Mechanical and leaching behaviour of slag-cement and lime-activated slag stabilised/solidified contaminated soil.

Stabilisation/solidification (S/S) is an effective technique for reducing the leachability of contaminants in soils. Very few studies have investigated the use of ground granulated blast furnace slag (GGBS) for S/S treatment of contaminated soils, although it has been shown to be effective in ground improvement. This study sought to investigate the potential of GGBS activated by cement and lime for S/S treatment of a mixed contaminated soil. A sandy soil spiked with 3000mg/kg each of a cocktail of heavy metals (Cd, Ni, Zn, Cu and Pb) and 10,000mg/kg of diesel was treated with binder blends of one part hydrated lime to four parts GGBS (lime-slag), and one part cement to nine parts GGBS (slag-cement). Three binder dosages, 5, 10 and 20% (m/m) were used and contaminated soil-cement samples were compacted to their optimum water contents. The effectiveness of the treatment was assessed using unconfined compressive strength (UCS), permeability and acid neutralisation capacity (ANC) tests with determination of contaminant leachability at the different acid additions. UCS values of up to 800kPa were recorded at 28days. The lowest coefficient of permeability recorded was 5×10(-9)m/s. With up to 20% binder dosage, the leachability of the contaminants was reduced to meet relevant environmental quality standards and landfill waste acceptance criteria. The pH-dependent leachability of the metals decreased over time. The results show that GGBS activated by cement and lime would be effective in reducing the leachability of contaminants in contaminated soils.

Defects Detection and Assessment in Concrete Surfaces

Aside from cracks, the impact of other surface defects, such as air pockets and discoloration, can be detrimental to the quality of concrete in terms of strength, appearance and durability. For this reason, local and national codes provide standards for quantifying the quality impact of these concrete surface defects and owners plan for regular visual inspections to monitor surface conditions. However, manual visual inspection of concrete surfaces is a qualitative (and subjective) process with often unreliable results due to its reliance on inspectors’ own criteria and experience. Also, it is labor intensive and time-consuming. This paper presents a novel, automated concrete surface defects detection and assessment approach that addresses these issues by automatically quantifying the extent of surface deterioration. According to this approach, images of the surface shot from a certain angle/distance can be used to automatically detect the number and size of surface air pockets, and the degree of surface discoloration. The proposed method uses histogram equalization and filtering to extract such defects and identify their properties (e.g. size, shape, location). These properties are used to quantify the degree of impact on the concrete surface quality and provide a numerical tool to help inspectors accurately evaluate concrete surfaces. The method has been implemented in C++ and results that validate its performance are presented.

Detecting air pockets for architectural concrete quality assessment using visual sensing

Air pockets, one kind of concrete surface defects, are often created on formed concrete surfaces during concrete construction. Their existence undermines the desired appearance and visual uniformity of architectural concrete. Therefore, measuring the impact of air pockets on the concrete surface in the form of air pockets is vital in assessing the quality of architectural concrete. Traditionally, such measurements are mainly based on in-situ manual inspections, the results of which are subjective and heavily dependent on the inspectors’ own criteria and experience. Often, inspectors may make different assessments even when inspecting the same concrete surface. In addition, the need for experienced inspectors costs owners or general contractors more in inspection fees. To alleviate these problems, this paper presents a methodology that can measure air pockets quantitatively and automatically. In order to achieve this goal, a high contrast, scaled image of a concrete surface is acquired from a fixed distance range and then a spot filter is used to accurately detect air pockets with the help of an image pyramid. The properties of air pockets (the number, the size, and the occupation area of air pockets) are subsequently calculated. These properties are used to quantify the impact of air pockets on the architectural concrete surface. The methodology is implemented in a C++ based prototype and tested on a database of concrete surface images. Comparisons with manual tests validated its measuring accuracy. As a result, the methodology presented in this paper can increase the reliability of concrete surface quality assessment