• International Journal of Highway Engineering
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• v.18 no.6
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• pp.131-136
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• 2016

OBJECTIVES : The objective of this research is to determine the integrity of pavement structures for areas where voids exist. Furthermore, we conducted the study of voided-area analysis and remaining life prediction for pavement structures using finite element method. METHODS : To determine the remaining life of the existing voided areas under asphalt concrete pavements, field and falling weight deflectometer (FWD) tests were conducted. Comparison methods were used to have better accuracy in the finite element method (FEM) analysis compared to the measured surface displacements due to the loaded trucks. In addition, the modeled FEM used in this study was compared with well-known software programs. RESULTS : The results show that a good agreement on the analyzed and measured displacements can be obtained through comparisons of the surface displacement due to loaded trucks. Furthermore, the modeled FEM program was compared with the available pavement-structure software programs, resulting in the same values of tensile strains in terms of the thickness of asphalt concrete layers. CONCLUSIONS : The study, which is related to voided-area analysis and remaining life prediction using FEM for pavement structures, was successfully conducted based on the comparison between our methods and the sinkhole grade used in Japan.

• Journal of the Korean Geotechnical Society
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• v.38 no.9
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• pp.69-77
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• 2022

Several studies have been conducted to analyze the risk of ground subsidence occurring in urban areas. Recently, the correlation between the density of underground utilities (i.e., the quantity of buried utilities in the analysis area) and the recorded ground subsidence has been explored to analyze such risk through. Choi et al. (2021) proposed an algorithm to optimize the correlation between the ground subsidence and normalized linear density of underground pipelines. In this study, the optimization algorithm was modified for analysis based on the risk grade. The analysis results using the modified optimization algorithm were compared with the correlation analysis results between the density of underground utilities and recorded ground subsidence presented by Choi et al. (2021). Compared with Choi et al. (2021), three analysis results showed equal or higher accuracy in the correlation analysis with recorded ground subsidence according to risk grade. In particular, for R100, it was divided into five grades and compared with the ratio of the recorded ground subsidence that occurred in grades 4 or higher. As a result, Choi et al. (2021) showed that 86% of recorded ground subsidence occurred in grades 4 or higher, whereas this study showed 93%. It was confirmed that the accuracy of the modified optimization algorithm was improved. The modified optimization algorithm can be applied to develop a ground subsidence risk map for each grade in an urban area, which can be used as basic data for decision-making for underground utility maintenance.

• Journal of the Korean Geotechnical Society
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• v.37 no.10
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• pp.77-87
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• 2021

Several studies have been conducted to analyze, predict, and prevent the risk of ground subsidence occurring in urban areas. Nevertheless, there is insufficient research effort on risk analysis that utilizes the correlation between the density of underground structures (i.e., the spatial quantity of buried objects installed in the ground around the interested area) and the occurrence of ground subsidence. In this paper, a study was conducted to analyze the line density of underground structures using GIS-based spatial information data, and to link this with the recorded ground subsidences. An optimization algorithm was developed to maximize the correlation between the line density of 29 recorded ground subsidences and 6 types of underground structures that occurred between 2010 and 2015 for the analysis area. The concept of normalized line density was also proposed for the analysis. The normalized line density of the analysis area was divided into five grades (Grade 1: lowest, Grade 5: highest). When the optimization algorithm was applied, the case where the normalized line density was Grade 4 or higher at the location of the recorded ground subsidences was about > 80%. It is thought that the density analysis result of underground facilities can be applied to the ground subsidence risk analysis by using the proposed optimization algorithm.