Impact-echo Testing Research Articles

Assessment of the Conditions of Anchor Bolts Grouted with Resin and Cement Through Impact-Echo Testing and Advanced Spectrum Analysis

Anchor bolts, such as rock bolts and concrete anchors, are widely used in civil, geotechnical, and mining engineering for anchorage and ground support. They are used in retaining walls, dry docks, dams, mines, and prestressed concrete structures. Evaluating the grouting condition of anchor bolts is essential to ensure the safety of these applications. Spectrum techniques have been used to develop non-destructive methods for estimating the grouting quality of grouted anchor bolts. The spectrum methods include fast Fourier transform, time–frequency analysis, wavelet transform analysis, and empirical mode decomposition. In this study, we introduce the parameter-optimized variational mode decomposition (VMD) method for the spectrum analysis of impact echo signals of anchor bolts. This method overcomes the difficulty of conventional spectrum methods that cannot separate highly coupled natural modes for advanced analysis. The parameter-optimized VMD method enables the generation of a new evaluation index for quantifying bolt grouting conditions, which has the potential to significantly enhance the quality evaluation of anchor bolts compared with conventional analysis of natural frequencies. This study uses impact response to establish a new benchmark for the integrity diagnosis of anchor bolts, paving the way for more accurate and reliable safety assessments.

Impact-Echo Testing Methods for Determining the Defect of Cured-In-Place Pipe

Non-destructive testing of cured-in-place pipe (CIPP) ensures that the liner (CIPP) has been successfully installed and that the pipe will function reliably, the Acoustic methods are used to detect cracks or leaks in pipes as the Impact-Echo (IE) method. This study aims to design a device to carry the tools inside the pipe. The received signals were analyzed to identify delamination, cavities behind CIPP, and overall pipe health.

Structural damage identification and classification from time-frequency impact echo data using deep learning

ABSTRACT Deep learning advancements have enabled non-destructive crack detection, yet it remains inadequate to quantitatively identify and evaluate the structural damage states leveraging deep learning with impact echo (IE). Therefore, the present study proposes a method to achieve this target on concrete structures, utilising Convolutional Neural Networks (CNNs) and IE. During the experiments, a reinforced concrete beam was loaded to simulate various damage levels. The IE test was conducted on the pure bending zone of the beam, and the obtained data were transformed into two-dimensional (2D) time-frequency data for a six-state damage dataset. Subsequently, several CNNs were used for training, testing, and analysing their performance. The findings revealed the networks’ proficiency in distinguishing various degrees of damage. Among them, GoogLeNet emerged as the most accurate classifier. Further analysis indicated that GoogLeNet, trained with datasets from at least two monitoring units, significantly outperformed those trained using datasets from a single unit, achieving a remarkable F1 score of no less than 0.778. Additionally, the study compared 1D and 2D GoogLeNet models trained on different data formats. The results showed that the model trained on 2D time-frequency data can achieve a higher accuracy of 0.984, surpassing the 1D model trained on time-series data.

Analysis of the effect of concrete repair and self-healing due to corrosion using the impact-echo method

Reinforced concrete is a primary component in the construction industry and is susceptible to damage from corrosion, fires, and natural disasters. Steel corrosion in concrete has become a serious global issue, resulting in significant economic losses. Dealing with existing concrete damage requires considerable time and cost, making innovation in repair methods essential. One of the repair methods employed is grouting with cement grout and jacketing. Innovative solutions have emerged in self-healing concrete, allowing concrete to repair small cracks autonomously. One self-healing approach involves using bacteria to seal cracks in concrete. Identifying damage caused by corrosion in reinforced concrete is crucial for implementing appropriate protective measures. Non-Destructive Testing (NDT) methods offer a way to detect damage without compromising the physical structure. NDT methods have been used for evaluating and monitoring steel corrosion in reinforced concrete, including the Impact-Echo (IE) method. The most important result of this study was that the peak frequency values obtained from Impact Echo (IE) testing on concrete before and after corrosion showed a shift towards lower values, indicating a decrease in concrete quality due to corrosion. It was observed that after the repair process, there was an increase in peak frequency values detected with Impact Echo on each specimen, indicating the effectiveness of the repair method. These results are significant as they provide insight into the impact of corrosion on concrete quality, the effectiveness of repair methods, the correlation between concrete quality and structural strength, and the potential for non-destructive testing impact-echo methods to monitor concrete conditions.

Robotic destructive and nondestructive testing of concrete structures

Structural elements may develop defects due to severe environmental conditions. Nondestructive testing is a common method for structural evaluations; however, these structures may be inaccessible or located in unsafe areas. In this paper, a Sawyer robot was employed to carry nondestructive testing (NDT) sensors such as Ground Penetration Radar (GPR) and Impact Echo (IE). The experiment comprised twelve reinforced concrete beams divided into four groups: Control (without defects), Void, Corrosion, and Debonding. The beams underwent three-point bending tests, with loads distributed across three levels: 50 %, 75 %, and 100 % of the failure load. Robotic and handheld IE and GPR tests were conducted, with IE performed before the actual test and GPR conducted before, during, and after the tests. Load-displacement diagrams were subsequently generated. The comparison between robotic and handheld tests demonstrated the high accuracy of the robotic NDT and indicated that the defects led to reduced load capacity and increased crack propagation.

Automatic elimination of invalid impact-echo signals for detecting delamination in concrete bridge decks based on deep learning

The impact-echo (IE) method is effective for evaluating invisible defects. However, it might return misleading results when its signals are invalid. This challenge aggravates when the tests are conducted using robotic devices that automatically collect massive data. This study proposes an automatic method to eliminate invalid signals based on the ResNet model. First, the signals are visualized into two-dimensional images as the input for ResNet. The input data can then be classified into valid and invalid data via the ResNet model, which is trained with 11,290 signals and tested with 5664 signals. Finally, defects can be detected using the dominant frequencies of the valid-class data. A case study with IE data from two concrete bridges was employed to validate the feasibility of the proposed approach. The results indicate that the method can achieve an average accuracy of 90.6% for eliminating invalid signals and significantly improve the IE test accuracy.

Evaluation of Corroded OPS Fibre-Concrete using NDT Method

Concrete is one part of an infrastructure that is very commonly used, materials widely used in concrete, such as sand and gravel, come from nature that are limited and will run out if used continuously. Oil palm shell (OPS) waste is an alternative that canbe used to solve this problem. In this study, the proportion of OPS used was 0%, 25%, 50%, and 75% as a partial substitute for coarse aggregate. Pre- and post-corrosion specimens use beam sizes with dimensions of 100 mm × 100 mm × 500 mm. The specimen has a corrosion rate of 7%. The specimen is tested for flexural strength, in addition, the specimen is tested using the Non-Destructive Testing (NDT)method at the age of 28 days and after acceleration corrosion. The NDT methods used in this study were resistivity and impact-echo as evaluation tools for the influence of OPS and fiber on corroded concrete. Based on the results, the lowest value of resistivity was 10.87 kohm/cm on 0% OPS post-corrosion specimen, and the highest value of resistivity of 24.12 kohm/cm on 0% OPS pre-corrosion specimen. Meanwhile, the impact-echo test obtained the lowest value of 2625.33 kHz on 75% OPS post-corrosion specimens and the highest impact-echo value of 11725.26 kHz on 0% OPS post-corrosion specimens. With the increase in the percentage of OPS, the resistivity obtained in pre-corrosion concrete will decrease as well as the impact echo value, except for the 75% OPS specimen, while in post-corrosion specimen impact-echo and resistivity are inversely proportional. The greater the percentage of OPS in concrete, the resistivity value tends to increase but the frequency of impact-echo tends to decrease except in specimens with 75% OPS.

Explaining impact echo geometry effects using modal analysis theory and numerical simulations

Geometry effects can strongly influence the resonance frequency and defect detectability for impact echo testing when lateral dimensions are small compared to the thickness of the testing object. Since there is a lack of understanding of these effects, it is the aim of this study to review the impact echo testing method using modal analysis. The comparison between measurements on reference specimens and numerical simulations reveals that the eigenmodes of the structure cause these geometry effects. Especially those eigenmodes show large amplitudes in impact echo data, in which the motion amplitude is not modulated in thickness direction. Upon further investigation of the eigenfrequencies with 3D numerical simulations, two different inversion schemes are developed to obtain the Poisson's ratio from impact echo measurements. The results are in good agreement with experimental results from the used reference specimens.

Nonlinear Impact-Echo Test for Quantitative Evaluation of ASR Damage in Concrete

Nonlinear Impact-Echo Test for Quantitative Evaluation of ASR Damage in Concrete

Automated honeycomb detection during Impact Echo inspections in concrete using AI trained by simulation data

The Impact Echo method is well established in the civil engineering world of NDT for defect detection and thickness estimation in thick and highly reinforced concrete structures. For most applications of Impact Echo however, only the resonance frequency of the measured time signal is evaluated, meaning that most information is neglected. Here, artificial intelligence (AI) in the form of machine learning can help to classify signals based on multiple input parameters and therefore make use of the additional information stored in the measured signals. As the most powerful classification models need labelled input data, this usually marks a problem since labelled NDT data sets are rarely available for concrete structures. One solution to overcome this problem is the use of numerical simulations. In the past, numerical simulations showed that they are capable to produce realistic synthetic data for Impact Echo testing in concrete specimens. In this study, numerical simulations of Impact Echo measurements were conducted using the Elastodynamic Finite Integration technique (EFIT) to create training data for machine learning models. The measurements were carried out on two concrete specimens (17 cm and 50 cm thickness) containing honeycombs. Using the simulation data, multi-layer perceptron (MLPNN) and convolutional neural networks (CNN) are trained and tested on measured data from each specimen for performance. Results showed that an accurate honeycomb detection using machine learning was only possible in some cases with many false alarms arising near the specimen edges.

Improved stack imaging of spectral amplitude based on the impact echo method for identification of defects in grout-filled lap connection

In this paper, we designed defects considering different sizes, burial depths and arrangement modes in the grout-filled lap connection specimens to carry out impact echo test. Different signal processing methods, including Fast Fourier Transform (FFT), Wavelet Transform (WT), and improved Stack Imaging of spectral amplitude based on the Impact Echo (SIBIE), were used to analyze the data. The results show that the improved SIBIE method can efficiently increase the accuracy of multi-media interface defect imaging by filtering the original echo signal and smoothing the spectrum. The superposition of multiple sets of data can eliminate the adverse effects of abnormal samples.

Evaluating Pre-Corrosion and Post-Corrosion of Oil Palm Shell Concrete with Non-Destructive Testing

Corrosion of reinforcement can decrease the quality and cause damage to reinforced concrete, so it is necessary to know the resistance of concrete, especially with oil palm shells (OPS) and mask fibers in a corrosive environment. This study aims to determine the effect of corrosion levels on OPS concrete and mask fiber using the NDT (non-destructive testing) method. Oil palm shells are a 10% replacement for coarse aggregate in the concrete mix. The mask fiber is 0.2% of the volume of the specimen, and the superplasticizer is 0.25% of the cement used as an additive in the concrete mixture. The specimen is 50 cm long, 10 cm wide, and 10 cm high. There are two types of specimens, namely pre-corrosion and post-corrosion. In pre-corrosion specimens, corrosion acceleration of the reinforcement is carried out before the concrete molding process. While the post-corrosion specimen is being prepared, corrosion acceleration is carried out after the concrete is 28 days old. Corrosion acceleration is carried out by immersing the concrete specimen in a 5% NaCl solution and using a DC power supply. After the concrete is corroded, NDT is carried out. The NDT methods used are resistivity and impact-echo as analysis and detection tools for the effect of corrosion on palm shell concrete and mask fiber. The pre-corrosion specimen got the highest resistivity value on the 0% specimen at 21.35 kΩ.cm and the lowest resistivity on the 5% specimen at 16.70 kΩ.cm. The resistivity value decreases with increasing corrosion levels. The post-corrosion concrete has the highest resistivity on the 0% specimen, with 18.56 kΩ.cm, and the lowest resistivity on the 5% specimen, with 13.88 kΩ.cm. The resistivity value decreases with increased corrosion levels. The impact-echo testing on the pre-corrosion specimen yielded a 0% specimen with a value of 14394.53 Hz and a 1% specimen of 18266.6 Hz. The frequency value decreases with increasing corrosion levels. The result of impact-echo testing on post-corrosion concrete was 14394.53 Hz for the 0% specimen and 1567.38 Hz for the 5% specimen. The frequency value decreases with increasing corrosion levels.

Ultrasonic and Impact-Echo Testing for the Detection of Scaling in Geothermal Pipelines

The efficiency and longevity of components in geothermal plants are significantly reduced by mineral deposition in pipes, also known as scaling [1]. A number of methods are used to monitor fouling (a broader term for all types of organic and inorganic deposition) in other industries, such as the oil, gas and food industries. These include direct and indirect, offline, in-situ and online, as well as inline methods, each of which have pros and cons [2, 3]. An effective monitoring solution specific to the high temperature conditions in geothermal plants, which optimises maintenance and cleaning measures in dependency of scaling, has yet to be developed. In this paper, two non-destructive testing techniques: contact ultrasonic testing (UT) and impact-echo (IE) testing are investigated in their viability as methods to detect scaling growth in geothermal plants. A descaling measurement was conducted on a heavily scaled segment of an obsolete pipeline from the production well of the geothermal power plant in Sauerlach, Germany. The pipeline segment was inserted in a test rig and the scaling was gradually etched away using an acidic solution. At regular time intervals, the scaling thickness was measured mechanically and contact UT as well as IE measurements were carried out. The testing apparatus was designed to withstand high temperatures ({140},^{circ }{text {C}} at the inlet pipe surface) and to be easy to install whilst being cost-efficient. Both techniques yielded usable results with submillimeter resolution. The advantages and limitations of the two methods are discussed. Impact-echo testing, in particular, can be automated and presents a simple and cost-efficient scaling monitoring option.

Evaluation of condition on replacing repaired concrete based on NDT and the Mahalanobis–Taguchi system

In Japan, much infrastructure has been constructed over the past 50 years, and maintenance of such infrastructure is important. Repairing and retrofitting of deteriorated concrete members has become a common place. Meanwhile, re-deterioration of repaired concrete members has become of concern. To prevent re-deterioration of the repaired concrete members, it is important to evaluate the quality and condition of repair, in addition to the use of NDE. Therefore, to evaluate the condition of replacing repaired concrete specimens, an ultrasonic test, impact-echo test, and impact test for measuring mechanical impedance were applied. In addition, data of these NDT are integrated by the Mahalanobis–Taguchi system (MT system). The MT system is an information processing system for pattern recognition advocated by quality engineering. From the results, it is clarified that the Mahalanobis distance calculated by the MT method could successfully evaluate the conditions of replacing repaired specimens.

Validation of wireless impact echo prototype for condition assessment of concrete structures

A new wireless impact echo prototype was developed for the condition assessment of reinforced concrete slabs. The new system includes a wireless handheld sensing device which connects by Bluetooth to a tablet equipped with a user-friendly software for managing the testing grid, recording measurements, processing and transforming the data to a frequency domain, and presenting the results in real time. To validate the device, three large-scale concrete slabs of dimensions 2000 mm x 2000 mm x 300 mm were fabricated with artificial defects including voids, delamination, vertical cracks, and geometric discontinuities. The influence of slab boundaries, defect depth and location, overall thickness, steel reinforcement, and an internal conduit on the obtained measurements were investigated. The results were also compared with other commercial non-destructive testing devices including ground penetrating radar (GPR) and ultrasonic pulse velocity (UPE), and the advantages and disadvantages of each method are discussed. Overall, the results suggest that the impact echo technique using the new wireless prototype is able to accurately detect and characterise various defect types provided that the right size of impactor is used. Use of improper impactor sizes can result in alternative vibration modes depending on the slab thickness and defect depth; in that case, multiple readings may be required. The new system greatly improved the ease-of-use of the impact echo technique, although the inspector should be trained to properly interpret the results. Compared with the GPR technique, the impact echo system was not as rapid and unable to detect the location of steel reinforcement; on the other hand, it was better able to detect defects in the presence of steel bars which tended to obscure readings using GPR. UPE measurements were also faster than the impact echo tests and performed well for slab thickness measurements and detection of certain defect types; however, readings were found to be affected by the presence of steel reinforcement and in some cases were inconsistent. Overall, it may be concluded that each technique presents certain strengths and weaknesses that should be considered in the condition assessment strategy. The results obtained from each technique are complementary and may be used to develop a complete understanding of the internal state of the structure.

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.

A Study on the Applicability of the Impact-Echo Test Using Semi-Supervised Learning Based on Dynamic Preconditions.

The Impact-Echo (IE) test is an effective method for determining the presence, depth, and area of cracks in concrete as well as the dimensions of the sound concrete without defects. In addition, shallow delamination can be measured by confirming a flexural mode in the low-frequency region. Owing to the advancement of non-contact sensors and automated measurement equipment, the IE test can be measured at multiple points in a short period. To analyze and distinguish a large volume of data, applying supervised learning (SL) associated with various contemporary algorithms is necessary. However, SL has limitations due to the difficulty in accurate labeling for increased volumes of test data, and reflection of new specimen characteristics, and it is necessary to apply semi-supervised learning (SSL) to overcome them. This study analyzes the accuracy and evaluates the applicability of a model trained with SSL rather than SL using the data from the air-coupled IE test based on dynamic preconditions. For the detection of delamination defects, the dynamic behavior-based flexural mode was identified, and 21 features were extracted in the time and frequency domains. Three principal components (PCs) such as the real moment, real RMS, and imaginary moment were derived through principal component analysis (PCA). PCs were identical in slab, pavement, and deck. In the case of SSL considering a dynamic behavior, the accuracy increased by 7–8% compared with SL, and it could categorize good, fair, and poor status to a higher level for actual structures. The applicability of SSL to the IE test was confirmed, and because the crack progress varies under field conditions, other parameters must be considered in the future to reflect this.

Investigation of Ground-Penetrating Radar, Impact Echo, and Infrared Thermography Methods to Detect Defects in Concrete Bridge Decks

A reinforced concrete bridge deck is often at risk of many forms of deterioration which affect its service life and maintenance cost. Nondestructive test (NDT) methods have seen increasing use by departments of transportation to locate deterioration in bridge decks before their condition rating becomes critically low and to help plan bridge deck preservation activities. Nonetheless, uncertainty in what forms of bridge deck deterioration each NDT method can identify has posed challenges in best deploying NDT methods for deck condition assessments. Thus, in this study, a full-scale 18 ft by 31 ft reinforced concrete bridge was constructed with defects in the deck simulating reinforcing steel corrosion, delaminations, concrete deterioration, voids, and poorly constructed concrete. The deck was evaluated with ground-penetrating radar, infrared thermography, and impact-echo nondestructive technologies to evaluate their potential for defect detection. Receiver operator characteristic analysis was implemented to quantify the capability of a given NDT method to detect a particular defect. Conclusions concerning which forms of concrete bridge deck deterioration each NDT method can detect were developed. It was found that of the three NDT methods considered, impact-echo testing was the most effective to evaluate the condition of bridge decks. Impact echo was able to detect both shallow and deep delaminations as thin as 0.01 in., shallow corrosion-induced delaminations, concrete deterioration, and some of the shallow voids; however, it was unable to detect deep voids, poorly constructed concrete, and mildly deteriorated concrete.

Gaussian process-assisted active learning for autonomous data acquisition of impact echo

The goal of this study is to pursue sampling efficiency and achieve reasonable accuracy for the quantity of interest (damage) in impact-echo testing. Because the locations of damages are not always obvious, the conventional grid-based measurement requires subjective judgment. The proposed solution is the implementation of Gaussian process (GP)-assisted active learning to enable the sequential sampling of the quantity of interest with autonomous decision-making for data acquisition. Throughout sampling, an automated model selection is employed to select a suitable kernel for GP modeling dynamically. Active learning is then applied to identify potential damage locations without any human assistance. The proposed method iterates the two modules to explore unknown damage and improve the accuracy of the quantity of interest until the maximum sample size is reached. Through numerical and experimental validation, this work demonstrates that the proposed method exhibits more accurate and efficient results.

Rapid damage assessment of concrete bridge deck leveraging an automated double-sided bounce system

We demonstrate the automated crack evaluation system for the rapid characterization of internal/external concrete structures. Practical generation and detection of reliable and consistent mechanical waves are made possible using an automatic impactor and air-coupled sensors, offering the potential to overcome limitations associated with the rapid damage assessment towards traffic disruption-free measurement. The objective is to demonstrate enhanced performance in laboratory and field tests when arrays of double-sided bounce impactors and microelectromechanical systems are used, suitable for automation without lane closures. The inspection includes detecting delamination, vertical cracks, and corroded reinforcement using flexural vibration mode of impact-echo testing, wave attenuation, and radar, respectively. The approach has novelty in three main techniques: the advanced auto-impacting system, multichannel acoustic scanning unit, and integrated scanning platform. The laboratory tests are performed to verify the system's performance under different conditions and parameters to these damages. The proposed system is applied in two highway bridge inspections.