Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN

Birkhoff, 1979

Wen, 2017, Cause analysis on the void under slabs of cement concrete pavement, 205(1), 012011

Tang, 2019, Quantitative evaluation of tunnel lining voids by acoustic spectrum analysis, Constr. Build. Mater., 228, 116762, 10.1016/j.conbuildmat.2019.116762

Qu, 2016, Multi-bandwidth wavelet transform of impact echo signals for delamination detection of thin concrete slabs, J. Civ. Struct. Heal. Monit., 6, 649, 10.1007/s13349-016-0187-2

Liu, 2022, Characterizing compressive strength of compacted saline loess subjected to freeze-thaw cycling with wave velocity, Bull. Eng. Geol. Environ., 81, 1, 10.1007/s10064-022-02663-6

Zhao, 2019, Assessment of concrete pavement support conditions using distributed optical vibration sensing fiber and a neural network, Constr. Build. Mater., 216, 214, 10.1016/j.conbuildmat.2019.04.195

Xue, 2012, Improvement on recognition method of void beneath slab based on nondestructive testing technologies, 77

Zhang, 2019, Identification and detection of a void under highway cement concrete pavement slabs based on finite element analysis, Rudarsko-geološko-naftni zbornik, 34, 41, 10.17794/rgn.2019.3.5

Tarefder, 2013, Consistency and accuracy of selected FWD backcalculation software for computing layer modulus of airport pavements, Int. J. Geotech. Eng., 7, 21, 10.1179/1938636212Z.0000000009

Han, 2019, Dynamic response solution of multi-layered pavement structure under FWD load appling the precise integration algorithm, CMC Comput. Mater. Contin., 59, 853

Saleh, 2016, Utilisation of the deflectograph data to evaluate pavement structural condition of the highway network, Road Mater. Pavem. Design, 17, 136, 10.1080/14680629.2015.1064823

Alland, 2017, Statistical model to detect voids for curled or warped concrete pavements, Transp. Res. Rec., 2639, 28, 10.3141/2639-04

Ahmed, 2017, Incorporation of GPR and FWD into pavement mechanistic-empirical design, Constr. Build. Mater., 154, 1272, 10.1016/j.conbuildmat.2017.06.105

Zhao, 2019, Support conditions assessment of concrete pavement slab using distributed optical fiber sensor, Transportmetrica A, 15, 71, 10.1080/23249935.2018.1457733

Rhee, 2020, A practical approach to condition assessment of asphalt-covered concrete bridge decks on korean expressways by dielectric constant measurements using air-coupled GPR, Sensors, 20, 2497, 10.3390/s20092497

Zhang, 2020, In-situ recognition of moisture damage in bridge deck asphalt pavement with time-frequency features of GPR signal, Constr. Build. Mater., 244, 10.1016/j.conbuildmat.2020.118295

Nobes, 2017, Ground penetrating radar response from voids: a demonstration using a simple model, NDT & E Int., 91, 47, 10.1016/j.ndteint.2017.05.007

Kravitz, 2019, Void detection in two-component annulus grout behind a pre-cast segmental tunnel liner using ground penetrating radar, Tunn. Undergr. Space Technol., 83, 381, 10.1016/j.tust.2018.09.032

Han, 2009, Imaging methods for detection of airfield runway disaster, 552

Tivive, 2020, Air void detection using variational mode decomposition with low rank, IEEE Sensors J., 20, 2600, 10.1109/JSEN.2019.2951698

Luo, 2020, Forward modelling on GPR responses of subsurface air voids, Tunn. Undergr. Space Technol., 103, 10.1016/j.tust.2020.103521

Lai, 2018, A blind test of nondestructive underground void detection by ground penetrating radar (GPR), J. Appl. Geophys., 149, 10, 10.1016/j.jappgeo.2017.12.010

Luo, 2020, GPR pattern recognition of shallow subsurface air voids, Tunn. Undergr. Space Technol., 99, 10.1016/j.tust.2020.103355

Shihab, 2002, Neural network target identifier based on statistical features of GPR signals, 4758, 135

Xie, 2013, GPR identification of voids inside concrete based on the support vector machine algorithm, J. Geophys. Eng., 10, 10.1088/1742-2132/10/3/034002

Xie, 2013, An automatic recognition algorithm for GPR images of RC structure voids, J. Appl. Geophys., 99, 125, 10.1016/j.jappgeo.2013.02.016

Zhang, 2020, Automatic detection of moisture damages in asphalt pavements from GPR data with deep CNN and IRS method, Autom. Constr., 113, 10.1016/j.autcon.2020.103119

Xiang, 2019, An improved convolutional neural network system for automatically detecting rebar in GPR data, 422

Xiang, 2021, Robust cascaded frequency filters to recognize rebar in GPR data with complex signal interference, Autom. Constr., 124, 10.1016/j.autcon.2021.103593

Dinh, 2019, Automated visualization of concrete bridge deck condition from GPR data, NDT & E Int., 102, 120, 10.1016/j.ndteint.2018.11.015

Ko, 2019, Performance analysis of detecting buried pipelines in GPR images using faster R-CNN, J. Converg. Inform. Technol., 9, 21

Tong, 2017, Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks, Constr. Build. Mater., 146, 775, 10.1016/j.conbuildmat.2017.04.097

Li, 2021, Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm, Constr. Build. Mater., 273, 10.1016/j.conbuildmat.2020.121949

Kim, 2018, Deep learning-based underground object detection for urban road pavement, Int. J. Pavem. Eng., 21, 1

Aydin, 2019, Transfer and multitask learning using convolutional neural networks for buried wire detection from ground penetrating radar data, 11012, 110120Y

Besaw, 2015, Deep convolutional neural networks for classifying GPR B-scans, 9454, 945413

Veal, 2018, Generative adversarial networks for ground penetrating radar in hand held explosive hazard detection, 10628, 106280T

Lei, 2019, Automatic hyperbola detection and fitting in GPR B-scan image, Autom. Constr., 106, 10.1016/j.autcon.2019.102839

Zou, 2021, Study on wavelet entropy for airport pavement inspection using a multi-static GPR system, Geophysics, 86, 1, 10.1190/geo2020-0361.1

Li, 2021, GPR-RCNN: An algorithm of subsurface defect detection for airport runway based on GPR, IEEE Robot. Automat. Lett., 6, 3001, 10.1109/LRA.2021.3062599

Artagan, 2020, Advances in the nondestructive condition assessment of railway ballast: a focus on GPR, NDT & E Int., 115, 10.1016/j.ndteint.2020.102290

Li, 2019, An effective FDTD model for GPR to detect the material of hard objects buried in tillage soil layer, Soil Tillage Res., 195, 104353, 10.1016/j.still.2019.104353

Howlader, 2015, FDTD based numerical framework for ground penetrating radar simulation, Progr. Electromagn. Res. M, 44, 127, 10.2528/PIERM15090304

Warren, 2016, gprMax: open source software to simulate electromagnetic wave propagation for ground penetrating radar, Comput. Phys. Commun., 209, 163, 10.1016/j.cpc.2016.08.020

Bianchini Ciampoli, 2019, Signal processing of GPR data for road surveys, Geosciences, 9, 1, 10.3390/geosciences9020096

Benedetto, 2017, An overview of ground-penetrating radar signal processing techniques for road inspections, Signal Process., 132, 201, 10.1016/j.sigpro.2016.05.016

Shorten, 2019, A survey on image data augmentation for deep learning, J. Big Data, 6, 1, 10.1186/s40537-019-0197-0

He, 2016, Deep residual learning for image recognition, 770