Concrete Defects Research Articles

A comparison study on Australia, Eurocode, and US reinforced concrete structure repair codes

Currently, there is no local standard on reinforced concrete structures repairing in Malaysia. The aim of this study is to discuss the similarities and differences of the reinforced concrete structure repair codes of Australia, Eurocode, and United States.In the methodology and work plan stage, preliminary decision for concrete repair is made after the structural assessment. Next, repair work is conducted in relation to the general planning. Lastly, repair products applied in the system are ensured to be mutually compatible in order to accept the repair work. According to the 11 principles listed in EN 1504-9, concrete damages are classified as concrete defects and corrosion on reinforced concrete structure. SA HB 84:2018 is using the fundamental principles of EN 1504 as the main reference. Similarly, all three codes highlight the reinforcement corrosion as the primary reason of concrete damage. The main difference between ACI 546R-14 and EN 1504 is ACI 546R-14 provides no principle for all intended uses in repair works. Two case studies of reinforced concrete structure repairing for irrigation control structures and low-cost flat building are discussed. The case studies reveal a possibility of combining the strengths of a selected standards in reinforced concrete structures repair works.

Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework.

Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The Faster RCNN ResNet50, Faster RCNN ResNet101, and Faster RCNN Inception-ResNet-v2 deep learning frameworks were trained using a transfer learning scheme with six typical concrete defect classes and deployed in an IoRT framework remote defect detection task. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trials using the SLAM technique. The experimental results indicate that robot’s maneuverability was stable, and its mapping and localization were also accurate in different drain types. Finally, for effective drain maintenance, the SLAM-based defect map was generated by fusing defect detection results in the lidar-SLAM map.

Nondestructive testing algorithm of building concrete material defects based on machine learning

In order to pay more attention to the quality of construction concrete and accurately judge whether concrete material meets the standard, a nondestructive testing algorithm of building concrete material defects based on machine learning is proposed. Through the ray tracing algorithm of Snell’s theorem, the shortest path between two random punctuation marks of building concrete is calculated. The original coordinate system and grid size were set, the trend and length of the line in the grid were calculated, and the coordinates between the grid corner points and the transmitting probe were calculated so as to obtain the position of the intermediate refractive points of the two probes. Finally, the vector dot product of the local defects is obtained by the optimal hyperplane calculation of the binary classification in the support vector machine. Experimental results show that the proposed method has the advantages of high precision.

Resistance to Sulfate Attack and Chemo-Damage-Transport Model of Sulfate Ions for Tunnel Lining Concrete under the Action of Loading and Flowing Groundwater

In this study, resistance to sulfate attack and a chemo-damage-transport model of sulfate ions for tunnel lining concrete under the action of loading and flowing groundwater are investigated. The results show that in the initial stage of erosion, the relative dynamic elastic modulus of tunnel lining concrete and the sulfate ion concentration increase gradually. However, in the subsequent stage of erosion, the relative dynamic elastic modulus of tunnel lining concrete decreases gradually, and the increase in sulfate ion concentration accelerates. The defects of the tunnel lining concrete in the tensile zone and compressive zone can be expanded and closed, respectively, under the action of the loading. Meanwhile, the tunnel lining concrete can be corroded under the action of flowing groundwater. Therefore, the sulfate ion concentration in the tensile zone is the highest under the action of loading and flowing groundwater. The second highest value is obtained under the action of only flowing groundwater, which is followed by the action of static groundwater. The sulfate ion concentration in the pressure zone is the lowest under the action of loading and flowing groundwater. The chemical reaction in the process of sulfate erosion, damage of tunnel lining concrete by crystallization pressure and loading, and influence of flowing groundwater dissolution on calcium leaching are considered in combination, and a weight coefficient is introduced to characterize the influence of pore structure on the crystallization pressure. Finally, a chemo-damage-transport model of sulfate ions is established and its rationality is verified.

Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network.

Concrete is the main material in building. Since its poor structural integrity may cause accidents, it is significant to detect defects in concrete. However, it is a challenging topic as the unevenness of concrete would lead to the complex dynamics with uncertainties in the ultrasonic diagnosis of defects. Note that the detection results mainly depend on the direct parameters, e.g., the time of travel through the concrete. The current diagnosis accuracy and intelligence level are difficult to meet the design requirement for automatic and increasingly high-performance demands. To solve the mentioned problems, our contribution of this paper can be summarized as establishing a diagnosis model based on the GA-BPNN method and ultrasonic information extracted that helps engineers identify concrete defects. Potentially, the application of this model helps to improve the working efficiency, diagnostic accuracy and automation level of ultrasonic testing instruments. In particular, we propose a simple and effective signal recognition method for small-size concrete hole defects. This method can be divided into two parts: (1) signal effective information extraction based on wavelet packet transform (WPT), where mean value, standard deviation, kurtosis coefficient, skewness coefficient and energy ratio are utilized as features to characterize the detection signals based on the analysis of the main frequency node of the signals, and (2) defect signal recognition based on GA optimized back propagation neural network (GA-BPNN), where the cross-validation method has been used for the stochastic division of the signal dataset and it leads to the BPNN recognition model with small bias. Finally, we implement this method on 150 detection signal data which are obtained by the ultrasonic testing system with 50 kHz working frequency. The experimental test block is a C30 class concrete block with 5, 7, and 9 mm penetrating holes. The information of the experimental environment, algorithmic parameters setting and signal processing procedure are described in detail. The average recognition accuracy is 91.33% for the identification of small size concrete defects according to experimental results, which verifies the feasibility and efficiency.

Thermal and Mechanical Properties of Calcium Alginate Capsules for Self-Healing Asphalt Concrete

The key physical and mechanical property is the strength of the capsules, which ensure the implementation of the self-healing technology, in which the capsules are not destroyed during the compaction of the asphalt concrete mixture, but are destroyed during the formation of defects in the asphalt concrete. An increase in the content of the reducing agent in the composition of the alginate emulsion leads to a decrease in the breaking load during compression of the capsules, which is explained by an increase in their diameter. But the change in the content of sodium alginate does not have a significant effect on mechanical properties. As a result of exposure to a temperature of 170 °C, a decrease in the strength of the capsules by 22 % after 1 hour of exposure in the burning oven is observed, and with an increase in the time to 4 hours, the strength decreases by 46.9 %. The maximum decrease in the strength index after 4 hours of exposure at a temperature of 160 °C reaches 29.9 %. A decrease in temperature to 150 °C leads to a decrease in the loss of strength. The strength of the capsules decreases by 4 % after 4 hours of exposure at 150 °C. Exposure of capsules to a temperature of 140 °C has no significant effect on strength.

Assessment on the Technical State of Spillway Concrete Gravity Dams in Lithuania

Spillway concrete gravity dams are exposed by loads and environment causing deterioration process. The article presents the concrete structure deteriorations, defects and technical state evaluation results of 34 Lithuanian spillway concrete gravity dams performed in 2006–2018. The typical deteriorations are: in piers’– crumbled protective layer of concrete, its corrosion, reinforcement corrosion, cracks in concrete, chipped edges; in spillway constructions’– concrete corrosion, bio–corrosion, concrete corrosion caused by cavitation; in outflow constructions – concrete corrosion, bigger cracks and deformations. According to results of technical state research, it was found that only 3% (1 SCGD) dams are in good technical state, 27% (9 SCGD) – moderate, 47% (16 SCGD) – satisfactory, 12% (4 SCGD) – unsatisfactory and 12% (4 SCGD) – in critical state.

ANALISA KOMPARATIF ANTARA ACUAN PERANCAH SEMI SISTEM DENGAN ACUAN PERANCAH ALUMINIUM. STUDI KASUS : PROYEK RANCANG BANGUN RUMAH SUSUN, PONDOK CINA, DEPOK

In selecting the formwork work method, the things that need to be considered include cost, quality, and time. Comparison of several methods of formwork work is carried out to obtain the correct method. Comparisons were made to the Semi System formwork method and the Aluminum formwork method. To compare the two methods requires volume calculation, the multiplier coefficient referred to by the PUPR Regulation No. 28 / PRT / M / 2016, non-conformence data, and work duration. This data is processed into the need for the cost of formwork for the two methods based on the PUPR Ministerial Regulation Number 28 / PRT / 2016, the percentage of concrete defects due to formwork, and the scheduling duration presented in the bar chart. The two methods are compared based on the need for the cost of the work, the number of percent of quality, and the work duration. The comparison shows that the aluminum formwork method is superior in terms of cost (RAB). It is produced more efficiently by 3.4%. In terms of quality, this method results in less damage (concrete defects) to minimize repair costs later, and when the work reaches 43% more effective.

Study on Nondestructive Test Method for Inner Defects of Mass Concrete

Due to the large geometric size and the influence of dense embedded steel bars, It is difficult to use the traditional nondestructive testing (NDT) method for evaluation the inner defects of mass concrete. A comprehensive nondestructive testing method for internal defects of mass concrete was proposed, which used elastic wave Computed Tomography (CT) scanning for general survey, ultrasonic tomography and ground penetrating radar (GPR) technology for fine investigation. The method was applied to the defect detection of a bearing platform concrete belonging to an interchange bridge. Finally, the reliability of this method was verified by the results of core drilling. The results show that the proposed method can effectively detect the distribution of defects in concrete, provided a new solution and practical guidance for the internal defect detection and reinforcement of mass concrete structure.

Pile defect quality control analysis on construction company in East Java

A production process has a possibility of product defects; the types of defects commonly found in pile products are porous concrete defects, dented joint plate defects, pile dimensions deviation defects, etc. The defect of the product needs to be controlled because it can cause losses to the company. Therefore, in this study, data processing was conducted on pile defect products using statistical process control. An analysis was then performed using the Pareto Chart to determine the types of defects that occur most often. After that, identifying the problems causes that occur in terms of man, machine, method, materials, money, and environment by using fishbone diagrams; so that recommendations for improvement can be given to the company. The results showed that, based on the Pareto principle, 80% of the defects types that occur include the type of porous concrete defects, the type of dented sheath plate defects, and types of joint plate dimension defects. The type of defect with the highest frequency is a porous concrete defects type. Recommendations for improvement that can be given include providing training to workers, maintaining machinery, improving product quality, and providing alternative electrical energy sources.

A fuzzy-logic model for benchmarking concrete roof maintenance

PurposeConcrete structures undergo early and fast deterioration, which causes defects such as cracks, water leaks and delamination, resulting from a lack of or inefficient maintenance practices. To improve this behaviour, this paper aims to develop a maintenance strategy benchmarking model for concrete structures.Design/methodology/approachFuzzy logic toolbox on MATLAB R2018a was used to develop the proposed model and it was applied to two cases. A comprehensive literature search was done to review common concrete defects, their impact on the performance and functionality of the structure, effectiveness of maintenance strategies and previous maintenance benchmarking models. The literature findings were further validated through expert interviews which have been incorporated in the model.FindingsCase study results show that preventive maintenance (PM), predictive maintenance (PdM) and corrective maintenance (CM) strategies are required more or less in similar combinations for maintenance of concrete roof structures. The best combination for case 1 is 36.42% PM, 35.40% PdM and 28.18% CM, and for case 2 is 35.93% PM, 35.08% PdM and 28.99% CM. According to suitability, they can be ranked as PM > PdM > CM.Originality/valueThis model will contribute as a comprehensive decision-making tool for building/facility managers. The findings further carry a strong message to those who practice only CM in their buildings.

Simultaneous pixel-level concrete defect detection and grouping using a fully convolutional model

Deep learning techniques have attracted significant attention in the field of visual inspection of civil infrastructure systems recently. Currently, most deep learning-based visual inspection techniques utilize a convolutional neural network to recognize surface defects either by detecting a bounding box of each defect or classifying all pixels on an image without distinguishing between different defect instances. These outputs cannot be directly used for acquiring the geometric properties of each individual defect in an image, thus hindering the development of fully automated structural assessment techniques. In this study, a novel fully convolutional model is proposed for simultaneously detecting and grouping the image pixels for each individual defect on an image. The proposed model integrates an optimized mask subnet with a box-level detection network, where the former outputs a set of position-sensitive score maps for pixel-level defect detection and the latter predicts a bounding box for each defect to group the detected pixels. An image dataset containing three common types of concrete defects, crack, spalling and exposed rebar, is used for training and testing of the model. Results demonstrate that the proposed model is robust to various defect sizes and shapes and can achieve a mask-level mean average precision (mAP) of 82.4% and a mean intersection over union (mIoU) of 75.5%, with a processing speed of about 10 FPS at input image size of 576 × 576 when tested on an NVIDIA GeForce GTX 1060 GPU. Its performance is compared with the state-of-the-art instance segmentation network Mask R-CNN and the semantic segmentation network U-Net. The comparative studies show that the proposed model has a distinct defect boundary delineation capability and outperforms the Mask R-CNN and the U-Net in both accuracy and speed.

Detection of Hidden Disease of Concrete Bridge Based on Infrared Thermal Imaging

Any object with temperature above absolute zero can radiate infrared energy outward [1]. According to this characteristic, the infrared thermal imaging method is applied to diseases of concrete bridges such as internal voids. The relationship between the hidden disease of concrete bridge and infrared thermal image is analyzed theoretically. The concrete model with internal cavity defects is established and the finite element simulation analysis is carried out to verify the feasibility of infrared thermal imaging method to detect the internal defects of bridge concrete. The final finite element simulation results are in agreement with the theoretical analysis, indicating that the method of detecting the internal defects of bridge concrete by infrared thermal imaging is feasible.

Detection of Concrete Structural Defects Using Impact Echo Based on Deep Networks

Abstract Deep learning is widely used in image processing, which significantly improves the performance of image classification detection. Based on the current status of concrete structure defect detection technology, this experimental study on the detection of concrete structure defects using impact echo was conducted. Focusing on the unsteady features of the impact echo signal, we adopted wavelet transforms at different scales to extract the wavelet spectrum. At the same time, the convolution and subsample operation were combined to establish the recognition system of concrete structure defect detection based on the deep learning network. The research results show that this system can accurately recognize defects in the concrete structure and has high detection accuracy in the concrete structure assessment process.

Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification.

Automatic machine classification of concrete structural defects in images poses significant challenges because of multitude of problems arising from the surface texture, such as presence of stains, holes, colors, poster remains, graffiti, marking and painting, along with uncontrolled weather conditions and illuminations. In this paper, we propose an interleaved deep artifacts-aware attention mechanism (iDAAM) to classify multi-target multi-class and single-class defects from structural defect images. Our novel architecture is composed of interleaved fine-grained dense modules (FGDM) and concurrent dual attention modules (CDAM) to extract local discriminative features from concrete defect images. FGDM helps to aggregate multi-layer robust information with wide range of scales to describe visually-similar overlapping defects. On the other hand, CDAM selects multiple representations of highly localized overlapping defect features and encodes the crucial spatial regions from discriminative channels to address variations in texture, viewing angle, shape and size of overlapping defect classes. Within iDAAM, FGDM and CDAM are interleaved to extract salient discriminative features from multiple scales by constructing an end-to-end trainable network without any preprocessing steps, making the process fully automatic. Experimental results and extensive ablation studies on three publicly available large concrete defect datasets show that our proposed approach outperforms the current state-of-the-art methodologies.

Defect Recognition in Concrete Ultrasonic Detection Based on Wavelet Packet Transform and Stochastic Configuration Networks

Aiming to detect concrete defects, we propose a new identification method based on stochastic configuration networks. The presented model has been trained by time-domain and frequency-domain features which are extracted from filtering and decomposing ultrasonic detection signals. This method was applied to ultrasonic detection data collected from 5 mm, 7 mm, and 9 mm penetrating holes in C30 class concrete. In particular, wavelet packet transform (WPT) was then used to decompose the detected signals, thus the information in different frequency bands can be obtained. Based on the data from the fundamental frequency nodes of the detection signals, we calculated the means, standard deviations, kurtosis coefficients, skewness coefficients, and energy ratios to characterize the detection signals. We also analyzed their typical statistical features to assess the complexity of identifying these signals. Finally, we used the stochastic configuration networks (SCNs) algorithm to embed four-fold cross-validation for constructing the recognition model. Based upon the experimental results, the performance of the presented model has been validated and compared with the genetic algorithm based on BP neural network model, where the comparison shows that the SCNs algorithm has superior generalization abilities, better fitting abilities, and higher recognition accuracy for recognizing defect signals. In addition, the test and analysis results show that the proposed method is feasible and effective in detecting concrete hole defects.

Application of ground penetrating radar in concrete defect detection of sluice floor

The defect investigation of sluice floor is an important measure to ensure the safe operation of the sluice. due to the traditional drilling methods will cause damage to the sluice floor, and the appearance inspection and compressive strength test can not evaluate the internal defects of reinforced concrete in the sluice floor. In this paper, we apply the ground penetrating radar(GPR) to the concrete quality inspection of a sluice gate in a seawall. And we describe the layout, detection principle and data processing flow of GPR. Then we simulate the propagation characteristics of radar electromagnetic wave in the presence of twodimensional electrical anomaly bodies by finite difference method. Through analyzing the amplitude, frequency and event characteristics of reflection wave shown in the gray map of GPR profiles, we determine the dividing line between concrete and muddy silty clay, finding out the density of steel in concrete, framing the location of construction joints and suspected water-filled gaps.

Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks

Using Unmanned Aerial Systems (UASs) for bridge visual inspection automation necessitates the implementation of Deep Convolutional Neural Networks (DCNNs) to process efficiently the large amount of data collected by the UASs sensors. However, these networks require massive training datasets for the defects recognition and detection tasks. In an effort to expand existing concrete defects datasets, particularly concrete cracks in bridges, this paper proposes a public benchmark annotated image dataset containing over 6900 images of cracked and non cracked concrete bridges and culverts. The presented dataset includes some challenging surface conditions and covers concrete cracks with different sizes and patterns. The authors analyzed the proposed dataset using three state of the art DCNNs in Transfer Learning mode. The three models were used to classify the cracked and non cracked images and the best testing accuracy obtained reached 95.89%. The experimental results showcase the potential use of this dataset to train deep networks for concrete crack recognition in bridges. The dataset is publicly available at https://github.com/MCBDD-ZRE/Concrete-Bridge-Crack-Dataset- for academic purposes.

Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification

In this work, we propose a deep multi-deformation aware attention learning (MDAL) architecture comprising of multi-scale committee of attention (MSCA) and fine-grained feature induced attention (FGIA) modules to classify multi-target multi-class defects in concrete structures found in civil infrastructures. The MDAL network is composed of interleaved MSCA and FGIA modules to encode crucial fine-grained deformation-aware information from concrete images. The novel attention mechanism is able to localize specific defect regions within an image and extracts crucial discriminative information in multi-scale fashion ranging from coarser to finer features without using any preprocessing step, such as region-of-interest selection or denoising. Our proposed attention mechanism enables the MDAL architecture to automatically classify multiple overlapping defect classes present in the concrete images and leads to an end-to-end trainable deep network. Experimental results on three large concrete defect datasets and ablation studies show that our MDAL network outperforms the current state-of-the-art methodologies significantly.

Selecting main technical specifications of the data aquisition system to improve the accuracy of using the electric response parameters

Based on the conducted experimental studies, the necessary parameters were selected to provide reliable testing of concrete defects. The measuring system must have: input sensitivity of the receiving electromagnetic channel of at least100 μV, the working frequency range of 1–120 kHz; the sampling frequency of 500 kHz, as well as a device that allows fast Fourier transform. The registration of the electrical response during an elastic impact should be made by a differential electric sensor, which allows measurements under conditions of external electromagnetic interference.