Concrete Defects Research Articles

Recent developments in utilization of nano silica in self‐compacting concrete: A review

AbstractSelf‐compacting concrete (SCC) is a type of flowable, high‐strength and high‐performance concrete in the construction field, which can flow and compact under its own weight. The damage of concrete is often caused by internal microstructural defects. Nano silica (NS) has excellent pozzolanic properties, and with an appropriate content of NS as auxiliary cementing material, it can promote the generation of C‐S‐H gels in addition to serve as internal filler, thus improves the micro‐scale defects in concrete. This paper extracts data from studies published over the last decade which focus on the application of NS in SCC and discusses the similarities and differences between the studies. Ultimately, a systematic review of the physical and chemical characteristics of NS, and the effect of NS on the workability, mechanical and durability properties of SCC is presented. Based on the review, the most common NS content is 0%–6%, where the negative effect of 0%–6% NS on the workability of SCC can be controlled by increasing the dosage of superplasticizer. The NS content of 2%–3% is optimum for improving the mechanical strength and the ability of SCC to resist water permeability, corrosion, acid and sulfate attacks, elevated temperature as well as shrinkage.

Damage-based constitutive model for a fiber-reinforced high-toughness cementitious composite exposed to temperatures ranging from +200 °C to −100 °C

An investigation on the uniaxial behavior in compression and tension of an innovative Fiber-Reinforced High Toughness Cementitious Composite – FRHTCC (fc = 45 MPa at 20 °C; mostly fly ash as a binder; polyvinyl alcohol fibers; with very fine aggregate) is presented in this paper within a fairly broad thermal range (from +200 °C to −100 °C). The tests, conducted in residual conditions after each thermal cycle, indicate that sub-zero temperatures have a more detrimental effect than moderately high temperatures (the elastic-hardening behavior shifts to elastic-quasi brittle in tension at sub-zero temperatures). Based on the test results, the evolution of the strength and of the stress–strain curve in uniaxial compression is modelled by means of a damage model that assumes the Weibull distribution for concrete defects, while Ramberg-Osgood equation is introduced to describe the nonlinear behavior in tension of the material. The proposed model is shown to effectively describe damage evolution, in both compression and tension, and to realistically reproduce the stress–strain curves of the material, as demonstrated by the very good fitting of the test results. Therefore, the model is a reliable predictive tool.

Spatial attention-based dual stream transformer for concrete defect identification

This paper addresses the problem of automatic crack identification within building structures. Cracks can cause structural damage to a building, necessitating prompt repairs to preserve lives. Detecting structural defects is a difficult process, especially in tall towers. Machine learning and deep learning approaches automate damage identification and crack detection. Machine learning-based methods train models with manually produced features that may contain errors. Deep convolutional neural networks (DCNN), on the other hand, enhance performance by extracting high-dimensional features. However, DCNN algorithms for crack identification lack a global focus on features that can compromise performance. This study inserts a dual-stream transformer module into a single 2D convolution neural network (CNN) layer. The dual-stream block contains a semantic and pixels path. Precise views of local and global features at the pixel level can be obtained through two paths. This increases self-attention information distributed parallel along the semantic and pixel paths to train the model. Further, the DSTNet (Dual Stream Transformer Network) superiority is validated on the three datasets and achieved notable performance.

Internal defect detection quantification and three-dimensional localization based on impact echo and classification learning model

Accurately identifying, localizing, and characterizing internal defects is crucial for ensuring the safety and durability of concrete structures. While impact echo (IE) is a highly promising non-destructive testing method for detecting concrete internal defects, previous studies have primarily focused on defect identification with less emphasis on quantification and depth localization of internal defects. In this paper, we propose an intelligent detection method based on IE and deep learning to achieve intelligent identification, area quantification, and depth localization of concrete internal defects. The proposed method includes the following three components: (1) A one-dimensional model combining wavelet packet decomposition and the Gate Recurrent Unit network was proposed to achieve an automatic diagnosis of defect signals inside concrete structures. (2) A method incorporating defect identification probability heatmap and threshold segmentation is employed to quantify the concrete defect area and detect the defect area detection rate. (3) A two-dimensional model combining wavelet transform and convolutional neural network was developed to achieve defect depth localization. The proposed method has been effectively validated in laboratory experiments involving concrete slabs with artificial defects.

A Bridge Damage Visualization Technique Based on Image Processing Technology and the IFC Standard

As an important part of the traffic infrastructure, the bridge has attracted much attention for its health conditions. Bridge damage detection is an important way of evaluating bridge health conditions. However, the bridge damage could not be directly expressed by traditional bridge damage detection methods. With the emergence of new technologies and engineering applications, it is possible to achieve visual expression of bridge damages, improve the digitization of bridge detection, and promote sustainable development in bridge maintenance. In this paper, a method of bridge damage visualization based on image processing technology and the IFC standard description is proposed for common bridge damages. An existing cable-stayed bridge is taken as the engineering background, and the collected damage images are processed and analyzed utilizing cutting-edge technology to obtain specific damage information such as the length and width of bridge cracks and concrete defects. Then based on the IFC standard, the bridge damage and its attribute information are visualized and stored through an established REVIT model of the bridge.

Application of deep learning in damage classification of reinforced concrete bridges

Inspecting Reinforced Concrete (RC) Bridges is crucial to ensure their safety and perform essential maintenance. The current research introduces the knowledge base for applying deep learning to classify and detect RC bridges' five most common defects (cracks, corrosion, efflorescence, spalling, and exposed steel reinforcement). Theimage classification process was carried out using Xception & Vanilla models based on convolutional neural networks (CNN). A comparative study between the two models is presented for multi-class, multi-target image classification.The concrete defect bridge image (CODEBRIM) dataset was used to train and test the models. The outcomes showed the potential application of deep learning models (Xception & Vanilla) for defect classification of concrete bridges and the superiority of the Xception model in defect classification with an accuracy of 94.95%, compared to 85.71% accuracy for the Vanilla model.

Deep learning-based concrete defects classification and detection using semantic segmentation

Visual damage detection of infrastructure using deep learning (DL)-based computational approaches can facilitate a potential solution to reduce subjectivity yet increase the accuracy of the damage diagnoses and accessibility in a structural health monitoring (SHM) system. However, despite remarkable advances with DL-based SHM, the most significant challenges to achieving the real-time implication are the limited available defects image databases and the selection of DL networks depth. To address these challenges, this research has created a diverse dataset with concrete crack (4087) and spalling (1100) images and used it for damage condition assessment by applying convolutional neural network (CNN) algorithms. CNN-classifier models are used to identify different types of defects and semantic segmentation for labeling the defect patterns within an image. Three CNN-based models—Visual Geometry Group (VGG)19, ResNet50, and InceptionV3 are incorporated as CNN-classifiers. For semantic segmentation, two encoder-decoder models, U-Net and pyramid scene parsing network architecture are developed based on four backbone models, including VGG19, ResNet50, InceptionV3, and EfficientNetB3. The CNN-classifier models are analyzed on two optimizers—stochastic gradient descent (SGD), root mean square propagation (RMSprop), and learning rates—0.1, 0.001, and 0.0001. However, the CNN-segmentation models are analyzed for SGD and adaptive moment estimation, trained with three different learning rates—0.1, 0.01, and 0.0001, and evaluated based on accuracy, intersection over union, precision, recall, and F1-score. InceptionV3 achieves the best performance for defects classification with an accuracy of 91.98% using the RMSprop optimizer. For crack segmentation, EfficientNetB3-based U-Net, and for spalling segmentation, IncenptionV3-based U-Net model outperformed all other algorithms, with an F1-score of 95.66 and 89.43%, respectively.

Assessment of void and crack defects in early-age concrete

Early-age voids and cracks can cause the mechanical qualities of concrete to deteriorate and impair its long-term operating performance. Therefore, it is crucial to detect and assess these problems at early ages. In this study, passive infrared thermography (IRT) and digital image correlation (DIC) techniques are used to study the temperature field and deformation field containing the defects. And the relationship between the geometric size of defects and temperature is investigated in conjunction with the finite element model. The results demonstrate that the subsurface void and surface void defects can be precisely identified and distinguished using infrared thermal imaging technology. The banded area radiating from the edge of the aggregate to the boundary, the connection between the aggregates, and the annular area surrounding the aggregate are where the majority of the large positive strain in the mortar is concentrated. These locations could develop cracks. Concrete voids will weaken the local mechanical properties, and increase the likelihood of local cracking when they are present in areas of large strain. The radius, depth, and thickness of voids are the geometric factors that influence the temperature difference, and there is a significant power function relationship between thickness × depth and temperature difference.

Preparation and Performance of Repair Materials for Surface Defects in Pavement Concrete.

Concrete surface defects are very complex and diverse, which is a great test for repair materials. The efficiency and durability of the repair system depend on the bonding effect between the concrete and the repair material. However, the rapid increase in system viscosity during the reaction of repair materials is an important factor affecting the infiltration effect. In the present work, the infiltration consolidation repair material was prepared, and its basic properties (viscosity, surface drying time and actual drying time, infiltration property) and mechanical properties were evaluated. Finally, the infiltration depth, film-forming thickness, and anti-spalling ability of concrete under a single-side freeze-thaw cycle are revealed. The results showed that using ethyl acetate could rapidly reduce the viscosity of the repair material, and the repair material could penetrate 20-30 mm into the concrete within 10 min. It was found by laser confocal microscopy that the thickness of the film formation after 3 days was only 29 µm. In the mortar fracture repair test to evaluate the bond strength, the bond strength of the repaired material reached 9.18 MPa in 28 days, and the new fracture surface was in the mortar itself. In addition, the freeze-thaw cycle test was carried out on the composite specimens under salt solution to verify the compatibility of the designed repair material with the concrete substrate. The data showed that the average amount of spalling was only 1704.4 g/m2 when 10% ethyl acetate was added. The penetrating repair material in this study has good infiltration performance, which can penetrate a certain depth in the surface pores and form a high-performance consolidation body, forming a "rooted type" filling.

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.

Conversion of underwater concrete images to air in detection of hydraulic structures

Taking underwater concrete images with an optical camera is an important measure for underwater defect detection. However, the underwater low-light environment and light refraction on the surface of different media result in poor image quality. In order to make the collected underwater images effectively reflect the real situation of underwater concrete defects, we propose an image conversion algorithm combining underwater image color enhancement and refraction distortion correction, which can convert underwater images into aerial equivalent images. In this paper, two conversion models of underwater image to air conversion are proposed, one of which models the refractive distortion of the underwater multilayered media through the ray projection method to correct the refractive distortion of underwater images and image field of view (FOV) conversion. The other is that by analyzing the problem of low image-imaging quality and loss of edge information due to the uneven illumination environment underwater, we process the dark channel priority adaptive enhancement algorithm to improve underwater image quality. The experimental results from real scenes show that the imaging quality of underwater images is improved by converting underwater images to air. The pixel error of images converted into the air is ⩽1 pixel, and the FOV error of images is ⩽8.5%. The high-precision underwater image conversion provides strong support for subsequent underwater measurement.

Effect of thermal fatigue on mechanical properties and microstructure of concrete in constant ambient humidity

The daily and annual temperature differences in mid and high latitudes areas vary greatly, which leads to thermal fatigue deterioration of concrete. The deterioration of concrete properties can also be generated by the cyclic change of humidity in concrete. In the experiment, the thermal fatigue tests of concrete with two strength grades were carried out at the temperature difference of 20 °C, 30 °C, and 40 °C (the initial temperature is 20 °C) under the condition of constant ambient humidity. The macroscopic properties change of concrete such as compressive strength and splitting tensile strength were measured. The microstructure changes of concrete were determined by ultrasonic nondestructive testing technique and mercury intrusion test. The results indicated that thermal fatigue has obvious deterioration effect on concrete. With the increase of temperature difference and cycle number, the strength of concrete decreased dramatically and the decrease of C40 concrete was greater than that of C25 concrete. The splitting tensile strength was more sensitive to thermal fatigue than compressive strength. The ultrasonic wave velocity gradually decreased and the damage factor increased, indicating the internal crack defects of concrete increased. At the same temperature difference, the porosity, total pore volume, average pore diameter, medium pore diameter, and the most probable pore diameter of concrete increased gradually with the increase of the cycle number, while the total pore surface area decreased. The pore structure showed the characteristics of coarsening and the trend of deterioration. The porosity of C40 concrete was less than that of C25 concrete, but the relative change value of porosity was larger, which reveals the internal reason for the strength damage of concrete under thermal fatigue at the microscopic level.

Effect of frequency on the evaluation of the concrete defects by ultrasonic shear wave tomography

Effect of frequency on the evaluation of the concrete defects by ultrasonic shear wave tomography

Effect of frequency on the evaluation of the concrete defects by ultrasonic shear wave tomography

Effect of frequency on the evaluation of the concrete defects by ultrasonic shear wave tomography

Implications of the compressive strength control accuracy on the probability of accepting defective concrete elements

The contribution of control method inaccuracy to the accuracy and performance of building structures while adhering to the metrological requirements for the strength determination process in reference materials is analysed. The metrological fundamentals for improving the testing and quality control methods of modern concretes and their impact on the reliability of erected structures are developed. The measurement accuracy dependence on the quality control of concrete materials and products by destructive methods is established, the number of rejected products is determined. The accuracy of concrete quality control is assessed in relation to the likelihood of defective products. A Bayesian model is proposed for the probability of missing a defective product with known control confidence and the probability of error due to measurement and testing error in the control process. The additional probability of missing concrete defects in strength when testing in standard specimens by the destructive method due to imperfect test methods is calculated. The 'non-zero probability' of missing a defect in the concrete strength control process affecting the level of reliability is assessed, provided the metrological requirements for the test are met.

Behavior of square geopolymer recycled brick aggregate concrete filled steel tubular stub columns under axial compression

Geopolymer recycled brick aggregate concrete filled steel tubes (GRBACFST) have the advantages of waste utilization, reducing carbon dioxide emission and overcoming concrete defects, which have broad potential applications in engineering structures. However, there is little relative research and regulations for this kind of structure with the innovative material. In this paper, 20 square GRBACFST stub columns were tested under axial compression to investigate the effects of concrete reference strength, replacement ratio of recycled brick aggregate (RBA) and thickness of steel tube. The failure mode, load–strain curve, ultimate strength, compressive stiffness and ductility were analyzed. Based on the material characteristics of geopolymer RBA concrete, the existing constitutive model of traditional core concrete confined by steel tube for finite element method was modified. Numerical data of 192 GRBACFST stub columns with different parameters under axial compression were generated and compared with the design methods for traditional concrete filled steel tubes. It can be found using the actual strength and elastic modulus of geopolymer RBA concrete, the existing formulas are capable of predicting the axial compressive ultimate strength and stiffness of square GRBACFST stub columns.

Concrete Multi-Type Defect Classification Algorithm Based on MSSMA-SVM.

In order to realize the automatic classification of internal defects for non-contact nondestructive testing of concrete, a concrete multi-type defect classification algorithm based on the mixed strategy slime mold algorithm support vector machine (MSSMA-SVM) was proposed. The concrete surface's vibration signal was obtained using a laser Doppler vibrometer (LDV) for four classification targets for no defect, segregation, cavity, and foreign matter concrete classification targets. The wavelet packet transform (WPT) decomposes the detected signals to get information on different frequency bands. The energy ratio change rate, energy ratio, and wavelet packet singular entropy of each node after the WPT were used as the feature input of MSSMA-SVM. The experimental results show that the designed MSSMA-SVM classifier can accurately detect the type, which provides a practical algorithm for classifying concrete defects by laser vibration measurement.

Performance Comparison of Multiple Convolutional Neural Networks for Concrete Defects Classification.

Periodical vision-based inspection is a principal form of structural health monitoring (SHM) technique. Over the last decades, vision-based artificial intelligence (AI) has successfully facilitated an effortless inspection system owing to its exceptional ability of accuracy of defects' pattern recognition. However, most deep learning (DL)-based methods detect one specific type of defect, whereas DL has a high proficiency in multiple object detection. This study developed a dataset of two types of defects, i.e., concrete crack and spalling, and applied various pre-built convolutional neural network (CNN) models, i.e., VGG-19, ResNet-50, InceptionV3, Xception, and MobileNetV2 to classify these concrete defects. The dataset developed for this study has one of the largest collections of original images of concrete crack and spalling and avoided the augmentation process to replicate a more real-world condition, which makes the dataset one of a kind. Moreover, a detailed sensitivity analysis of hyper-parameters (i.e., optimizers, learning rate) was conducted to compare the classification models' performance and identify the optimal image classification condition for the best-performed CNN model. After analyzing all the models, InceptionV3 outperformed all the other models with an accuracy of 91%, precision of 83%, and recall of 100%. The InceptionV3 model performed best with optimizer stochastic gradient descent (SGD) and a learning rate of 0.001.

Defects in Concrete in Building Construction

Defects in Concrete in Building Construction

Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic‐rich building information model

AbstractConcrete defect information is of vital importance to building maintenance. Increasingly, computer vision has been explored for automated concrete defect detection. However, existing studies suffer from the challenging issue of false positives. In addition, 3D reconstruction of the defects to pinpoint their positions and geometries has not been sufficiently explored. To address these limitations, this study proposes a novel computational approach for detecting and reconstructing concrete defects from geotagged aerial images. A bundle registration algorithm is devised to align a batch of aerial photographs with a building information model (BIM). The registration enables the retrieval of material semantics in BIM to determine the regions of interest for defect detection. It helps rectify the camera poses of the aerial images, enabling precise defect reconstruction. Experiments demonstrate the effectiveness of the approach, which significantly reduced the false discovery rate from 70.8% to 56.8%, resulting in an intersection over union 6.4% higher than that of the traditional method. The geometry of the defects was successfully reconstructed in 3D world space. This study opens a new avenue to advance the field of defect detection by exploiting the rich information from BIM. The approach can be deployed at scale, supporting urban renovation, numerical simulation, and other smart applications.