투수 콘크리트 블록 공극률 및 투수계수 평가를 위한표면 이미지 분석 기법 개발
Surface Image Analysis for Evaluating Porosity and Permeability Coefficient of Permeable Concrete Block
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초록
The increase of impermeable area ratio is causing hydrologic cycle problems in urban areas and groundwater depletion in rural areas, permeablepavements are getting attention to expand permeable areas. The performance of the permeable concrete block pavement, which is part of the permeablepavement, is greatly affected by the porosity. In addition, the permeability coefficient is a major factor when designing permeable concrete blockpavement. Existing porosity and permeability test methods have problems such as uneconomical or poor field applicability. The object of this studywas to develop a methodology for evaluating porosity and permeability coefficient using a surface image of a permeable concrete block. Specimensare manufactured with various porosity ranges and porosity and permeability tests are performed. After surface image preprocessing, normalization andbinarization methods were compared. Through this, the method with the highest correlation with the lab test result was determined. From the results,the PDR (pore determined ratio) was obtained. Simple linear regression analysis is performed with PDR and lab test results. The results showed a highcorrelation of R2 more than 0.8, and the errors were also low.
Abstract
The increase of impermeable area ratio is causing hydrologic cycle problems in urban areas and groundwater depletion in rural areas, permeablepavements are getting attention to expand permeable areas. The performance of the permeable concrete block pavement, which is part of the permeablepavement, is greatly affected by the porosity. In addition, the permeability coefficient is a major factor when designing permeable concrete blockpavement. Existing porosity and permeability test methods have problems such as uneconomical or poor field applicability. The object of this studywas to develop a methodology for evaluating porosity and permeability coefficient using a surface image of a permeable concrete block. Specimensare manufactured with various porosity ranges and porosity and permeability tests are performed. After surface image preprocessing, normalization andbinarization methods were compared. Through this, the method with the highest correlation with the lab test result was determined. From the results,the PDR (pore determined ratio) was obtained. Simple linear regression analysis is performed with PDR and lab test results. The results showed a highcorrelation of R2 more than 0.8, and the errors were also low.
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