Impact-echo Signals Research Articles

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.

Experimental Study on the Damage of Bridge Concrete in a Fire Environment Based on the Impact Echo Method

Abstract To accurately assess the damage degree of bridge structures in a fire environment, fire damage test studies were conducted on the C30 and C50 concrete specimens at different temperatures (room temperature, 100°C∼700°C) based on the impact echo method. In addition, the variation rules of mass, impact echo signal, dynamic modulus of elasticity Ed, and static and dynamic modulus of elasticity ratios (Ec/Ed) with overfire temperature were obtained. The results show the following: (1) at ablation temperature ≤ 300°C, the main frequency of the impact echo signal is single, the peak value is obvious, the signal attenuation is fast, and the change is uniform; at ablation temperature > 300°C, the main frequency is frequent, there is more than one peak, and the vibration duration of the phenomenon is longer. (2) After fire damage, the specimen’s dynamic modulus of elasticity Ed is in a cubic relationship with the overfire temperature. (3) In engineering practice, the static and dynamic modulus of elasticity (Ec/Ed) ratio is classically based on Lydon and Balendran’s formula, which is usually used for derivation at room temperature. This article found that the formula can be extended to the rough estimation at lower temperatures (≤200°C), and the model proposed in this article can be used for the derivation of the static modulus of elasticity Ec and the dynamic modulus of elasticity Ed at high temperatures. In summary, the impact echo method can be used to quantitatively assess the damage state of bridges after fire. After the signal time-frequency domain conversion, a qualitative assessment of the degree of concrete damage can be made. Furthermore, a quantitative assessment of bridge concrete after fire damage can be made using the model proposed in this article.

Geometry effects in Impact Echo Signals - why traditional time-frequency filtering fails to remove them

Impact Echo signals can sometimes contain a lot of noise, even though the wavelength of the investigated modes is in the order of the specimen thickness. A special type of noise signals are the so-called geometry effects. These effects occur when the lateral dimensions of the investigated structure are not much larger than its thickness. This study compares signals with and without geometry effects using various types of signal processing methods such as classical fast Fourier transform, short-time Fourier transform, and the continuous wavelet transform. Results show that signals without geometry effects have a much more stable resonance frequency, whereas the main frequency content in Impact Echo signals containing geometry effects typically oscillates between two or more discrete frequencies. As this oscillation lasts for the entirety of the signals, traditional time-frequency filters will likely not be capable to remove geometry effects from Impact Echo signals. Resonance frequencies in Impact Echo signals unaffected by geometry effects are much more stable, with no fluctuation or oscillation over time. By comparing time-frequency representations of signals obtained at the same spot with different impactors, it is observed that the peak frequency at the beginning of each signal is similar for all three measurements. This result suggests that, for signals heavily affected by geometry effects, the beginning of the signal, which is usually neglected during Impact Echo inspection, could provide useful information regarding the thickness of the inspected structure. Two ways of obtaining thickness information from the signal beginning are presented and results are compared using a dataset from a reference specimen: one method uses classical Fourier transform of the signal beginning and the other approach uses the maximum frequency determined from the continuous wavelet transform. The results for both approaches show that the Impact Echo signal beginning contains depth information, which could prove valuable for evaluations of noisy datasets.

Non-Destructive Characterization of Cured-in-Place Pipe Defects.

Sewage and water networks are crucial infrastructures of modern urban society. The uninterrupted functionality of these networks is paramount, necessitating regular maintenance and rehabilitation. In densely populated urban areas, trenchless methods, particularly those employing cured-in-place pipe technology, have emerged as the most cost-efficient approach for network rehabilitation. Common diagnostic methods for assessing pipe conditions, whether original or retrofitted with-cured-in-place pipes, typically include camera examination or laser scans, and are limited in material characterization. This study introduces three innovative methods for characterizing critical aspects of pipe conditions. The impact-echo method, ground-penetrating radar, and impedance spectroscopy address the challenges posed by polymer liners and offer enhanced accuracy in defect detection. These methods enable the characterization of delamination, identification of caverns behind cured-in-place pipes, and evaluation of overall pipe health. A machine learning algorithm using deep learning on images acquired from impact-echo signals using continuous wavelet transformation is presented to characterize defects. The aim is to compare traditional machine learning and deep learning methods to characterize selected pipe defects. The measurement conducted with ground-penetrating radar is depicted, employing a heuristic algorithm to estimate caverns behind the tested polymer composites. This study also presents results obtained through impedance spectroscopy, employed to characterize the delamination of polymer liners caused by uneven curing. A comparative analysis of these methods is conducted, assessing the accuracy by comparing the known positions of defects with their predicted characteristics based on laboratory measurements.

Visualization of concrete internal defects based on unsupervised domain adaptation algorithm for transfer learning of experiment-simulation hybrid dataset of impact echo signals

Detecting concrete internal defects through deep learning analysis of impact echo signals faces two challenges: (1) the traditional signal processing method such as wavelet transform (WT) fails to reflect data-sensitive damage characteristics due to the uncertainty principle and (2) the limited labeled data acquired from real structures impedes network training. To address the first challenge, this paper proposes the WT-based synchrosqueezing transform (WT-SST) for the conversion of time-series data to the time-frequency spectrogram, which can provide effective features for the network in time and frequency domains simultaneously. To overcome the second challenge, numerical simulation data are supplemented for the augment of labeled data. To minimize the effect of data variance between experiments and simulations, this paper uses an unsupervised domain adaptation (DA) network for the transfer training of labeled simulation data (original domain) and unlabeled experimental data (target domain). The DA network extracts domain-invariant features by maximizing the domain recognition error and minimizing the probability distribution distance. The damage probability is calculated by the trained model to produce a 2D defect contour image of concrete specimens, and the three-dimensional visualization of internal defects by estimating the defect depth based on the defect area of contour image. Finally, the recognition precision, recall, F1-score, and accuracy of the model of unsupervised DA network trained by a hybrid dataset reaches 89.4%, 88.4%, 88.9%, and 94.7%, respectively.

Validating a Physics-Based Automatic Classification Scheme for Impact Echo Signals on Data Using a Concrete Slab with Known Defects

Impact echo (IE) is capable of locating subsurface defects in concrete slabs from the vibrational response of the slab to a mechanical impact. For an intact slab (“good” condition), the frequency spectrum of the IE is dominated by a single peak corresponding to the slab’s “thickness resonance frequency,” whereas the presence of subsurface defects (“fair” or “poor” conditions) could manifest in various ways such as multiple distinct peaks at frequencies higher, or lower, than the thickness resonance. In previous research, the authors have proposed a frequency partitioning of the spectrum for IE signal classification. Firstly, the thickness resonance frequency band is identified using a data-driven approach and then the IE signals are represented by their energy distribution in three bands—frequencies less than, within, and greater than the thickness resonance. Following this feature extraction, an unsupervised clustering approach is used to identify the centroids for each signal class—good, fair, and poor—which are further used to classify any test signal into one of the three aforementioned classes. The classification is developed by training on unlabeled IE signals from real bridge deck data (the Federal Highway Administration’s [FHWA’s] InfoBridge dataset) without making use of any labeled data. This study aims to validate the proposed methodology on a labeled dataset of eight reinforced concrete specimens constructed at the FHWA Advanced Sensing Technology Nondestructive Evaluation laboratory having known artificial defects. Our findings indicate that the physics-based feature definition and the method developed on real bridge data are robust and can classify IE signals in the labeled data with moderate accuracy.

Experimental and numerical study on detection of sleeve grouting defect with impact-echo method

Grouted sleeve connections are widely used for vertical connections in precast concrete shear walls. Grouting defect is unavoidable in construction and may affect the performance of the joint in service. A two-step research was carried out to investigate the effectiveness of impact-echo method in the detection of grouting defects in sleeves. First, a mechanistic analysis of the differences in the propagation paths of impact stress waves in grout-compact sleeve concrete assemblies and grout-deficient sleeve concrete assemblies was carried out. Second, finite element models of the assemblies with different sleeve arrangements were established, and the FFT decomposition of the impact-echo signals was carried out with the impact point and reception point spacing, impact hammer diameter, sleeves position and thickness of the assemblies as variable groups. At the same time, the difference in thickness frequencies of grout-compact and grout-deficient assemblies was discussed. Results indicate that the presence of grouting defects can be effectively detected by impact hammers of different diameters for centrally arranged single sleeves, making qualitative analysis of grouting defects possible. For side-sleeve and double-sleeve specimens, there is uncertainty in the detection of grouting defects and therefore the impact-echo method needs further study. Furthermore, an amplitude discrimination method for defect location in double-sleeve specimens is proposed. The findings of this study can provide a reference for quality inspection of grouting sleeve connection.

Classification of Impact Echo Signals Using Explainable Deep Learning and Transfer Learning Approaches

Impact echo (IE) is one of the most frequently used nondestructive evaluation (NDE) techniques for detecting subsurface defects such as delamination, honeycombing, and voids in concrete structures. In the conventional analysis of IE data, the time-domain signal is transformed into the frequency domain and the frequency content is used to estimate the presence and nature of the defect. Machine learning (ML) has been recently applied to the IE signal classification problem. However, because of the scarcity of labeled IE datasets, most existing work relies on relatively small training and test datasets without addressing the generalizability and transferability of the developed models. In this paper, we compare two approaches for automatic classification of IE signals: clustering based on expert-crafted features and deep learning (DL) from automatically extracted features. Next, we use the knowledge gained from a DL model trained on concrete specimens with available ground truth to make predictions about defects in a different specimen with completely different construction and characteristics (transfer learning). Finally, we examine our DL model to gain insights into the model working (explainability) and highlight the attributions that are significant in classifying a particular IE signal. Our findings demonstrate the utility of ML and DL for IE signal classification, but also highlight the need for high-quality labeled datasets for advancing ML and DL in NDE data analysis.

Comparison between Supervised and Unsupervised Learning for Autonomous Delamination Detection Using Impact Echo

Impact echo (IE) is a non-destructive evaluation method commonly used to detect subsurface delamination in reinforced concrete bridge decks. Existing analysis methods are based on frequency domain which can lead to inaccurate assessments of reinforced concrete bridge decks since they do not consider features of the IE signals in the time domain. The authors propose a new method for IE classification by combining features in the time and the frequency domains. The features used in this study included normalized peak values, energy, power, time of peaks, and signal lengths that were extracted from IE signals after they are preprocessed. We used a dataset containing IE data collected from four in-service bridges, annotated using chain dragging. A support vector machine (SVM) classifier was constructed using combined features to classify IE signals. A 1DCNN with unfiltered IE signals and a two-dimensional CNN using wavelet scalograms (2D representations of unfiltered IE signals) were also used to classify IE signals. The SVM model performed significantly better than the other models, with an accuracy rate, true positive rate, and true negative rate of 97%, 92%, and 98%, respectively. The SVM model also generated more accurate defect maps for all investigated bridges. IE data from the Federal Highway Administration’s InfoBridge website were used to investigate the efficacy of the developed models. The investigation yielded promising results for the proposed SVM model when used for a new set of IE data.

A Hybrid Hidden Markov Model and Time-Frequency Approach to Impact Echo Signal Classification

A Hybrid Hidden Markov Model and Time-Frequency Approach to Impact Echo Signal Classification

Validating a physics-based automatic classification scheme for Impact Echo signals on data from a concrete slab with known defects

Impact Echo (IE) is capable of locating subsurface defects in concrete slabs from vibrational response of the slab to mechanical impact. For an intact slab (‘good’ condition), the frequency spectrum of IE is dominated by a peak corresponding to the slab’s ‘thickness resonance frequency’ since much of the impact energy is reflected from the bottom of the deck. The presence of subsurface defects (‘fair’ or ‘poor’ conditions) could manifest in various ways such as multiple distinct peaks at frequencies higher or lower than the thickness resonance. Energy distribution across different frequency bands varies with the type and extent of damage. In previous research, the authors have proposed a frequency partitioning of the spectrum for IE signal classification. First, the thickness resonance frequency band is identified using Gaussian Mixture modeling. Next, IE signals are represented by their energy distribution in three bands – frequencies less than, within and greater than thickness resonance. IE signals are reduced to feature vectors representing the proportion of energy in each band. Following the feature extraction, an unsupervised clustering approach is used to identify the centroids for each signal class – good, fair, poor, which is used to classify any test signal into one of the three classes. The classification is developed by training on unlabeled IE signals from real bridge deck data (Federal Highway Administration’s (FHWA) InfoBridge dataset) without making use of any labeled data. This study aims to validate the proposed methodology developed based on field data on a labeled dataset of eight reinforced concrete specimens constructed at FHWA Advanced Sensing Technology NDE laboratory with known artificial defects: shallow and deep delamination, honeycombing and voids. Findings indicate that the physics-based feature definition and the method developed on real bridge data is robust and can classify IE signals in the labeled data with high accuracy.

Transfer learning of Impact Echo signal classification from laboratory to the field

Impact Echo (IE) is a popular nondestructive testing tool for condition assessment of concrete bridge decks. Traditionally, IE signals require to be manually interpreted and classified into discrete classes – good, fair and poor - representing the different conditions at test points. This study aims to develop an automated fuzzy classification approach for IE signals trained on a set of eight reinforced concrete specimens constructed at the FHWA Advanced Sensing Technology (FAST) NDE laboratory with artificial defects that are known apriori. We choose fuzzy classification to obtain condition “goodness” represented on a continuum rather than on discrete classes (good, fair and poor). We use fuzzy classification models informed by physics-based features and automatically extracted features to determine the departure of a bridge deck condition from ‘good’. Next, using transfer learning, we suitably tune the classification model for optimum performance on data from FHWA’s InfoBridge database that has a limited amount of IE data from real bridge decks. We evaluate the transferred model performance against expert-assisted manual signal classification. This study will demonstrate the utility of laboratory studies for developing models applicable to real field data, where little or no ground truth data are available.

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.

Empirical mode decomposition based techniques for imaging of shallow delamination in concrete using impact echo

An impact echo (IE) signal comprises different frequency components as a result of interaction between stress waves and targets. Empirical mode decomposition (and its improved techniques), an adaptive and fully data-driven technique have been used to separate different components of an IE signal into what is known as intrinsic mode functions (IMF). The applications of improved versions of EMD and their advantages over other new adaptive decomposition techniques have also been demonstrated. This separation of modes differentiates constituent signals such as surface wave, echo wave, and flexural wave into corresponding IMFs and thus allows reconstruction of a new signal using only desirable components. An image reconstruction strategy for the selection of appropriate IMFs based on correlation coefficient is found to produce an improved image of the delamination or flaws compared to the raw signal images. The IE signal processed with noise-assisted EMD based techniques profoundly increased the accuracy of flaw features over the raw data. This improvement is validated with a quantification approach using amplitude histogram and binary mapping of flaws in B-scan and C-scan respectively. The study demonstrated that the flexural mode of vibration produced due to shallow delamination can be used for accurate imaging of flaws provided a suitable signal processing technique is used. The technique employed in this study can also be extended for imaging of deep delamination which is exhibited by echo waves in IE signals.

Recognition method of internal concrete structure defects based on 1D-CNN

Conventional intelligent recognition methods highly depend on artificial feature extraction and expert knowledge to recognize concrete structures’ internal defects. For solving this problem, one intelligent recognition method for internal concrete structure defects is proposed, based on a one-dimensional, convolutional neural network (1D-CNN). First, the impact echo detection signals are acquired, to establish the training and testing of samples for various internal concrete structure defects. Then, the convolutional network structure is used to achieve the adaptive, hierarchical extraction of the impact echo signals’ features. Finally, the Softmax classifier is used to provide the diagnosis result at the output end. The experimental results of four types of internal defects (including voids, water-filled, imperfect solids, and sound) show that the 1D-CNN classifier, with the predicted signals as the training set, enables the successful identification of the internal defects of the concrete structure and achieves more than 90% defect-recognition accuracy. In addition, the 1D-CNN classifier has strong anti-interference ability and feasibility in practical applications. This work improves the performance of ‘impact echo’ in identifying internal defects in concrete and realizes the intelligent analysis of impact echo signals.

Utilization of Unsupervised Machine Learning for Detection of Duct Voids inside PSC Box Girder Bridges

The PSC box girder bridge is a pre-stressed box girder bridge that accounts for a considerable part of large-scale bridges. However, when concrete is poured, even small mistakes might result in voids that appear during long-term maintenance. In this paper, we present a technique for detecting the void in the duct inside the PSC box girder bridge. Data are acquired utilizing the non-destructive impact-echo (IE) approach to detect these voids. IE creates time-series data as signal data initially; however, we want to use a CNN auto-encoder (AE). A scalogram, which is a kind of wavelet transformation, is used to convert time series data into an image. An AE is a type of unsupervised learning that aims to minimize the difference between the input and output. By comparing histograms, the difference is calculated. To begin, we create scalogram images from all IE signal data, which were randomly sampled as 98% normal and 2% void. The CNN AE is then trained and evaluated utilizing all the data. Finally, we examine the input and output histogram similarity distributions. As a consequence, only 4% of the normal data had a similarity of less than two standard deviations from the mean, whereas 34.7% of the void data did. As a result, the existence of voids inside the PSC duct could be demonstrated to be predictive in the absence of annotated data.

Evaluation of a Concrete Slab Track with Debonding at the Interface between Track Concrete Layer and Hydraulically Stabilized Base Course Using Multi-Channel Impact-Echo Testing.

Multi-channel Impact-echo (IE) testing was used to evaluate debonding defects at the interface between track concrete layer, TCL, and hydraulically stabilized base course, HSB, in a real scale mockup model of concrete slab tracks for Korea high-speed railway (KHSR) system. The mockup model includes three debonding defects that were fabricated by inserting three 400 mm by 400 mm (length and width) thin plastic foam boards with three different thicknesses of 5 mm, 10 mm, and 15 mm, before casting concrete in TCL. Multi-channel IE signals obtained over solid concrete and debonding defects were reduced to three critical IE testing parameters (the velocity of concrete, peak frequency, and Q factor). Bilinear classification models were used to evaluate the individual and a combination of the characteristic parameters. It was demonstrated that the best evaluation performance was obtained by using average peak frequency or the combination of average peak frequency and average Q factor, obtained by eight accelerometers in the multi-channel IE device. The results and discussion in this study would improve the understanding of characteristics of multiple IE testing parameters in concrete slab tracks and provide a fundamental basis to develop an effective prediction model of non-destructive evaluation for debonding defects at the interface between TCL and HSB in concrete slab tracks.

Impact-Echo 신호와 장단기 메모리에 기반한 교량 결함 탐지

PSC 박스 거더 교량은 다양한 요소로 구성되어 있으며, 그 중 강연선은 가장 중요한 요소이다. 강연선은 부식과 같은 결함으로부터 매우 취약하다. 그래서 이를 보호하기 위해 덕트에 삽입한 후 콘크리트를 타설한다. 하지만 덕트의 내부 직경은 매우 좁기 때문에 작은 실수로도 공동이 발생할 수 있다. 따라서 비파괴 검사를 이용해 얻은 신호에 대한 전문적인 해석으로 결함의 여부를 확인하지만, 이는 많은 비용과 시간을 요구한다. 또한 콘크리트 구조물의 내부 상태를 정확하게 탐사하는 것이 어렵다. 따라서 본 연구에서는 장단기 메모리(Long Short-Term Memory)를 도입하고자 한다. 이 때, 콘크리트 구조물에서 자주 사용되는 Impact-Echo (IE) 방법을 입력 자질로 사용한다. 그리고 구조물의 특징(콘크리트의 두께, 덕트의 깊이, 타격점과 측정점과의 거리)을 학습에 함께 활용하며, IE 신호에 대한 주파수 성분도 추가로 활용한다. 실험 결과, 본 연구에서 제안한 모델은 약 88.56%의 정확도를 확인할 수 있었다.

Application of Closed-Form Solution for Normal Displacements on Impacted Half Space: Quantification Impact-Echo Signals

In order to investigate the feasibility of deducing a simulated transfer function based on the Rayleigh wave form in an Impact-Echo signal, the analytical solution for the normal sur- face displacement due to a heaviside force at the half-space was reviewed and used to compute the surface displacement responses resulted from various types of impulse forces. Based on a series of numerical studies on the characteristics of Rayleigh wave form in the surface displace- ment responses, this paper presents the idea of using an equivalent impact force to derive an in- tentionally scaled transfer function. The pseudo force can be obtained from using Rayleigh wave form as a pseudo force or by generating an equivalent half-sine impact force accordingly. The effect of using such pseudo and equivalent force functions was discussed in details. In the pro- posed method, the force amplitude was first estimated from an amplitude curve established from numerical simulations using half-sine force functions. The recovery of a simulated transfer func- tion was next achieved via the use of an estimated force amplitude and a selected force function. The proposed procedure also results in steady thickness amplitudes when measurements on two concrete plates were taken for various impacts associated with different steel balls and different impact locations. The success in recovering constant thickness amplitudes for plate-like struc- tural members proved that the derivation of simulated transfer function is a useful tool in ex- tending the Impact-Echo test. The quantitative evaluation of the interfacial property of the sub- strate layer will also benefit from this simulated transfer function.

An effective crack-identification approach for impact echo signals using MWT spectrograms and scaled FFT spectra

When performing an impact-echo (IE) test to detect internal cracks in concrete structures, the fast Fourier transform (FFT) of the measured surface responses is used to extract frequency peaks of major periodic events and obtain crack depth information. However, reflections from reinforcing bars located at approximately one-half the crack depth may result in comparable frequencies. It is then difficult to determine whether a certain peak frequency is associated with a crack or a bar based solely on the FFT frequency. Our research group suggested using the Morlet wavelet transform (MWT) to facilitate distinguishing between crack responses and bar responses. It was found that the proposed idea is feasible for identifying relatively large cracks. This paper extends the applicability of the previous study by incorporating two analytical models into the methodology: an attenuation model for describing the decrement trend of the amplitude histograms extracted from the MWT spectrograms; and a method for normalizing and scaling the frequency amplitudes. Thus, an enhanced scheme for identifying cracks and bars has been developed, in which the evaluation procedures consist of a quick diagnosis stage for large cracks and a confirmation stage for bars. Through numerical studies and experimental measurements, this study demonstrates the logic in how to validate the two-stage procedures with appropriate benchmark values. Hence, this paper provides the analysts with an effective scheme to distinguish between bar and crack echoes in IE signals.