Detailed GPR Survey for Detecting Boulders Buried in a Subgrade of a Highway Under Construction

Kisanuki, Hiroshi; Aoike, Kunio; Ogahara, Takanori; Inazaki, Tomio

doi:10.4133/sageep.31-035

Cited by 3 publications

(1 citation statement)

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“…archaeological treasure hunting [7]), transportation (e.g. highway subgrade detection [8]), urban construction (e.g. detection of urban underground metal pipelines [9]), engineering construction (e.g.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis and Classification Algorithms Comparison for Underground Target Detection

Duan

Wan

et al. 2019

IEEE Access

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Underground target detection technology has been widely used in urban construction and resource exploration. With the development of industrial modernization, the demand for underground target detection is becoming more specific, such as the material and shape of underground targets. Therefore, it is necessary to classify the properties of underground targets. In this paper, sensitivity analysis was performed on the spheroid model and the approximate forward model at first, and the influence of the target properties on the model output is obtained. Secondly, we utilized the fitting algorithm to obtain the model parameters of the simulation data (model response of targets with varying shapes and materials), and analyzed the influence of the fitting algorithm on the classification results at different SNR. Finally, eight machine learning algorithms: support vector machine(SVM), neural network(NN), quadratic discriminant analysis (QDA), Gaussian process (GP), decision tree (DT), random forest (RF) and AdaBoost were used in this study to compare the obtained results. From the above analysis, we found that the shape (radius) have a greater influence on the model than the material (permeability) in the spheroid model. According to the approximate forward model, we found that it is not feasible to classify targets when the orientation is unknown. The influence of the fitting algorithm on the classification performances is related to the noise level. The obtained results using neural network demonstrated that the proposed method outperformed in material-based classification and shape-based classification. In the material-based classification, the classifier generally has a weaker ability to distinguish between permeable materials.

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Section: Introductionmentioning

confidence: 99%