Dataset Information

Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine.

ABSTRACT: In this paper, a method that uses a ground-penetrating radar (GPR) and the adaptive particle swarm support vector machine (SVM) method is proposed for detecting and recognizing hidden layer defects in highways. Three common road features, namely cracks, voids, and subsidence, were collected using ground-penetrating imaging. Image segmentation was performed on acquired images. Original features were extracted from thresholded binary images and were compressed using the kl algorithm. The SVM classification algorithm was used for condition classification. For parameter optimization of the SVM algorithm, the grid search method and particle swarm optimization algorithm were used. The recognition rate using the grid search method was 88.333%; the PSO approach often yielded local maxima, and the recognition rate was 86.667%; the improved adaptive PSO algorithm avoided local maxima and increased the recognition rate to 91.667%.

SUBMITTER: Liu X

PROVIDER: S-EPMC8022505 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine.

Liu Xinyu X Hao Peiwen P Wang Aihui A Zhang Liangqi L Gu Bo B Lu Xinyan X

PeerJ. Computer science 20210330

In this paper, a method that uses a ground-penetrating radar (GPR) and the adaptive particle swarm support vector machine (SVM) method is proposed for detecting and recognizing hidden layer defects in highways. Three common road features, namely cracks, voids, and subsidence, were collected using ground-penetrating imaging. Image segmentation was performed on acquired images. Original features were extracted from thresholded binary images and were compressed using the kl algorithm. The SVM class ...[more]

PMID: 33834102

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Project description:This paper presents the results of a research study where ground penetrating radar (GPR) was successfully used to reveal the remains of the Württemberg-Stambol Gate in the subsurface of Republic Square, in Belgrade, Serbia. GPR investigations were carried out in the context of renovation works in the square, which involved rearranging traffic control, expanding the pedestrian zone, renewing the surface layer, and valorising existing archaeological structures. The presence of the gate remains was suggested by historical documents and information from previous restoration works. A pulsed radar unit was used for the survey, with antennas having 200- and 400-MHz central frequencies. Data were recorded over a grid and two three-dimensional models were built, one for each set of antennas. The grid was the same for both sets of antennas, therefore the two models could be compared. Several horizontal cross sections of the models were plotted, corresponding to different depths; these images were carefully examined and interpreted, paying particular attention to signatures that could originate from the sought archaeological structures. Reflections coming from the gate remains were identified in both models, in the same region of the survey area and at the same depth; the geometry, size, and layout of the gate columns, as well as of other construction elements belonging to the gate, were determined with very good accuracy. Based on the GPR findings, archaeological excavation works were carried out in the region where the foundation remains were estimated to be. The presence of the remains was confirmed, with various columns and side walls. This case study demonstrates and further corroborates the effectiveness and reliability of GPR for the non-invasive prospection of archaeological structures hidden in the heterogeneous subsurface of urban environments. In the opinion of the authors, GPR should be incorporated as a routine field procedure in construction and renovation projects involving historical cities.

Project description:Agricultural subsurface drainage systems are commonly installed on farmland to remove the excess water from poorly drained soils. Conventional methods for drainage mapping such as tile probes and trenching equipment are laborious, cause pipe damage, and are often inefficient to apply at large spatial scales. Knowledge of locations of an existing drainage network is crucial to understand the increased leaching and offsite release of drainage discharge and to retrofit the new drain lines within the existing drainage system. Recent technological developments in non-destructive techniques might provide a potential alternative solution. The objective of this study was to determine the suitability of unmanned aerial vehicle (UAV) imagery collected using three different cameras (visible-color, multispectral, and thermal infrared) and ground penetrating radar (GPR) for subsurface drainage mapping. Both the techniques are complementary in terms of their usage, applicability, and the properties they measure and were applied at four different sites in the Midwest USA. At Site-1, both the UAV imagery and GPR were equally successful across the entire field, while at Site-2, the UAV imagery was successful in one section of the field, and GPR proved to be useful in the other section where the UAV imagery failed to capture the drainage pipes' location. At Site-3, less to no success was observed in finding the drain lines using UAV imagery captured on bare ground conditions, whereas good success was achieved using GPR. Conversely, at Site-4, the UAV imagery was successful and GPR failed to capture the drainage pipes' location. Although UAV imagery seems to be an attractive solution for mapping agricultural subsurface drainage systems as it is cost-effective and can cover large field areas, the results suggest the usefulness of GPR to complement the former as both a mapping and validation technique. Hence, this case study compares and contrasts the suitability of both the methods, provides guidance on the optimal survey timing, and recommends their combined usage given both the technologies are available to deploy for drainage mapping purposes.

Dataset Information

Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine.

Publications

Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine.

Similar Datasets

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