Concrete Delamination Research Articles

Ensemble learning model for concrete delamination depth detection using impact echo

Delamination in concrete structures typically arises from the expansion of steel bar corrosion and the separation of the bonding layer. The presence of these delamination defects leads to a reduction in overall structural integrity, safety, and longevity. However, the delamination depth localization remains challenging. To address this issue, this paper introduces a predictive approach that combines time-frequency features extracted from vibration signals with ensemble learning models. The innovation presented in this paper can be summarized in two key aspects: (1) Signal time-frequency spectrograms are transformed into image features using the Histogram of Oriented Gradients (HOG) method, which serves as input attributes. The utilization of ensemble learning models facilitates both rapid and precise depth estimation of delamination signals, especially those with shallow defects. (2) By analyzing the time-frequency spectrogram of the vibration signals, a double exponential model is established between the depth-to-thickness ratio of delamination and the pixel matrix Eulerian distance. This may provide a new perspective for the depth estimation of delamination defects whose corresponding main frequencies are difficult to locate due to the influence of geometry and boundary effects. Laboratory experiments were conducted on concrete slab with simulated delamination defects at different depths to confirm the proposed method's effectiveness.

Nondestructive Testing (NDT) for Damage Detection in Concrete Elements with Externally Bonded Fiber-Reinforced Polymer

Fiber-reinforced polymer (FRP) composites offer a corrosion-resistant, lightweight, and durable alternative to traditional steel material in concrete structures. However, the lack of established inspection methods for assessing reinforced concrete elements with externally bonded FRP (EB-FRP) composites hinders industry-wide confidence in their adoption. This study addresses this gap by investigating non-destructive testing (NDT) techniques for detecting damage and defects in EB-FRP concrete elements. As such, this study first identified and categorized potential damage in EB-FRP concrete elements considering where and why they occur. The most promising NDT methods for detecting this damage were then analyzed. And lastly, experiments were carried out to assess the feasibility of the selected NDT methods for detecting these defects. The result of this study introduces infrared thermography (IR) as a proper method for identifying defects underneath the FRP system (wet lay-up). The IR was capable of highlighting defects as small as 625 mm2 (1 in.2) whether between layers (debonding) or between the substrate and FRP (delamination). It also indicates the inability of GPR to detect damage below the FRP laminates, while indicating the capability of PAU to detect concrete delamination and qualitatively identify bond damage in the FRP system. The outcome of this research can be used to provide guidance for choosing effective on-site NDT techniques, saving considerable time and cost for inspection. Importantly, this study also paves the way for further innovation in damage detection techniques addressing the current limitations.

Objective characterisation of reinforced concrete with progressive corrosion defects through clustering and thresholding of infrared images

The research in this paper focuses on detection and quantification of subsurface damage in reinforced concrete (RC) structures by the analysis of infrared images. Experimental investigations were performed on RC slabs with embedded reinforcing bars (rebars). The corrosion mechanism in rebars was accelerated using an electrical circuit setup. The initial temperature of the slabs was adjusted in the environmental chamber and IR images were taken at regular intervals while they were exchanging heat to laboratory air through convection. Records of IR images were post-processed using an objective thresholding method proposed based on unsupervised k-means clustering. The results were discussed in context and with respect to the ASTM D 4788–03 recommendation for minimum detectable thermal contrast (0.5 °C) from delamination in bridge concrete by infrared thermography (IRT). The conclusions lead to recommendations for improved practical implementation of IRT that contribute to the broader prospects of structural health monitoring (SHM).

Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology

Bridge inspection methods using unmanned vehicles have been attracting attention. In this study, we devised an efficient and reliable method for visually inspecting bridges using unmanned vehicles. For this purpose, we developed the BIRD U-Net algorithm, which is an evolution of the U-Net algorithm that utilizes images taken by unmanned vehicles. Unlike the U-Net algorithm, however, this algorithm identifies the optimal function by setting the epoch to 120 and uses the Adam optimization algorithm. In addition, a bilateral filter was applied to highlight the damaged areas of the bridge, and a different color was used for each of the five types of abnormalities detected, such as cracks. Next, we trained and tested 135,696 images of exterior bridge damage, including concrete delamination, water leakage, and exposed rebar. Through the analysis, we confirmed an analysis method that yields an average inspection reproduction rate of more than 95%. In addition, we compared and analyzed the inspection reproduction rate of the method with that of BIRD U-Net after using the same method and images for training as the existing U-Net and ResNet algorithms for validation. In addition, the algorithm developed in this study is expected to yield objective results through automatic damage analysis. It can be applied to regular inspections that involve unmanned mobile vehicles in the field of bridge maintenance, thereby reducing the associated time and cost.

Quantifying Shallow-Depth Concrete Delamination using Impact-Echo

Corrosion-induced concrete deck delamination is quite common in bridges. Locating these embedded delaminations is important to assess the extent of the damage, the remaining member capacity and any necessary rehabilitation. The impact-echo (IE) is a simple and straightforward non-destructive testing (NDT) technique by which the depth and extent of concrete delamination may be estimated. It is effective in detecting the location and determining the depth of relatively deep delaminations in concrete. Delaminations that occur near the concrete surface can also be detected by the IE method. However, the exact depth cannot be quantified due to difficulties in analysing the flexural mode that dominates the vibration response over the corresponding delaminated region. This study developed an IE-based procedure to quantitatively estimate the depth of shallow depth delaminations in concrete slab members. Four slab specimens were prepared, each with three artificial delaminations with varying shapes and sizes at shallow depths. The frequency contour maps showed good agreement with the actual location of the delaminations. The perimeter-to-depth ratio of the delaminated region can be used to analyze the flexural mode vibration frequency. Two equations are proposed that relate the depth of arbitrarily shaped delaminations to the flexural mode vibration frequency measured over the shallow-depth delaminations. Keywords: impact echo, concrete delamination, concrete slabs, flexural mode vibration, non-destructive evaluation (NDE)

Numerical Analysis of Delamination of High-Performance Concrete (HPC) Strengthened Reinforced Concrete (RC) Beam

Abstract This study investigates the numerical analysis of delamination behavior of high-performance concrete (HPC) strengthened reinforced concrete (RC) cantilever T-beam by using finite element method. A length of 1000 mm reinforced concrete cantilever T-beam and height of 1600 mm reinforced concrete column will be created. Besides that, the truss members such as the stirrups and the rebars will also be added into the structure as a reinforcement. The HPC layer will be assigned on the top of the cantilever T-beam as a coating material. The study is to investigate the influences on the delamination behavior when the thickness, width, and coefficient of friction of the HPC layer has been applied on the model as a coating material. Finite element model (FEM) will need to be created and justified in ABAQUS software before applying the parameters for analysis. Limited studies had been conducted on the delamination behavior of using HPC layer as a strengthening mechanism and analyze the output such as the COPEN and SDEG. Based on the results extracted from the software, the delamination behavior can be observed and analyzed and investigate the influence of difference parameter has been assigned. Therefore, this research paper will analyze the influence of the layer thickness, width of the layer, and the coefficient of friction between the substrate and the layer.

Performance Evaluation of Uncooled UAV Infrared Camera in Detecting Concrete Delamination

Concrete delamination detection using unmanned aerial vehicle (UAV)-mounted infrared cameras has proved effective in recent research. However, most studies used expensive research-grade infrared cameras and proprietary software to acquire images, which is hard to implement in state departments of transportation (DOTs) due to the lack of specialty professionals. Some state DOTs started deploying lightweight UAV-based consumer-grade infrared cameras for delamination detection. Quantitative performance evaluation of such a camera in concrete delamination detection is lacking. To fill this gap, this study intends to conduct a comprehensive assessment of the consumer-grade camera benchmarked against the results of a research-grade camera to see the practicality of using the small and low-cost camera in concrete delamination detection. Data was collected for a slab with mimicked delamination and two in-service bridge decks. For the case of the slab, maximum detectability of 70–72% was achieved. A transient numerical simulation was conducted to provide a supplemental and noise-free dataset to explore detectability accuracy peaks throughout the day. The results of the in-service bridge decks indicated that the consumer-grade infrared camera provided adequate detection of the locations of suspected delamination. Results of both the slab and in-service bridge decks were comparable to those of a research-grade infrared camera.

Impact of Corrosion on the Bond Strength between Concrete and Rebar: A Systematic Review.

Corrosion of the reinforcement affects more than the cross-sectional area of the rebar. The volume of steel also increases due to expansive corrosion products, leading to the cracking, delamination, and spalling of concrete. As a result, the bond capacity between concrete and rebar is affected. Researchers have extensively examined the impact of corrosion on the bond strength between concrete and rebar to propose empirical, theoretical, or numerical predictive models. Therefore, research programs on this topic have increased rapidly in recent years. This article presents a systematic literature review to explore experimental methods, outcomes, and trends on this topic. The Web of Science search collected 84 relevant research articles through a rigorous selection. Key factors that affect bond strength degradation, including concrete cover, concrete strength, and stirrups, have been documented. However, a general model is still unavailable due to discrepancies caused by differences in testing methods to evaluate the effect of corrosion on bond strength. Furthermore, researchers attempted to clarify the degradation mechanism of bond strength affected by corrosion. As a result, new alternatives have been proposed to build a practical model to assess the bond strength deterioration of corroded structures.

SDNET2021: Annotated NDE Dataset for Subsurface Structural Defects Detection in Concrete Bridge Decks

Annotated datasets play a significant role in developing advanced Artificial Intelligence (AI) models that can detect bridge structure defects autonomously. Most defect datasets contain visual images of surface defects; however, subsurface defect data such as delamination which are critical for effective bridge deck evaluations are typically rare or limited to laboratory specimens. Three Non-Destructive Evaluation (NDE) methods (Infrared Thermography (IRT), Impact Echo (IE), and Ground Penetrating Radar (GPR)) were used for concrete delamination detection and reinforcement corrosion detection. The authors have developed a unique NDE dataset, Structural Defect Network 2021 (SDNET2021), which consists of IRT, IE, and GPR data collected from five in-service reinforced concrete bridge decks. A delamination survey map locating the areas, extent and classes of delamination served as the ground truth for annotating IRT, IE and GPR field tests’ data in this study. The IRT were processed to create an ortho-mosaic maps for each deck and were aligned with the ground truth maps using image registration, affine transformation, image binarization, morphological operations, connected components and region props techniques to execute a semi-automatic pixel–wise annotation. Conventional methods such as Fast Fourier transform (FFT)/peak frequency and B-Scan were used for preliminary analysis for the IE and GPR signal data respectively. The quality of NDE data was verified using conventional Image Quality Assessment (IQA) techniques. SDNET2021 dataset consists of 557 delaminated and 1379 sound IE signals, 214,943 delaminated and 448,159 sound GPR signals, and about 1,718,083 delaminated and 2,862,597 sound IRT pixels. SDNET2021 addresses one of the major gaps in benchmarking, developing, training, and testing advanced deep learning models for concrete bridge evaluation by providing a publicly available annotated and validated NDE dataset.

Vibration characteristics of spalling defects in concrete lining

The mechanization of the hammering test, which checks the delamination or damage of concrete using a hammer, is required to improve the efficiency of inspections. To realize mechanization, the characteristics of defects with high risk of spalling must be quantified. The objective of this study was to investigate the vibration characteristics. To this end, concrete specimens with inclined detachment were prepared and the vibration measurement, detachment process, and loading were repeated until spalling occurred. In the loading process, it was assumed that the defect existed at the tunnel crown, and the self-weight was considered. The natural frequencies, damping ratios, and response amplitudes for each state are presented in this paper. Additionally, numerical analysis results are presented to supplement the experimental results. The experimental results reveal that spalling did not occur until the adhesion area and frequency of the lowest mode significantly decreased. The frequency of the lowest mode just before spalling is expected to be less than 100Hz. Based on the simulation results obtained for an actual severe concrete spalling accident in Fukuoka Tunnel, it is concluded that the ability to detect defects with the response amplitude of 1mm/s/N is required to mechanize the hammering test.

Practical service life assessment and rehabilitation strategy development for hyperbolic shell natural draft cooling towers

Years of service in industrial plant operating environments can cause varying types and degrees of concrete deterioration on reinforced concrete hyperbolic shell natural draft cooling towers. Exposure to plant operating cooling water can potentially result in concrete material erosion and corrosion of embedded reinforcing steel and associated concrete delamination and spalling over time. Such deterioration can affect the integrity of the structure to the degree that repair programs are required to reliably maintain the towers for their required service life. Condition assessment of natural draft cooling towers is challenging due to their massive size, hyperbolic geometry, and plant operational considerations (i.e. schedule, risk and cost). A case history is presented illustrating a rational and targeted investigation approach that included a combination of field condition survey, laboratory materials testing, consideration of operational constraints, probabilistic-based concrete deterioration service life modeling, and structural analysis. Existing degradation as well as predicted conditions from service life modeling informed structural analyses that were performed to identify risk-based limit states over time. The multi-faceted approach provided information critical to understand degradation mechanisms, characterize structural health, and develop prioritized repair strategies to obtain the desired service life extension.

An Autonomous Wall Climbing Robot for Inspection of Reinforced Concrete Structures: SIRCAUR

A wall climbing inspection robot has been designed to climb on safety critical concrete structures by adhering to reinforcement steel bars (rebars) using permanent magnets to generate the adhesion forces. Simulation and experimental validation has been performed to determine the optimum flux focusing magnet configurations with the robot operating on 30 to 35mm of concrete cover over rebars arranged in different patterns. The goal of adhesion force optimization is to be able to carry a ground penetrating radar (GPR) sensor which detects rebar corrosion, concrete delamination and concrete cover deterioration. The autonomous robot uses an ultra-wide band (UWB) localisation system and GPR data to control its motion trajectories to avoid regions where there is insufficient density of rebars. Non-destructive testing (NDT) inspection data acquired by GPR is transmitted wirelessly to a ground station for processing and monitoring by NDT technicians.

Activated Ductile CFRP NSMR Strengthening.

Significant strengthening of concrete structures can be obtained when using adhesively-bonded carbon fiber-reinforced polymer (CFRP) systems. Challenges related to such strengthening methods are; however, the brittle concrete delamination failure, reduced warning, and the consequent inefficient use of the CFRP. A novel ductile near-surface mounted reinforcement (NSMR) CFRP strengthening system with a high CFRP utilization is introduced in this paper. It is hypothesized that the tailored ductile enclosure wedge (EW) end anchors, in combination with low E-modulus and high elongation adhesive, can provide significant strengthening and ductility control. Five concrete T-beams were strengthened using the novel system with a CFRP rod activation stress of approximately 980 MPa. The beam responses were compared to identical epoxy-bonded NSMR strengthened and un-strengthened beams. The linear elastic response was identical to the epoxy-bonded NSMR strengthened beam. In addition, the average deflection and yielding regimes were improved by 220% and 300% (average values), respectively, with an ultimate capacity comparable to the epoxy-bonded NSMR strengthened beam. Reproducible and predictable strengthening effect seems obtainable, where a good correlation between the results and applied theory was reached. The brittle failure modes were prevented, where concrete compression failure and frontal overload anchor failure were experienced when failure was initiated.

Full-resolution 3D imaging for concrete structures with dual-polarization GPR

Ground-penetrating radar (GPR) is one of the most commonly used technologies for concrete inspection. This technology's main limitation is that its raw data are complex and only understood by GPR experts. Thus, this study's ultimate goal was to explore effective strategies for transforming GPR data collected by dual-polarization antennas into a more intuitive form of three-dimensional (3D) images. To understand the strategies' performance, they were implemented on a 1mx1m section of a reinforced concrete slab specimen. The evaluation of the results indicated a great benefit of the dual-polarization GPR system that created an accurate, high-resolution 3D representation of concrete with a single scanning direction dataset. More importantly, while there were differences between the 3D images obtained by different polarization, their combined use provided a comprehensive visualization of the concrete specimen's interior, which displayed reinforcements in two directions, steel bar debonding, and concrete delamination.

Concrete Delamination Depth Estimation Using a Noncontact MEMS Ultrasonic Sensor Array and an Optimization Approach

In this study, we present a method to estimate the depth of near-surface shallow delamination in concrete using a noncontact micro-electromechanical system (MEMS) ultrasonic sensor array and an optimization-based data processing approach. The proposed approach updates the bulk wave velocities of the tested concrete element by solving an optimization problem using reference ultrasonic scanning data collected from a full-depth concrete region. Subsequently, the depth of concrete delamination is estimated by solving a separate optimization problem. Numerical simulations and laboratory experiments were conducted to evaluate the performance of the proposed ultrasonic data processing approach. The results demonstrated that the depth of shallow delamination in concrete structures could be accurately estimated.

Factors affecting the detectability of concrete delamination in GPR images

Concrete delamination is a common type of defects in concrete bridge decks and rigid pavements. While it was reported that ground-penetrating radar (GPR) was able to visualize such types of defects in many instances, it is still unclear about the conditions of success for that application. In such a context, this study aimed to explore various factors that might affect the detectability of concrete delamination in GPR images/signals. In terms of methodology, the study was conducted using both synthetic data generated from a GPR simulation program, and real data collected on a concrete slab specimen. The analysis of such image data revealed the following. First, there is always a waveform reflected from concrete delamination. However, its strength is affected mainly by the thickness of the delamination, the material (air or water) within it, and the peak (most energetic) frequency of the emitted signal. Second, the depth of delamination and its location relative to neighboring steel bars might impact its detectability in GPR images.

Automatic delamination segmentation for bridge deck based on encoder-decoder deep learning through UAV-based thermography

Concrete deck delamination often demonstrates strong variations in size, shape, and temperature distribution under the influences of outdoor weather conditions. The strong variations create challenges for pure analytical solutions in infrared image segmentation of delaminated areas. The recently developed supervised deep learning approach demonstrated the potentials in achieving automatic segmentation of RGB images. However, its effectiveness in segmenting thermal images remains under-explored. The main challenge lies in the development of specific models and the generation of a large range of labeled infrared images for training. To address this challenge, a customized deep learning model based on encoder-decoder architecture is proposed to segment the delaminated areas in thermal images at the pixel level. Data augmentation strategies were implemented in creating the training data set to improve the performance of the proposed model. The deep learning generated model was deployed in a real-world project to further evaluate the model's applicability and robustness. The results of these experimental studies supported the effectiveness of the deep learning model in segmenting concrete delamination areas from infrared images. It also suggested that data augmentation is a helpful technique to address the small size issue of training samples. The field test with validation further demonstrated the generalizability of the proposed framework. Limitations of the proposed approach were also briefed at the end of the paper.

Nondestructive Corrosion Evaluation of Reinforced Concrete Bridge Decks with Overlays: An Experimental Study

Abstract Concrete corrosion induced by deicing agents can decrease the durability of concrete bridge decks by causing cross-sectional loss of reinforcement, concrete delamination, and spalling owing to the expansion of corroded reinforcement. The installation of overlays can extend the service life of the deteriorated decks. However, overlays present challenges in the evaluation of the corrosion condition of the underlying decks. This laboratory study employed three nondestructive testing (NDT) methods to assess the effects of seven types of overlays on corrosion evaluation for concrete bridge decks. The NDT methods were electrical resistivity (ER), ground-penetrating radar (GPR), and half-cell potential (HCP). The ER method could not evaluate the corrosive environment in the concrete decks through the overlays. The GPR method could detect the corrosive environment through four of the seven overlays. The HCP method could detect the decrease of electrical potential over the actively corroded reinforcement for all seven specimens; however, only two of the seven specimens could be identified as having active corrosion per ASTM C876-15, Standard Test Method for Corrosion Potentials of Uncoated Reinforcing Steel in Concrete. Overlay debonding did not affect GPR and HCP testing results in this study.

Evaluation of Delamination in Concrete by IE Testing Using Multi-Channel Elastic Wave Data.

The main objectives of this study are to develop a non-destructive test method for evaluating delamination defects in concrete by the Impact-echo test using multi-channel elastic wave data and to verify the validity of the proposed method by experimental studies in the laboratory. First, prototype equipment using an eight-channel linear sensor array was developed to perform elastic wave measurements on the surface of the concrete. In this study, three concrete slab specimens (1500 mm (width) by 1500 mm (length) by 300 mm (thickness)), with simulated delamination defects of various lateral dimensions and depth, were designed and constructed in the laboratory. Multi-channel elastic wave signals measured on the three concrete specimens were converted to the frequency-phase velocity image by using the phase-shift method. A data processing method was proposed to extract the dominant propagating waves and non-propagating waves from the dispersion images. The dominant wave modes were used to evaluate delamination defects in concrete. It was demonstrated that the surface wave velocity values were useful for characterizing the shallow delamination defects in concrete. In addition, the peak frequency of non-propagating wave modes extracted from the dispersion images gives information on the lateral dimensions and depths of the delamination defects. This study also discussed the feasibility of combined use of the results from propagating and non-propagating wave modes to better understand the information on delamination defects in concrete. As will be discussed, the multi-channel elastic wave measurements enable more accurate, consistent, and rapid measurements and data processing for evaluation of delamination defects in concrete than the single-channel sensing method.

Behavioural study of surface Rayleigh waves in concrete structure containing delamination

Delamination is one of the defects that cause concrete structures to fail in structural integrity. For this reason, the assessment of concrete delamination without affecting the serviceability and functionality of concrete structure is essential. Non-destructive tests (NDT) are developed and applied to serve this purpose without damaging the structure components. In this study, Rayleigh wave (R-wave) is proposed to determine the extent of delamination in concrete. Numerical simulations were carried out to examine the behaviour of R-waves propagated through the concrete medium with different concrete delamination conditions, i.e., top delamination and bottom delamination. The relationships between the changes of R-wave group velocity and peak frequency with the existence of delamination were established. The established data were then verified with experimental measurements. It is found that the R-wave group velocity is reduced in the presence of delamination. Furthermore, the group velocity of R-wave is relatively faster while propagating through the delamination layer which is closer to the testing surface. In addition, the peak frequency retrieved from R-wave power spectrum also reduced in the presence of delamination. Owing to these observed changes, the potential of utilising R-wave group velocity and peak frequency in the assessment of concrete delamination is high.