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

Xinyu Liu, Peiwen Hao, Aihui Wang, Liangqi Zhang, Bo Gu, Xinyan Lu

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Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Xinyu LiuCHANG'AN University, Xian, China.

Peiwen HaoCHANG'AN University, Xian, China.

Aihui WangZhongyuan University of Technology, Zhengzhou, China.

Liangqi ZhangHenan Wanli Road & Bridge Group Co. Ltd., Xuchang, China.

Bo GuSchool of Electric Power, North China University of Water Resource and Electric Power, Zhengzhou, China.

Xinyan LuSchool of Electric Power, North China University of Water Resource and Electric Power, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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%.

Indexed as

5 Ground penetrating radar (GPR)Feature extractionGrid search methodImage segmentationParticle swarm optimization (PSO)Support vector machine (SVM)

Identifiers

PMID33834102

DOI10.7717/peerj-cs.417

PMCPMC8022505

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.