Some measurements of the permeability of kaolin

The writers wish to present some additional data obtained independently and with different techniques that confirm the results published in the paper. For these tests, the speswhite kaolin clay was prepared as a slurry with a water content of 133 percent and was then consolidated one-dimensionally under an axial stress of 100 kPa in a 203 mm dia. tube. The results presented here show that the anisotropy of permeability is completely preserved (even after the sample is compressed isotropical) as long as the initial part of the stress path corresponds to one-dimensional compression. The data supports the speculation by the authors regarding permeability anisotropy for stress paths other than one-dimensional compression.

Analysis of asphalt concrete permeability data using representative pore size

The permeability of asphalt concrete has been the subject of much study by pavement engineers over the last decade. The work undertaken has tended to focus on high air voids as the primary indicator of permeable asphalt concrete. This paper presents a simple approach for understanding the parameters that affect permeability. Principles explained by Taylor in 1956 in channel theory work for soils are used to derive a new parameter-representative pore size. Representative pore size is related to the air voids in the compacted mix and the D75 of the asphalt mix grading curve. Collected Superpave permeability data from published literature and data collected by the writers at the Queensland Department of Transport and Main Roads is shown to be better correlated with representative pore size than air voids, reducing the scatter considerably. Using the database of collected field and laboratory permeability values an equation is proposed that pavement engineers can use to estimate the permeability of in-place pavements. © 2011 ASCE.

Real Time Concrete Damage Visual Assessment for First Responders

First responders are in danger when they perform tasks in damaged buildings after earthquakes. Structural collapse due to the failure of critical load bearing structural members (e.g. columns) during a post-earthquake event such as an aftershock can make first responders victims, considering they are unable to assess the impact of the damage inflicted in load bearing members. The writers here propose a method that can provide first responders with a crude but quick estimate of the damage inflicted in load bearing members. Under the proposed method, critical structural members (reinforced concrete columns in this study) are identified from digital visual data and the damage superimposed on these structural members is detected with the help of Visual Pattern Recognition techniques. The correlation of the two (e.g. the position, orientation and size of a crack on the surface of a column) is used to query a case-based reasoning knowledge base, which contains apriori classified states of columns according to the damage inflicted on them. When query results indicate the column's damage state is severe, the method assumes that a structural collapse is likely and first responders are warned to evacuate.

Shape-Based Retrieval of Construction Site Photographs

Digital photographs of construction site activities are gradually replacing their traditional paper based counterparts. Existing digital imaging technologies in hardware and software make it easy for site engineers to take numerous photographs of “interesting” processes and activities on a daily basis. The resulting photographic data are evidence of the “as-built” project, and can therefore be used in a number of project life cycle tasks. However, the task of retrieving the relevant photographs needed in these tasks is often burdened by the sheer volume of photographs accumulating in project databases over time and the numerous objects present in each photograph. To solve this problem, the writers have recently developed a number of complementary techniques that can automatically classify and retrieve construction site images according to a variety of criteria (materials, time, date, location, etc.). This paper presents a novel complementary technique that can automatically identify linear (i.e., beam, column) and nonlinear (i.e., wall, slab) construction objects within the image content and use that information to enhance the performance of the writers’ existing construction site image retrieval approach.