Digital Image Processing (DIP) algorithms are often required as a precursor to measure the internal characteristics of pavement structures during X-ray computed tomography (XRCT) based non-destructive evaluation (NDE) of pavement materials. The improper use of DIP algorithms can result in the significant under- or over-estimation of internal pavement characteristics, thereby affecting pavement design and maintenance strategies. Past research studies highlighted the significance of threshold segmentation algorithms and binarization of greyscale images on the porosity and permeability characteristics of pervious pavement mixtures. In addition, the use of a watershed segmentation algorithm was introduced to separate interconnected pore network structure into multiple pores. However, isolated pores were not removed in past analyses found in the literature due to a lack of consideration in using ungrouping algorithm to segregate connected and isolated pores. The main objective of this study is to select the appropriate DIP algorithms that can be used to evaluate pervious pavement pore network properties from three-dimensional XRCT based images. In this paper, a key microstructural pore parameter was investigated using various DIP algorithms for different pervious pavement mixtures and recommendations are made. It is expected that the results presented in this paper can help researchers understand the importance of DIP algorithms on XRCT-based pavement evaluation studies.

Digital image processing of the X-ray computed tomography images involves the crucial step of image segmentation which affects the subsequent pore structure quantitative analysis. The main objective of this study is to investigate the effect of ten different global thresholding algorithms based on the grey scale histogram, clustering, entropy and laboratory volumetric characteristics on the internal pore structure properties of the pervious concrete. The key microstructural parameters of the pervious concrete air voids such as porosity, tortuosity, throat number, pore coordination number and distributions of pore volume, throat area, pore sphericity, shape factor and throat eccentricity were analyzed for different thresholding algorithms. It was found from the analysis that the nine histogram, clustering and entropy based algorithms are found to be either under or over estimating the air void voxels compared to the volumetric segmentation method. And as the threshold value increases, effective porosity and number of throats increases and isolated porosity and tortuosity decreases due to the increase of air void voxels and pore connectivity. Overall, it is expected that the present study will help in understanding the importance of threshold segmentation in the field of pavement image processing.

Pervious concrete is a special class of concrete with sufficient continuous void structure resulting in the increase of drainage, skid resistance and acoustic characteristics. The paper attempts to investigate the effect of pore network properties obtained using advanced image processing techniques on the non-Darcy permeability characteristics of pervious concrete samples obtained from the same batch mixing process. Twelve different pervious concrete samples for a single pervious concrete mixture were produced in the laboratory using batch mixing and its internal pore network structure was obtained using medical X-ray computed tomography (XRCT) and digital image processing. The pore network structure from the XRCT scan is then adopted into a finite-volume computational fluid dynamics permeability simulation model to evaluate how pore network characteristics can affect non-Darcy permeability coefficients. The key microstructural parameters of the pervious concrete air voids and solids were analyzed in the paper, and it was found that an increase in non-Darcy permeability coefficient can be attributed to higher effective porosity, mean effective pore volume, throat area and coordination number properties. Overall, the findings presented in the paper can help in future optimization of pervious concrete mixture design and provide an understanding towards future works on pavement mixture quality control.

The functional performance of pervious concrete pavement surfaces (such as hydraulic, acoustic, and frictional performances) is greatly influenced by the properties of its internal pore structure (such as effective porosity, intrinsic permeability, tortuosity, and pore size distribution). Nondestructive evaluation (NDE) using X-ray computed tomography (CT) and digital image processing (DIP) involves the crucial step of image segmentation of grayscale histograms, which can significantly affect subsequent pore structure analysis and fluid flow simulations. This paper presents a new discharge-based segmentation algorithm capable of predicting non-Darcy permeability of pervious concrete mixtures. The algorithm uses X-ray CT image-based finite-volume permeability simulations to determine the specific discharge at various hydraulic gradients. Experimental results of a falling-head permeability test were used to calibrate and validate the developed finite-volume models. The permeability simulation results from the developed thresholding algorithm were compared against simulation results obtained from 10 different global thresholding algorithms. It was found from the analyses that the developed discharge-based thresholding algorithm predicts non-Darcy permeability characteristics and the effective porosity of the pervious concrete mixtures more accurately than other global thresholding algorithms.

Pervious concrete is widely used as pavement surfaces as means to increase water infiltration for water storage or conservation purposes or to reduce surface runoff (and increase skid resistance) for roadway safety. A proper evaluation of pervious concrete pore network properties is important to ascertain the ability of the material to serve the intended purposes and X-ray computed tomography (CT) scan is one method that allows for the non-destructive evaluation of the pervious concrete specimens. Pore network structures can be derived from X-ray CT scan images through the use of segmentation algorithms. Current image processing-based segmentation algorithms, however, can yield significant errors when deriving pervious concrete pore network properties. This paper describes the use of the watershed segmentation algorithm on X-ray CT scans of pervious concrete pavement mix and evaluate essential pore network properties such as pore volume, flatness, elongation, and shape factor distributions. First, the fundamentals of the watershed segmentation algorithms are described. The paper next presents on the experimental program in testing pervious concrete mix and the use of X-ray CT scans in deriving images of the samples. The watershed algorithm of different elevation functions are then applied to derive the pore network properties and the results are presented. Finally, the advantages of this algorithm over existing image processing techniques are discussed.