Guidelines for Impact Echo Test Signal Interpretation Based on Wavelet Packet Transform for the Detection of Pile Defects

Defects in pile foundations, such as neck defects, bulge imperfections, weak concretes, cracks, and broken piles, can cause a decrease in the bearing capacity and the structural stability of the foundation. Identification of the type of defect is vital in formulating a reasonable repair plan for the pile foundation. In this study, the authors proposed a scheme to identify the types of defects in concrete piles based on a convolution neural network and a low-strain pile integrity test (LSPIT). A batch modeling method of defective pile foundations using Python script was also proffered. The different degrees of signals of five types of defective pile foundations were simulated by this method. The original data were decomposed and reconstructed by wavelet packet decomposition (WPT). To prevent the data from losing too much information after WPT, the data of 400 × 1 after decomposition and reconstruction were processed by dimension-raising to obtain the data of 20 × 20 × 1. Then, the multidimensional feature index of 20 × 20 × 2 was generated by index fusion with the original data. Finally, the data were input onto convolutional neural network (CNN) as a training parameter. Following an improvement of the dataset, the recognition accuracy of the type of defect in the pile foundation by the proposed identification scheme reached 94.4%.

The detection of impact and depth defects in Glass Fiber Reinforced Polymer (GFRP) composites has been extensively studied to develop effective, reliable, and cost-efficient assessment methods through various Non-Destructive Testing (NDT) techniques. Challenges in detecting these defects arise from varying responses based on the geometrical shape, thickness, and defect types. Long Pulse Thermography (LPT), utilizing an uncooled microbolometer and a low-resolution infrared (IR) camera, presents a promising solution for detecting both depth and impact defects in GFRP materials with a single setup and minimal tools at an economical cost. Despite its potential, the application of LPT has been limited due to susceptibility to noise from environmental radiation and reflections, leading to blurry images. This study focuses on optimizing LPT parameters to achieve accurate defect detection. Specifically, we investigated 11 flat-bottom hole (FBH) depth defects and impact defects ranging from 8 J to 15 J in GFRP materials. The key parameters examined include the environmental temperature, background reflection, background color reflection, and surface emissivity. Additionally, we employed image processing techniques to classify composite defects and automatically highlight defective areas. The Tanimoto Criterion (TC) was used to evaluate the accuracy of LPT both for raw images and post-processed images. The results demonstrate that through parameter optimization, the depth defects in GFRP materials were successfully detected. The TC success rate reached 0.91 for detecting FBH depth defects in raw images, which improved significantly after post-processing using Canny edge detection and Hough circle detection algorithms. This study underscores the potential of optimized LPT as a cost-effective and reliable method for detecting defects in GFRP composites.

Traditionally reservoir simulation has been accomplished by solution of reservoir flow models using variations of the methods of finite differences and less frequently by finite elements. The accuracy of these methods depends heavily on the size of grid blocks and time steps employed for the numerical solution. Consequently, computational time and memory requirements may be overwhelming in many cases. In the present study, a recently proposed method called the finite analytic method is shown to alleviate the difficulties associated with the conventional methods. The finite analytic method is operationally and principally different than the conventional finite difference and finite element methods in that a general analytical solution of the simulation model is used over mesh elements to obtain a numerical scheme. Therefore, for comparable accuracy, finite analytic method can lead to higher order approximations than those achieved by the finite difference and finite element techniques. Finite analytic schemes are derived from the reservoir flow models and therefore they are naturally problem dependent. This is the reason for the efficiency of the finite analytic method. By this method problems such as numerical diffusion, numerical instability, slow convergence, as well as requirements of large number of time steps and grid blocks are significantly reduced. In this study, the methodology of formulating and calculating with this method is illustrated by means of a typical two-phase immiscible flow problem described by the Buckley-Leverett equation. The results are compared with the analytic solution of Buckley-Leverett problem and solutions obtained by finite difference and finite element methods. The present study demonstrates that the finite analytic method is capable of producing accurate flow behavior in porous media even when relatively large time steps and grid blocks are used.

Wavelet transforms and wavelet packets are widely imposed in the analysis and resolution of problems related to science and technical engineering. Decomposition wavelet packet allows several frequency bands according to various levels of resolutions. We apply this transform (PWT) coupled with the SPIHT coder to reduce the limitations of conventional wavelet filter bank. The results obtained using the applied algorithm, are very satisfactory and encouraging compared to many of the best coders cited in the literature and show a visual and numerical superiority over conventional methods. These the promising results are confirmed by visual evaluation parameters (PSNR, MSSIM and VIF).

New stopping criteria for crack detection during fatigue tests of railway axles

As a widely used non-destructive evaluation (NDE), ultrasonic testing utilizes the echo wave to evaluate the material damage and degradation. However, there exists a lot of unwanted noise, which makes the detection of small faults difficult. In order to achieve the good detection performance of non-destructive evaluation, an improved denoising method of ultrasonic echo was proposed in present study. Firstly, the echo wave of ultrasonic testing is decomposed with the wavelet packet transform (WPT). Subsequently, the energy and entropy parameters of wavelet packets are calculated, respectively. Based on the analysis of their statistical properties, the threshold for the signal reconstruction packets is determined. Besides, by the simulation experiments on two cracks at different locations in a metal plate, the denoising performance of the present method using two popular wavelet functions of sym4 and db4 are investigated and compared with other conventional methods. All these experiment results show that the better denoise performance can be achieved with this proposed method especially when the signal noise ratio (SNR) is smaller than 10dB.

The wood-based furniture manufacturing industries prioritize quality of production to meet higher market demands. Identifying various types of edge-glued wooden panel defects are a challenge for a human worker or a camera. Several studies have shown that the detection of edge-glued defects with low, high, normal, overlong, short is identified but detection of residue and bluntness is highly challenging. Thus, the present model identifies defects of low, high, normal, overlong, short by computer vision and/or deep learning, whereas defects of residue and bluntness by deep learning based decide by pass for having better performance. The goal of this paper is to provide an improved defect detection solution for wood-based furniture manufacturing industries by process automation. Therefore, a system was designed that takes defect input images from a camera as raw image and laser-aligned image for defect detection of the edge-glued wooden panel. The process automation then performs computer vision-based image features extraction with deep learning for defect detection. The aim of this paper is to solve edge-glued defect detection problems by using design and implementation of edge-glued wooden defect detection, that can be stated as edge-glued wooden panel defect detection using deep learning (WDD-DL) for process automation by artificial intelligence and Automated Optical Inspection (AOI) consolidation. Possibly there exist several types of defects on the edges while edge-banding on the wooden panel in furniture manufacturing. Therefore, the scope is to achieve higher accuracy by raw image and laser-aligned image feature extraction using deep learning algorithms for final result defect classification in WDD-DL by AOI. The WDD-DL system uses Gabor, Harris corner, morphology, structured light detection and curvature calculation for pre-processing and InceptionResnetV2 Convolutional Neural Network algorithm to attain the best results. The applications of this work can be found in quality control of the furniture manufacturing industry for an edge, corner, joint defect detection of the wooden panels. The WDD-DL achieves best results as the precision, recall and F1 score are 0.97, 0.90 and 0.92, respectively. The experiments demonstrate higher accuracy achievement as compared to other methods with overkill and escape rate analysis. Ultimately, the discussion section provides an interesting experience sharing about the necessary factors for implementing the WDD-DL in real-time industrial operations.

Hybrid Laplace transform finite element method for solving the convection–dispersion problem

The main objective of this paper is to evaluate the capabilities of the non-destructive impact-response method in detecting the existence of defects in piles with high slenderness ratio. The impact-echo technique was developed many years ago for non-destructive detection of defects in piles and structures. However, it is recognised that this method could be applied confidently for piles with length/diameter relationship up to 20. In the last years, with the development of hardware in computers and sensors, it is now possible to use this method for piles with high slenderness ratio. In this paper, piles of length/diameter relationship of about 40 are studied. A numerical-experimental study was carried out. It was shown that the results of the experiment were in agreement with those of the numerical studies and the accuracy of the impact-echo method was influenced by the type, size and location of the defects. Taking into account the uncertainties related to the studied problem, the obtained results preliminarily allow consider this NDT methodology as a promissory alternative to detect defects in piles with high slenderness ratio.

Efficient automated print defect mapping is valuable to the printing industry since such defects directly influence customer-perceived printer quality and manually mapping them is cost-ineffective. Conventional methods consist of complicated and hand-crafted feature engineering techniques, usually targeting only one type of defect. In this paper, we propose the first end-to-end framework to map print defects at pixel level, adopting an approach based on semantic segmentation. Our framework uses Convolutional Neural Networks, specifically DeepLab-v3+, and achieves promising results in the identification of defects in printed images. We use synthetic training data by simulating two types of print defects and a print-scan effect with image processing and computer graphic techniques. Compared with conventional methods, our framework is versatile, allowing two inference strategies, one being near real-time and providing coarser results, and the other focusing on offline processing with more fine-grained detection. Our model is evaluated on a dataset of real printed images.

In many engineering problems, the meshfree methods (MMs) have been dynamically projected and increasingly advanced in order to overwhelm some hitches in the predictable numerical methods. Over the past three decades in many different application area, MMs have found their way ranging from solid mechanics analysis, fluid problems, vibration analysis, heat transfer and optimization to numerical solutions of all kinds of (partial) differential equations. As every technique has shortcomings, the meshfree method also has drawbacks like higher computational cost and imposition of boundary condition which can be overruled by coupling it with the finite element method (FEM). In the past two decades, coupled MMs and FEM have appeared into a new session of computational methods with significant achievement. In addition, a noteworthy amount of growth has been made in addressing the major deficiencies that were present in the conventional methods and MMs at the premature phases. The objective of the present work is to provide a comprehensive review of various coupling techniques used for interface elements of MMs and FEM and general discussion on shape function formulation of FE and element free Galerkin method (EFGM). Key contribution of coupling techniques for coupled EFGM and FEM to structure mechanics application as primary concern.

The failure progression characteristics of adhesively bonded Carbon Fiber Reinforced Polymer (CFRP) composites are investigated using Acoustic Emission (AE) technique. Different failure progression modes such as matrix cracking, fiber breakage, delamination and through-thickness crack growth releases AE waveforms in different frequency domains. The characteristic features of these different AE waveforms are studied in Mel Scale, which is a perpetual frequency scale of average human hearing frequency. The recurring noise in the recorded waveforms has been identified more efficiently when the waveforms are analysed in Mel Scale. The recorded AE signals from the adhesively bonded CFRP under static tensile loading are stretched to match the Mel filter banks. The sampling rate of the recorded signal is adjusted from 1 MHz to 20 kHz. Following that, the Mel spectrogram and its cepstral coefficients are used for identifying the different failure modes from which the AE signals are generated. A comprehensive comparison of the AE analysis in Mel scale with conventional waveform processing techniques such as Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), Wavelet Packet Transform (WPT) and Hilbert-Huang Transform (HHT) has been made. The advantages and further applications of Mel Scale over traditional waveform processing techniques in defining the failure modes in the composites are also discussed.

Sanitary ceramic products, such as toilet and wash basin, are widely used in our daily life. Sanitary ceramics are expected to have some excellent physical properties, such as corrosion resistance, easy cleaning, and low water absorption. However, surface defects in sanitary ceramics are inevitable due to complex production processes and changing production environment. Therefore, surface defect detection must be performed in the manufacturing process of sanitary ceramics. There are many types of surface defects in sanitary ceramics, and different types of defects have large differences in characteristics and scales. Traditional detection methods with artificially designed features and classifiers are difficult to apply in this context. In addition, there are few studies on surface defect detection methods of sanitary ceramics based on deep neural networks. In this article, a lightweight real-time defect detection network based on the lightweight backbone MobileNetV3 is presented. The proposed network achieves multi-scale detection of surface defects in sanitary ceramics with a multi-layer feature pyramid. Combining region proposal network and anchor-free method, real-time defect detection is achieved. Finally, a detection head with channel attention structure and a low-level mixed feature classification strategy is used to perform defect classification with higher accuracy. Experimental results show that the proposed approach achieves at least 22.9% detection speed improvement and 35.0% average precision improvement while reducing memory consumption by at least 8.4% compared with the classic one-stage SSD, YOLO V3 and two-stage Faster R-CNN methods.

The rapidly increasing population in the Kathmandu valley has demanded for the construction of multi-storeyed buildings. Such structures can be vulnerable to failures as they are constructed on soft fluvio-lacustrine sediments without detailed deformation analysis of their foundation. Hence, the Bir Hospital Trauma Centre is taken here as a case study for the deformation analysis. The settlement calculated by conventional test methods (e.g., oedometer test, compressibility index, etc.) are purely one dimensional and do not represent the actual value where lateral influence s are possible. For example, the settlement reckoned from conventional methods was 41.6 mm for the stratification below 4 m depth and 24 mm for the stratification below 8 m depth. On the other hand, using a finite element method, it was 42.5 mm for the stratification below 8 m depth.

Topology optimization of bounded acoustic problems using the hybrid finite element-wave based method