Detection Of Voids Research Articles

Detection of Voids under a Concrete Airfield Parking Apron using a Heavy Weight Deflectometer (HWD)

A series of incidents involving aircraft punching through pavements prompted the Navy to develop procedures for detecting voids on airfields. Initially, a successful void detection survey was completed at a Naval Air Station (NAS) where several voids were generated by leakage of large underground drainpipes. To determine the location of the voids, a procedure was developed which used a combination of methods such as internal videotaping of the pipes, heavy weight deflectometer (HWD) testing, ground penetrating radar (GPR), and dynamic cone penetrometer (DCP) testing. This procedure is now routinely applied at all 70 major Navy and Marine Corps airfields. This paper summarizes the methodology and presents a recent field case where a waterline ruptured under a concrete airfield parking apron. After evaluating an area of interest with a HWD possible voids were detected under a number of slabs which were then drilled to be able to take DCP measurements. Grout was used to fill in the voids and Pavement-Transportation Computer Assisted Structural Engineering (PCASE) analysis showed that the pavement should be able to support the estimated traffic levels of that airfield for the next 10 years.

Subsurface Soil Enhancement to Strengthen the Ground under the Concrete Pavements in Operating Zones.

This paper outlines the suitable techniques employed for soil improvement in Heavy industries, oil & gas refinery, and petrochemical Plants. The materials and procedures will be detailed within this paper, with any exceptions to the specifications clearly indicated. The primary scope of work involves the filling of sand and gravel, as well as the injection of soil cement at designated cavity locations beneath the concrete grade slab in areas where space is limited and accessibility to the ground is challenging. The proposed method aims to fill the voids between the top of the foundation soil and the underside of the slabs, while ensuring the provision of all necessary equipment, tools, and a skilled workforce for geotechnical operations. Each testing location, including DCP Testing, void detection, and the installation of shallow monitoring wells, will be addressed in accordance with the specific site conditions.

Practice and theoretical analysis of ground penetrating radar in voids detection of urban underground pipe-jacking

Practice and theoretical analysis of ground penetrating radar in voids detection of urban underground pipe-jacking

SWC-Net and Multi-Phase Heterogeneous FDTD Model for Void Detection Underneath Airport Pavement Slab

SWC-Net and Multi-Phase Heterogeneous FDTD Model for Void Detection Underneath Airport Pavement Slab

Acoustic Tunnel Lining Void Detection: Modeling and Instrument System Development

The detachment of railway tunnel lining constitutes a grave danger to train operation safety and drastically curtails the tunnel’s service life. This study endeavors to efficiently detect the void defects in railway tunnel lining by creating a finite element model of tunnel lining structures. Utilizing this model, the study simulates the nonlinear acoustic wave propagation cloud maps for three representative tunnel lining structures: void-free, air void, and water void. This facilitates a thorough examination of the acoustic signal characteristics in the wavefield, time domain, and frequency domain. To satisfy the precision and efficiency demands of tunnel lining void detection, this study has devised and developed a portable acoustic detector that incorporates automatic analysis and processing capabilities and is furnished with a high-performance rare-earth magneto-strictive acoustic excitation device. This detection system can swiftly detect and assess typical void defects in tunnel lining. To further validate the effectiveness of this system, this study conducted lining defect detection in the Pingdao Railway Tunnel in the eastern Qinling Mountains. The test results show that the detection rate of this system for both air-filled and water-filled voids with a width of 1 m reached 100%, demonstrating its extremely high application value.

Raspberry Pi-Based IoT System for Grouting Void Detection in Tunnel Construction

This paper presents an IoT-based solution for detecting grouting voids in tunnel construction using the Raspberry Pi microcomputer. Voids between the primary and secondary tunnel linings can compromise structural integrity, and traditional methods like GPR lack continuous feedback. The proposed system uses embedded electrical wires in the secondary lining to measure conductivity, with disruptions indicating unfilled voids. The Raspberry Pi monitors this in real time, uploading data to a cloud platform for engineer access via smartphone. Field tests were conducted in a full-scale, 600 m long tunnel to evaluate the system’s effectiveness. The tests demonstrated the system’s accuracy in detecting voids in various tunnel geometries, including straight sections, curves, and intersections. Using only the proposed void detection system, the largest void detected post-grouting was 1.8 cm, which is within acceptable limits and does not compromise the tunnel’s structural integrity or safety. The system proved to be a cost-effective and scalable solution for real-time monitoring during the grouting process, eliminating the need for continuous manual inspections. This study highlights the potential of IoT-based solutions in smart construction, providing a reliable and practical method for improving tunnel safety and operational efficiency during grouting operations.

Void detection in structural adhesive joints using a k-Nearest Neighbors model with features from Electromechanical Impedance Spectroscopy

Electromechanical Impedance Spectroscopy (EMIS) is a promising Structural Health Monitoring (SHM) method which allows the early detection of defects by analyzing the structure response to an AC electrical signal swept through a range of high frequencies. In this work, EMIS measurements of pristine and damaged adhesive joints were performed, and features were extracted from the experimental measurements. These features were inputted to a k-Nearest Neighbors (kNN) model for damage detection. Results show that only one type of features is enough for damage detection. Furthermore, the use of the Manhattan Distance in the kNN enables a better classification.

Investigation of additively manufactured metamaterials for load-bearing structural applications

The study explores the potential application of Triply Periodic Minimal Surface (TPMS) metamaterials in structural engineering and architecture. It evaluates parametric design possibilities and analysis approaches while examining the mechanical properties of five TPMS structures (Gyroid, Schwarz, Diamond, Split, and Neovius). Comparisons are made between Additively Manufactured (AM) samples and numerical simulations. Gyroid and Diamond structures, which display advantageous properties, undergo further investigation with parametric design for uniform and non-uniform lattices. The study proposes simplified analytical formulations for metamaterial design, along with Homogenization analysis and detailed FEM analysis. Compression tests reveal distinct failure modes and stress-strain behaviors for each TPMS. The research suggests further investigation into the influence of the 3D printing process, void detection, and thermomechanical analysis. Finally, the study provides insights into tailored applications of these lattice metamaterials in structural engineering.

Cross-Halbach magnetic levitation configuration for density-based measurement, separation and detection

Magnetic levitation (MagLev) is a well-documented and widely-applied technique in accurate, sensitive, and untethered testing of living and non-living materials in both macroscale and microscale. However, most magnetic levitation configurations face the dilemma between working range and sensitivity, since the distance between the two like-pole-facing magnets are in positive relationship with sensitivity, but negative with measuring range. And there exists a maximum distance for MagLev configurations where the misalignment happens when the distance is too large, which is a restriction on the working space and measurement range of the MagLev devices. In this paper, we propose a novel configuration based on the principle of Halbach array, namely the Cross-Halbach MagLev configuration. By doing parallel comparisons with the Standard MagLev consisting of magnets of the same size, we determined the superiority of the Cross-Halbach configuration in magnetic flux density (0.47 T) and B→∙∇B→z (6.25 T2/m), maximum distance (90 mm), sensitivity (525 mm/(g/cm3)) and wide linear zone on B→∙∇B→z (42 mm). To demonstrate and further explore the potential of the Cross-Halbach MagLev, we carried out a series of experiments, including density measurements of standard beads and different polymers, detection and separation of similar polymers with minute difference in density, and nondestructive detection of voids. The results indicate that the proposed magnetic levitation configuration is competent of density-based applications and can serve as a new solution to high-sensitivity measurement and detection.

Nondestructive Defect Detection in 3D X-ray Microscopy Data of Ball Grid Array Solder for Void Detection in Solder Joints using Deep Learning

Abstract A deep learning-based nondestructive approach for void segmentation in BGA solder balls using 3D x-ray microscopy is presented.

Identification of internal voids in pavement based on improved knowledge distillation technology

Investigating methods for the detection of internal voids within road structures is a critical measure to ensure the safety and integrity of roadway operations. The purpose of this research is to investigate on the identification method of internal voids in pavement based on improved knowledge distillation technology. Ground penetrating radar data in three dimensions were extensively collected to capture the internal voids present within roadways, and this data was subsequently validated through in-situ verification. The echo characteristics of ground penetrating radar for areas with road voids were analyzed, and a dataset containing 1700 images of these internal voids was established. A YOLOv8 model improvement method was proposed, and a model for the detection of internal road voids was constructed based on the improved YOLO v8 framework. To further refine the model's performance, a knowledge distillation method based on multiple guidance from teacher assistants was developed. A stochastic learning approach was integrated, resulting in the establishment of a model optimized by this stochastic learning scheme for the identification of internal road voids. The results demonstrate that the presence of overfitting during the training phase of the void identification model can restrict its performance within a certain domain. The proposed stochastic learning-optimized, multi-teacher assistant guided knowledge distillation model, adeptly harnesses the performance benefits of both the teacher and assistant models by means of knowledge transfer, consequently achieving a significant improvement in the detection of internal road voids.

Explainable artificial intelligence prediction of defect characterization in composite materials

Non-destructive evaluation (NDE) techniques are integral across diverse applications for void detection within composites. Infrared (IR) thermography (IRT) is a prevalent NDE technique that utilizes reverse heat transfer principles to infer defect characteristics by analyzing temperature distribution. Although the forward heat transfer problem is well-posed, its inverse counterpart lacks uniqueness, posing non-unique solutions. The present study performs simulations using finite element analysis (FEA) in defective (a penny-shaped defect) composites through which the heat transfer flux is modeled. A total of 2100 simulations with various defect positions and sizes (depth, size, and thickness) are executed, and the corresponding surface temperature vs. time and vs. distance diagrams are extracted. The FEA outputs provide ample input data for developing an explainable artificial intelligence (XAI) model to estimate the defect characteristics. A detailed feature engineering task is performed to select the representative information from the diagrams. Explainable decision tree-based machine learning (ML) models with transparent decision paths based on derived features are developed to predict the defect depth, size, and thickness. The ML models’ results suggest superb accuracy (R2 = 0.92 to 0.99) across all three defect characteristics. The provided workflow sets a benchmark applicable to a range of fields, including health monitoring.

Study of Void Detection Beneath Concrete Pavement Panels through Numerical Simulation

Study of Void Detection Beneath Concrete Pavement Panels through Numerical Simulation

FFF print defect characterization through in-situ electrical resistance monitoring

Fused filament fabrication is a popular fabrication technique. Currently there is a need for in-situ monitoring modalities to gather real-time information on prints, both for quality control and closed-loop control. Despite current advancements, effective and affordable in-situ monitoring techniques for non-destructive defect detection of voids and bonding quality are still limited. This work demonstrates in-situ monitoring of fused filament fabrication through electrical resistance measurements as an alternative to thermal and optical methods. A new, easy-to-implement setup is demonstrated which measures the electrical resistance of a conductively doped filament between the nozzle and single or multi-electrodes on the bed. Defects can be located in an unprecedented way with the use of encoded axes in combination with the observed resistance variations throughout the part. A model of the anisotropic electrical conduction is used to interpret the measurements, which matches well with the data. Warping, inter-layer adhesion, under-extrusion and overhang sagging print defects can be observed in the measurements of parts with a complex geometry, which would be difficult to measure otherwise. Altogether in-situ electrical resistance monitoring offers a tool for optimising prints by online studying the influence of the print parameters for quality assessment and it opens up possibilities for closed-loop control.

Segmentation of void defects in X-ray images of chip solder joints based on PCB-DeepLabV3 algorithm

Defects within chip solder joints are usually inspected visually for defects using X-ray imaging to obtain images. The phenomenon of voids inside solder joints is one of the most likely types of defects in the soldering process, and accurate detection of voids becomes difficult due to their irregular shapes, varying sizes, and defocused edges. To address this problem, an X-ray void image segmentation algorithm based on improved PCB-DeepLabV3 is proposed. Firstly, to meet the demand for lightweight and easy deployment in industrial scenarios, mobilenetv2 is used as the feature extraction backbone network of the PCB-DeepLabV3 model; then, Attentional multi-scale two-space pyramid pooling network (AMTPNet) is designed to optimize the shallow feature edges and to improve the ability to capture detailed information; finally, image cropping and cleaning methods are designed to enhance the training dataset, and the improved PCB-DeepLabV3 is applied to the training dataset. The improved PCB-DeepLabV3 model is used to segment the void regions within the solder joints and compared with the classical semantic segmentation models such as Unet, SegNet, PSPNet, and DeeplabV3. The proposed new method enables the solder joint void inspection to get rid of the traditional way of visual inspection, realize intelligent upgrading, and effectively improve the problem of difficult segmentation of the target virtual edges, to obtain the inspection results with higher accuracy.

Effect of the void and crack quantity and location on ultrasound: resin experiments and carbon brick validation

ABSTRACT In industrial applications, there is a lack of methods applicable to large-scale, rapid and non-destructive detection of voids and cracks inside carbon bricks. This paper used the ultrasonic testing (UT) A-scan method to investigate the influence of quantity (void fraction:1.4%-4.6%; number of artificial cracks:1–7) and location (five layers) of voids and cracks inside the resin on ultrasonic waves. Both fast Fourier transform (FFT) and synchrosqueezed wavelet transform (SWT) are employed to analyse the amplitude, main frequency and normalised peak time of ultrasonic waves. The results indicate that increased void fraction and number of cracks lead to greater amplitude attenuation, promoting the transition from the fundamental wave to harmonics. Void and crack location symmetrically affect ultrasound amplitude, main frequency, and normalised peak time around the sample centre. Meanwhile, fitting equations are established for the main frequency of the fundamental wave (f 1) in relation to defect quantity. A high-precision quantitative defects detection standard is also developed based on normalised peak time. The validation based on carbon bricks indicates that the derived empirical relations between these parameters and defects can lay the foundation for large-scale and rapid non-destructive detection in carbon bricks.

Automated void detection in high resolution x-ray printed circuit boards (PCBs) images with deep segmentation neural network

Defect detection and quantification on printed circuit board (PCB) assemblies are crucial for ensuring the quality of electronic products. X-ray inspection is commonly used to detect internal defects, in which high-resolution gray-scale images are captured to reveal the internal structure, particularly the defect features, of an assembled PCB. To detect defect features, conventional computer vision techniques require hand-crafted feature filters and usually have a narrow application scope. This study introduces an automated method for detecting voids using supervised machine learning. The method employs end-to-end segmentation models to identify pixels that belong to the void and integrated circuit (IC) regions in X-ray PCB images. The segmentation results are then used to compute the percentage of voids within each IC, which is a key measure of the quality of the inspected PCB. Accurate predictions with an intersection over union of over 0.95 have achieved. A challenge in this development is the large size of the images, which can reach to millions of pixels, and the small scale of defect features. Training a deep segmentation model for large-scale images using conventional methods requires a lot of memory. Downscaling high-resolution images result in the loss of defect features. In this study, a unique training method utilizing the local nature of the defects is proposed, which reduces memory consumption and speeds up the training process. Additionally, to overcome the limited training data, various augmentation methods are applied and transfer learning is employed to construct segmentation models for different types of PCBs.

Detection and delineation of cracks and voids in concrete structures using the ground penetrating radar technique

The accurate detection of early-age cracking and voids in concrete structures is vital for assessing their health and preventing deterioration, ultimately saving lives and resources. While Ground Penetrating Radar (GPR) offers a widely used method for concrete inspection and defect detection, its accuracy is subject to various limitations influenced by factors such as software, defect dimensions, moisture, and the analysis and interpretation of data. This study presents comprehensive techniques for detecting, delineating, and analyzing hidden cracks and voids in concrete using GPR, validated through laboratory testing and real-world applications. Introducing “DefectInspectoPy,” Python software, aims to address limitations in traditional GPR data processing software and defect detection methods. Results demonstrate the effectiveness of the proposed methods in accurately locating, analyzing, and delineating defects, aiding in determining coring locations for concrete structures. These findings have significant implications for enhancing safety, reducing maintenance costs, and preserving concrete infrastructure integrity.

3D Full-Waveform Tomography of Standard Penetration Test-Seismic Wavefields in Karst Florida Limestone

The shallow subsurface across large parts of Florida consists of weathered karst limestone, a spatially variable porous stratigraphy with significant influence in the design and load capacity of deep foundations. For reliable load transfer of the super structure into the bearing soil/rock, detection of voids and weak zones is crucial. This study investigated the capabilities of a recently developed standard penetration test (SPT)-seismic testing method for characterizing a large volume of soil/rock properties with a single SPT. The method utilizes a three-dimensional full-waveform inversion (3D FWI) of wavefields induced by SPT blows at depths (in-depth source) to characterize the subsurface around the SPT borehole. A site in Florida that presents shallow, surface karst limestone, was the location of a field experiment that consisted of two 36- × 18-m areas, each with an SPT at the area center. Seismic wavefields induced by SPT blows at depth were recorded by 72 vertical geophones on the ground surface and analyzed by the 3D FWI. The results revealed that subsurface soil/rock properties were characterized in submeter pixels over a large 3D domain of 24 × 36 × 18 m (depth × length × width). Multiple voids at various depths from 5 to 17 m were successfully detected at the site and confirmed by SPT data. The results suggested that the SPT-seismic method is an efficient tool for site investigation, as a bridge pier or pile group could be designed with only one SPT.

Detection and diagnosis of concrete void defect using percussion-based method combined with convolutional neural network

As a fundamental construction material, concrete commonly encounter void defects, which could significantly impair the integrity of concrete members and seriously weaken their load-bearing capacity. Worse still, void defects typically exist inside concrete members, making it difficult to detect them in real time. Therefore, conducting regular inspections for internal void defects of concrete structures is of great importance to maintain structural health. This paper investigates an integrated method to detect concrete voids based on percussion approach and a convolutional neural network (CNN). Firstly, percussion-induced sound signals were collected from 18 concrete specimens with different precast void defects. Each specimen was tapped 100 times, for a total of 1,800 percussion-induced acoustic signals. Subsequently, continuous wavelet transform (CWT) and Mel frequency cepstral coefficient (MFCC) were introduced to convert the collected acoustic signals into two types of images. The converted images were divided into testing and training datasets according to the ratio of 1:3, and 600 images were input to the CNN as the test dataset and 1200 images as the training dataset. The CNN was then trained and verified by the joint input of CWT and MFCC images to perform concrete voids classification. The result shows that the proposed method presents a satisfying classification accuracy of 100 %. Next, percussion tests under 5 levels of noisy environments were conducted, and the results prove the remarkable anti-noise performance of the methodology by achieving 88.12 % accuracy even at the strongest signal-to-noise ratio of 0 dB. Finally, the superiority of the proposed method was further demonstrated through the accuracy comparison with machine learning (ML) methods, with the accuracy consistently outperforming over 25 %. In conclusion, the proposed method has a great potential for future in-situ applications in concrete void detection.