one telephoto lens, enabling accurate crack quantification as well as efficient 3D reconstruction. Furthermore, a robust depth estimation strategy is proposed for planar surfaces, such as are found in most concrete bridges. The performance of the proposed approach is field validated using an in-service concrete bridge. The 3D reconstruction model generated by a set of wide-angle images, including crack information extracted from the telephoto images using deep learning, can enable the improved inspection of concrete structures.

Development of Low-Frequency Phased Array for Imaging Defects in Concrete Structures.

The nondestructive inspection of concrete structures is indispensable for ensuring the safety and reliability of aging infrastructures. Ultrasonic waves having a frequency of tens of kHz are frequently used to reduce the scattering attenuation due to coarse aggregates. Such low frequencies enable the measurement of the thickness of concrete structures and detection of layer-type defects, such as delamination, whereas it causes a lack of sensitivity to crack-type defects. In this paper, to realize the ultrasonic phased array (PA) imaging of crack-type defects, we fabricated a low-frequency (LF) array transducer with a center frequency of hundreds of kHz. To avoid the crosstalk between piezoelectric elements and dampen the vibration of each element, we adopted soft lead zirconate titanate (soft PZT) with a low mechanical quality factor. Subsequently, we optimized the geometry of each piezoelectric element using a finite element method to generate a short pulse. After validating the design in a fundamental experiment using a single-element transducer, we fabricated a 32-element array transducer with a center frequency of 350 kHz. To show the imaging capability of the LF array transducer, we applied it to a concrete specimen with a delamination. As a result, the PA with the LF array transducer clearly visualized the delamination, which could not be visualized using the PA with a 2.5 MHz array transducer. Furthermore, we applied it to a more challenging defect, a slit, which is sometimes used to simulate crack-type defects. As a result, the PA with the LF array transducer clearly visualized a slit of 1 mm width and 40 mm height in a concrete specimen. Thus, we demonstrated the usefulness of the LF array transducer for inspecting crack-type defects.

Automated defect inspection of concrete structures

This paper presents a framework for automated defect inspection of the concrete structures, made up of data collection, defect detection, scene reconstruction, defect assessment and data integration stages. A mobile data collection system, comprising a 360° camera and a digital Light Detection and Ranging (LiDAR), is developed to render high flexibility of data acquisition of image and three-dimensional spatial data, while users traverse complex indoor environments. Deep learning algorithms are implemented to efficiently detect defects from the collected images, and a simultaneous localization and mapping algorithm is adopted for site reconstruction with the acquired LiDAR data. Based on the images of detected defects, assessment is conducted to evaluate the defect conditions, complemented with the defect dimensions estimated from the aligned image and LiDAR data. The position of defects could also be identified and mapped to respective structural elements. All the inspection results are finally integrated into existing Building Information Modelling files for better facility management. The proposed workflow was validated using a case study for determining concrete cracks and spalls in a real-world facility, successfully demonstrating the joint application of advanced technologies in facilitating inspection programs of civil infrastructure.

Remote characterization of chloride content in oven-dried concrete specimens by using synthetic aperture radar image models

Synthetic aperture radar (SAR) imaging is a remote sensing technique capable of performing noncontact subsurface inspection of dielectric materials like Portland cement concrete. Such techniques can be applied to field inspection of concrete structures and laboratory material characterization. The objective of this paper is to demonstrate the application of SAR imaging on characterizing chloride content inside oven-dried concrete specimens, using a 10-GHz central frequency SAR imaging sensor. Twelve concrete panel specimens (0.3x0.3x0.05 m3) with a 0.45 water-to-cement ratio were manufactured in six groups of different chloride contents (0%, 2%, 4%, 6%, 8% and 10% of cement weight). Five SAR image-based parameters were developed from each SAR image of concrete specimens for chloride, including integrated SAR amplitude of back reflection Iintb, integrated amplitude Iint, average maximum amplitude I-max, critical contour area Ac, and average Gaussian curvature of critical contour Kavg. A parametric analysis on the combined use of different numbers of SAR image parameters was carried out to determine the optimal application of SAR image parameters on the chloride detection problem in this paper. From our result, it is found that, for the purpose of chloride detection inside concrete specimens using single SAR parameter, integrated amplitude Iint shows the best performance. When using two SAR image parameters, combination of integrated amplitude Iint and critical contour area Ac shows the best performance among other two-parameter combinations. When using three SAR image parameters, combination of integrated amplitude Iint, critical contour area Ac, and average maximum amplitude I-max shows the best performance among other three-parameter combinations. Furthermore, it is interesting to report that combination of three SAR image parameters (Iint, Ac, and I-max) provides the overall best performance among all other combinations for the chloride detection problem in oven-dried concrete specimens.

Application of non-destructive methods of control within the inspection of concrete structures

This paper discusses the main non-destructive quality control methods that are used in the inspection of both under construction and already existing concrete structures. Purpose of the work: to conduct a comparative analysis of non-destructive testing methods, to consider a practical example of their application in the survey of a building being reconstructed, to identify the essential points of the organization of non-destructive quality control, which must be taken into account when developing a site work execution program. Materials and methods: the application of methods of non-destructive testing, separation with spalling and the ultrasonic method of surface sounding, is considered in detail. Results: the results of the inspection of structures by the methods separation with spalling and the ultrasonic method of surface sounding are presented, the test conditions, basic requirements for correct data interpretation, and the necessary calculations are given. Recommendations are given for planning the quality control of concrete structures at the stage of organizational and technological preparation of construction. Conclusions: non-destructive quality control finds its application both in the construction of new facilities and in the inspection of existing buildings. The choice of the survey method is made at the stage of development of a site work execution program, while it is necessary to take into account the features and scope of application of a particular method, probable measurement error, it is also possible to use several methods in combination.

Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures

The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, a new image analysis technique is needed to automatically detect cracks and analyze the characteristics of the cracks objectively. In this study, an image analysis technique using deep learning is developed to detect cracks and analyze characteristics (e.g., length, and width) in images for small-scale facilities. Three stages of image processing pipeline are proposed to obtain crack detection and its characteristics. In the first and second stages, two-dimensional convolutional neural networks are used for crack image detection (e.g., classification and segmentation). Based on convolution neural network for the detection, hierarchical feature learning architecture is applied into our deep learning network. After deep learning-based detection, in the third stage, thinning and tracking algorithms are applied to analyze length and width of crack in the image. The performance of the proposed method was tested using various crack images with label and the results showed good performance of crack detection and its measurement.

Artificial intelligence-empowered pipeline for image-based inspection of concrete structures

Inspection of civil infrastructure is a major challenge to engineers due to the limitations in existing practice, which are as laborious, time-consuming and prone to error. To address these issues, we have applied deep learning for image-based inspection of concrete defects of civil infrastructure, and have established an artificial intelligence-empowered inspection pipeline methodology. This innovative approach comprises anomaly detection, anomaly extraction and defect classification. The anomaly detection and extraction are used to identify defect regions from the enormous volume of image datasets, which used to be the common challenges encountered in automated visual inspections. The search space of defects is substantially reduced, i.e., at least 60% of the original volume of image datasets, with an average hit rate of ~88.7% and an average false alarm rate of ~14.2%. Following that, deep learning-based classifiers are used to categorize defects into appropriate classes. The assessment results show that the proposed inspection pipeline exhibits great capability in detecting, extracting and classifying defects subjected to various environmental and operational conditions, including lighting condition, camera distance and capturing angle, with an average testing accuracy of 95.6%.

Electrical properties of cement-based materials: Multiscale modeling and quantification of the variability

The electrical properties, resistivity and conductivity, inform on the durability of cement-based materials and can be used for monitoring and inspection of concrete structures. The physical origin of these properties can be linked to the dynamics of the pore solution. We propose a multiscale modeling approach of the electrical conductivity and resistivity informed by the dynamics of ions that enables the quantification of property variability across scales using Monte Carlo Micromechanics (MCM) computations. As a source of variability, we consider the pore solution composition, clinker composition and the uncertainty on solid conductivity. The results are compared to experimental measurements on various cement systems. The age-dependency of ionic diffusion, due to ion-ion and ion-solvent collective effects, is crucial to model the evolution of electrical conductivity. The main results show that Monte Carlo Micromechanics enables the quantification of the variability and uncertainty across scales, since MCM computations have provided estimates of the standard deviation of the electrical conductivity and resistivity at the scales of cement paste, mortar and concrete. Also, the results show that self-consistent scheme provides a good estimate of the effective electrical conductivity of cement-pastes, capturing the transition from a liquid to a solid matrix during cement hydration.