Cracks In Concrete Structures Research Articles

Image enhancement-based detection of concrete cracks under turbid water bodies

ABSTRACT Underwater concrete structure crack detection and structural health condition assessment based on image processing is challenging. The complex underwater environment and severe image degradation seriously affect the accuracy of crack detection. To solve these problems, a monocular vision and image-enhanced fractal-based fractal science based on computer vision and image processing techniques are proposed to conduct a non-contact detection study of underwater concrete cracks. This study established a four-level structural health condition to assist in underwater crack measurement and safety assessment. The box-counting method was used as a practical tool to calculate the fractal dimension. Three distances of 0.5, 0.8, and 1.2 m were set to verify the effective distance of the algorithm. The results show that the method proposed in this study can effectively detect cracks in submerged concrete members within 0.6 m and help managers correctly determine the structure's health using the fractal dimension.

Identifying the influence of split tensile strength to crack width of high-strength reinforced concrete beam with polypropylene fiber from medical mask waste

Cracks in concrete structures due to tensile weakness can be repaired by using fiber concrete, including medical mask waste as an additive in high-strength concrete mixes. This research the influence of polypropylene fiber from medical mask waste on the mechanical characteristics, flexural behavior, and crack width of high-strength reinforced concrete (RC) beams. The initial stage involved examining the properties of the concrete constituent materials. The testing process was based on a high-strength concrete mix design using the Aitchin method mix design sheet. Compressive strength and split tensile strength were tested at fiber content (0 %, 0.15 %, 0.20 %, and 0.25 %). The three-point flexural testing procedure was carried out at 28 days on 1200×100×150 (mm) RC beams. The use of LVDT, strain gauge, and other measuring devices supported the acquisition of the required data. The results showed that the split tensile strength reached the optimum value of 66.19 MPa at 0.24 % fiber content. Polypropylene fiber from medical mask waste in RC beams showed a positive impact on reducing crack width at increased split tensile strength. Waste mask fiber content of 0.15 %, 0.20 % and 0.25 % gave stable and better results compared to 0 % content (no fiber). With high steel stress (fs), and high strain, it offers the potential to improve the mechanical properties of high-strength concrete, thereby reducing the width of cracks that occur. This improves the tensile weakness of the concrete. The effect of split tensile strength on the crack width (w) of beams with the formula approach: wexp=3.74ftf-1.513, wan=0.187ftf-0.022 shows that the experimental results have a significant effect on decreasing the crack width that occurs in high-strength RC beams, thereby improving the quality of concrete

Fractal theory based identification model for surface crack of building structures

The existing building structures are mainly composed of concrete and masonry structures. This paper proposes a Fractal Theory-based Method (FTM) designed to efficiently detect surface cracks in both concrete and masonry building structures, addressing limitations in existing crack recognition techniques, such as weak adaptability to complex building surfaces and requiring a large amount of computational and human resource. A novel Soft Box-Counting (SBC) algorithm is proposed to calculate texture roughness information from multi-channel and multi-scale crack images, effectively mitigating the influence of non-feature information on crack identification results. The SBC algorithm is integrated into neural networks through the fractal embedding mechanism, enhancing feature analysis through visualization experiments. The resulting FTM model integrates texture roughness information from crack images and employs fractal theory to accurately classify crack regions while minimizing the similarity between crack and non-crack regions. In comparison to existing models, FTM demonstrates superior identification performance at the same computational level on benchmark datasets for surface cracks in both concrete and masonry building structures. The FTM model exhibits enhanced identification universality and higher resource utilization efficiency.

Cohesive element-based chemo-thermo-mechanical multi-field coupled cracking simulation of early-age concrete

Due to the intense exothermic hydration process, early-age concrete undergoes restrained shrinkage deformation, which can easily lead to cracks in concrete structures during construction. In order to accurately predict the adverse effects of early-age concrete cracking behavior, a cohesive element-based chemo-thermo-mechanical multi-field coupled cracking model of early-age concrete is proposed in this study. Parametric analysis shows that by selecting appropriate gap conductance, constitutive thickness, mesh size, and viscosity coefficient for the cohesive elements, the multi-field coupled cracking model can successfully simulate the entire cracking process of early-age concrete, and it has the ability to obtain crack development behavior based on the real displacement field jumps, coordinates, and damage of cohesive elements. The validation of the numerical examples reveals the competing mechanisms between thermal expansion and autogenous shrinkage in early-age concrete and accurately predicts the cracking strain, region, time, and width (within an order of magnitude) as reported in the literature.

An Ensemble Approach for Robust Automated Crack Detection and Segmentation in Concrete Structures.

To prevent potential instability the early detection of cracks is imperative due to the prevalent use of concrete in critical infrastructure. Automated techniques leveraging artificial intelligence, machine learning, and deep learning as the traditional manual inspection methods are time-consuming. The existing automated concrete crack detection algorithms, despite recent advancements, face challenges in robustness, particularly in precise crack detection amidst complex backgrounds and visual distractions, while also maintaining low inference times. Therefore, this paper introduces a novel ensemble mechanism based on multiple quantized You Only Look Once version 8 (YOLOv8) models for the detection and segmentation of cracks in concrete structures. The proposed model is tested on different concrete crack datasets yielding enhanced segmentation results with at least 89.62% precision and intersection over a union score of 0.88. Moreover, the inference time per image is reduced to 27 milliseconds which is at least a 5% improvement over other models in the comparison. This is achieved by amalgamating the predictions of the trained models to calculate the final segmentation mask. The noteworthy contributions of this work encompass the creation of a model with low inference time, an ensemble mechanism for robust crack segmentation, and the enhancement of the learning capabilities of crack detection models. The fast inference time of the model renders it appropriate for real-time applications, effectively tackling challenges in infrastructure maintenance and safety.

Concrete crack analysis using a deep belief convolutional neural network

The assessment of surface cracks in concrete structures plays a pivotal role in determining structural integrity. However, current diagnostic technologies suffer from drawbacks such as being time-consuming, subjective, and reliant on inspectors' experience, resulting in low detection accuracy. This paper seeks to address these issues by proposing an automated, vision-based method for identifying the surface condition of concrete structures. The method integrates advanced pre-trained convolutional neural networks (CNNs), transfer learning, and decision-level image fusion. To develop and validate this approach, a total of 6,500 image patches from diverse concrete surfaces were generated. Each pre-trained CNN establishes a predictive model for the initial diagnosis of surface conditions through transfer learning. Given the potential for conflicting results among different CNNs due to architectural differences, a modified Deep Belief CNN algorithm is crafted, thereby enhancing crack detection accuracy. The effectiveness of the proposed method is confirmed through a comparison with other CNN models. Robustness is tested by subjecting the method to images with various types and intensities of noise, yielding satisfactory outcomes. In practical scenarios, the hybridised approach is applied to analyse field-captured images of concrete structures using an exhaustive search-based scanning window. Results showcase the method's capacity to accurately identify crack profiles, with minimal areas of incorrect predictions underscoring its potential for practical applications.

Developing a Model to Improve the Efficiency of Maintenance Management for Service Buildings Using BIM and Power BI: A Case Study

Developing a Model to Improve the Efficiency of Maintenance Management for Service Buildings Using BIM and Power BI: A Case Study

Cracks in Concrete Structures Causes And Treatments: A Review

Cracks in Concrete Structures Causes And Treatments: A Review

A CNN-based network with attention mechanism for autonomous crack identification on building facade

ABSTRACT This paper presents a rapid and precise deep learning-based approach for measuring cracks in concrete structures. The proposed methodology involves data acquisition, pre-processing, model construction and post-processing. The Deeplabv3+ model is used for crack detection, and the use of Coordinate Attention is introduced to enhance its performance. And the implications of attention locations in the model are discussed. Insertion position after atrous spatial pyramid pooling (ASPP) operation is the most effective and accurate. The crack widths are obtained through post-processing, and the actual width is determined using the width conversion equation. The proposed method achieves an effective crack detection MIoU of 85.95 and a calculated width error of 6.3%, with a reduction of 2.5% compared to traditional models. Numerical experiments and real-world building experiments have demonstrated the feasibility and effectiveness of the proposed method. Overall, the proposed method presents a fast and dependable crack detection technique that has great potential for practical application.

A multi-scale robotic approach for precise crack measurement in concrete structures

This paper introduces a multi-scale robotic approach for measuring the width, length, shape, and profile of hairline cracks in concrete structures. The approach uses a convolutional neural network to identify potential surface cracks, and then robotically navigates a high-resolution laser scanner to measure the detailed shape of the detected cracks. Finally, 3D point cloud registration techniques fuse the laser scans with LiDAR-based scan of the surrounding environment. The proposed method is validated with computer simulations and physical experiments on a concrete specimen. The results are compared against the state-of-the-art, vision-based methods as well as readings of a transparent crack width ruler. The comparison demonstrates the superiority and effectiveness of the proposed multi-scale robotic approach in measuring hairline cracks providing vital data for assessing the conditions of civil infrastructures.

Real-time assessment of surface cracks in concrete structures using integrated deep neural networks with autonomous unmanned aerial vehicle

Ensuring the structural integrity of civil infrastructures is critical, given the potential hazards presented by cracks. Traditional manual inspections, though common, often face challenges related to accuracy, accessibility, safety, and cost-effectiveness. To address these issues, this study presents a new approach that integrates unmanned aerial vehicles (UAVs) with advanced computer vision techniques. A key feature of our approach is a system where a self-operating UAV is paired with a carefully crafted deep-learning design. This design takes advantage of both the Fast Region-based Convolutional Neural Network (FRCNN) and Residual Network (ResNet) models, allowing for real-time and accurate crack detection from ongoing video feeds. For uninterrupted real-time processing, our UAV is equipped with a data transmission module, sending live video to a computational platform enhanced with our advanced crack-spotting tool. After thorough training using a diverse dataset of 1000 images, our FRCNN-ResNet model was compared to other top models, achieving a precision rate of 93.3% and a quick 59.7ms inference time. Overall, this study offers a notable improvement in how we monitor civil structures, focusing on better safety and cost savings.

Determination of Adiabatic Temperature in Hardening Concrete According to Different Standards

The problem of thermal cracking in mass concrete structures still remains unsolved. There are many factors that influence the thermal regime in these concrete structures, the main ones being the type and content of cement in the concrete mixture, the thickness of the concrete layer, and the ambient temperature. However, the most important factor is the cause of the heat generated. Currently, there are a large number of standards and empirical formulas for determining the origin of a heat source using adiabatic equations. This paper provides a comparison of formulas for determining the adiabatic temperature in hardening concrete as the basis for determining the thermal regime in large-sized concrete structures.

Image-based Concrete Cracks Identification under Complex Background with Lightweight Convolutional Neural Network

Cracks are important indexes to evaluate the health status of concrete structures. To accurately and automatically identify the cracks of concrete structures, and solve the time-consuming and labor-intensive limits of manual detection methods, this paper proposed an image-based concrete cracks identification method based on a lightweight Convolutional Neural Network, which includes three modules: crack classification, semantic segmentation and quantitative calculation of crack geometric size. Firstly, the S_MobileNet was used to classify cracks, exclude irrelevant regions, and reduce the interference of non-crack images; Secondly, the optimized method SM-UNet based on the U-Net network was employed to segment the detected crack image at the pixel level; Finally, based on the results of crack semantic segmentation, image post-processing technology was used to realize the quantitative calculation of crack geometric size parameters, which provides a basis for crack damage assessment of concrete structures. The experimental results show that this study provides a solution for the automatic detection of crack images and high-precision measurement of crack size, which has an important value in scientific research and engineering application.

Research on the Repair Quality of Crack in Concrete Structures by Stress Waves

The main purpose of this study is to develop non-destructive testing techniques for assessing the repair condition of concrete structures. Concrete is a building material commonly used in engineering, such as dams, bridges, retaining walls, houses, etc.. Concrete structures frequently exist various types of cracks due to inferior environments and excessive loading. There are several crack-repairing techniques used in concrete construction. The most common method is to use epoxy. The proposed research will perform a series of numerical and experimental studies to investigate the feasibility of developing a new technique for quantitative evaluation of the concrete surface crack repairing status degree. The results of the studies will form the experimental study for establishing the experimental procedure and signal analysis criteria for further field application in the nondestructive evaluation of concrete surface crack repairing status.

Mobile-DenseNet: Detection of building concrete surface cracks using a new fusion technique based on deep learning

Crack detection is very important during the inspection of building structures to determine whether they are safe. Therefore, to ensure the reliability and longevity of buildings, it is necessary to have experts periodically carry out building inspections. Building inspection has traditionally been conducted using human-based visual inspection methods as well as artificial intelligence methods that have shown great success in computer vision in recent years. In this study, 9 different models (Xception, VGG16, ResNet101, InceptionV3, InceptionResNetV2, MobileNetV2, DenseNet169, NASNetMobile, and EfficientNetB6), which have shown significant success in the field of artificial intelligence, are discussed to detect and classify cracks in building structures. In addition, a new fusion model structure called Mobile-DenseNet has been proposed by making block cutting and adding auxiliary layers to the MobileNetV2 and DenseNet169 model structures. With this proposed model structure, cracks in concrete structures were classified. A dataset consisting of concrete surface images was used to detect and classify cracks occurring in concrete structures, and a 99.87 % success rate was achieved with the proposed Mobile-DenseNet model in classifying cracks occurring on the concrete surface. The proposed model outperformed the traditional pretrained model structures in the study in terms of the number of transactions, density, features, complexity, and success accuracy.

Exploring low-grade clay minerals diving into limestone calcined clay cement (LC3): Characterization − Hydration − Performance

The physicochemical properties of calcined kaolinite clay have an impact on the hydration kinetics of limestone calcined clay cements (LC3). Therefore, this study compares the effects of two types of common clays, namely low-grade kaolinite (LGK) and mixed minerals (MIX), on the characterization, heat of hydration, setting times, and strength development mechanisms in LC3. The characterization of calcined clays is initially conducted. After conducting heat of hydration, TGA, and XRD analyses, the phase assemblage of both LC3 and ordinary Portland cement (OPC) systems is investigated. This is followed by quantifying the amounts of AFt/AFm, portlandite, and calcite. The more amorphous nature of the LGK compared to the MIX results in the formation of Stratlingite, which contributes to gaining strength at an early age. Hemi-carbonates and mono-carbonates are useful for achieving adequate compressive strength. The LC3 exhibits a lower heat of hydration compared to the OPC, which is advantageous in reducing the likelihood of thermal cracking in concrete structures. However, the LC3-MIX exhibits a higher heat of hydration after 36 h due to an excess of alkali content in the clay mixture. Moreover, the compressive strength of the LC3 exhibited a striking similarity to that of the OPC.

Fabrication of Microcapsule-Type Long-Lasting Phosphorescent Sensor for Concrete Crack Monitoring

The generation and expansion of cracks in concrete structures reduce the durability and safety of structures. In order to detect cracks in concrete structures, a long-lasting phosphorescent microcapsule coating is proposed in this study. The microcapsule-based sensor is pasted on the surface of cement-based materials and solidified. The microcapsules become ruptured and cause the core material to flow out when the microcracks occur on the material, which emits strong phosphorescence at the cracked position under UV irradiation. The results indicate that the successful encapsulation of microcapsules could enhance the thermal stability of phosphorescent dye. The excitation wavelength of the phosphorescent microcapsules is also investigated. The phosphorescent microcapsules could effectively highlight unnoticeable cracks by a long-lasting phosphorescence response in the cracking region. The mechanical properties of microcapsules/epoxy resin composite coatings were studied, and the optimal content of microcapsules in the coating was determined. The as-fabricated phosphorescent microcapsules have good damage-sensing effects in conditions of different light and temperatures. The method proposed in this study will assist in the further development of damage-sensing material in the field of concrete crack monitoring.

Localization of Cracks in Concrete Structures Lacking Reference Objects and Feature Points Using an Unmanned Aerial Vehicle

Information on the location of cracks in concrete structures is an important factor enabling appropriate maintenance or reinforcement measures to be taken. Most studies related to concrete cracks are limited to crack detection and identification, and studies related to crack location information are insufficient. The novelty of this study is to develop application technology related to crack localization by proposing a methodology that can estimate the location of concrete cracks even when reference objects or feature points are lacking using an unmanned aerial vehicle and image processing techniques. For the development and verification of the proposed method, aerial photography and image acquisition were performed using mounting a laser pointer model on an unmanned aerial vehicle. To build the analysis data, image distortion correction and feature point extraction were performed using the homography matrix and scale-invariant feature transform algorithm. Spatial information was established using the point cloud technique and image stitching technique, and crack localization was estimated using generating crack expression data via layer merging. The proposed method was validated using comparison with field-measured data. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

A Rapid Bridge Crack Detection Method Based on Deep Learning

The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concrete crack images were generated by a deep convolutional generative adversarial network (DCGAN), and the artificial samples were balanced and feature-rich. Then, the dataset was established by mixing the artificial samples with the original samples. You Only Look Once v5 (YOLOv5) was trained on this dataset to implement rapid detection of concrete bridge cracks, and the detection accuracy was compared with the results using only the original samples. The experiments show that DCGAN can mine the potential distribution of image data and extract crack features through the deep transposed convolution layer and down sampling operation. Moreover, the light-weight YOLOv5 increases channel capacity and reduces the dimensions of the input image without losing pixel information. This method maintains the generalization performance of the neural network and provides an alternative solution with a low cost of data acquisition while accomplishing the rapid detection of bridge cracks with high precision.

Finite element modeling of early-age temperature development of in-situ concrete under variable ambient temperatures

The early-age temperature rises in in-situ concrete, caused by the exothermic hydration of cement, poses a significant risk of thermal cracking in concrete structures. This issue is particularly critical in large-scale structures, where thermal cracking can lead to serious structural integrity issues. Therefore, the accurate prediction of the concrete hydration temperature development is paramount for predicting and mitigating early thermal cracking in concrete. However, one of the main challenges in predicting in-situ concrete temperatures is the significant effect of variable ambient temperature on cement hydration rates and concrete heat dissipation. To address this challenge, this paper presents a finite element modeling (FEM) approach that accurately predicts the early-age temperature development of in-situ concrete by adequately considering the effect of variable ambient temperatures. The model integrates the heat of hydration of the cement, the impact of ambient temperature variations, and the heat transfer through the concrete and insulation mould. The cement hydration rate under real-life ambient temperatures is simulated using an Arrhenius-based method, which mathematically adjusts the isothermal calorimetry curves to reflect the actual conditions. Additionally, the study investigates the inhibitory effect of Ground Granulated Blast-furnace Slag (GGBS) on the early hydration temperature of concrete, with experimental tests and FEM simulations conducted on concrete mixes where up to 70% of the cement was replaced with GGBS. The validity of the FEM model was confirmed through comparison with concrete semi-adiabatic tests, demonstrating that the model provides accurate predictions of the temperature development of in-situ concrete. Importantly, the results indicate that the adjusted isothermal calorimetry curves successfully capture the ambient temperature effect on the cement hydration rate. The results provide valuable insights for the design of concrete structures, particularly in preventing early-age thermal cracking.