Pullout test correlations and in-place strength assessment - the European Concrete Frame Building Project

A full-scale, seven-story, reinforced concrete building frame was constructed in-place at the Building Research Establishment's Cardington Laboratory, which encompassed a range of different concrete mixtures and advanced construction techniques. This provided an opportunity to assess in-place nondestructive test methods, namely the pullout test, and more specifically the Danish version, which has been known as the Lok test, on a systematic basis during the construction of the building. It was used in conjunction with both standard and temperature-matched cube specimens to assess its practicality and accuracy under site conditions. Strength correlations were determined using linear and power function regression analysis. Strength predictions from these were found to be in very good agreement with the compressive strengths of temperature-matched cube specimens. When a general correlation is used, however, estimates for compressive strength are likely to have 95% confidence limits of around '20% of the mean value of four results.

'PERMIT' ion migration test for measuring the chloride ion transport of concrete on site

The ingress of chlorides into concrete is predominantly by the mechanism of diffusion and the resistance of concrete to the transport of chlorides is generally represented by its coefficient of diffusion. The determination of this coefficient normally requires long test duration (many months). Therefore, rapid test methods based on the electrical migration of ions have widely been used. The current procedure of chloride ion migration tests involves placing a concrete disc between an ion source solution and a neutral solution and accelerating the transport of ions from the source solution to the neutral solution by the application of a potential difference across the concrete disc. This means that, in order to determine the chloride transport resistance of concrete cover, cores should be extracted from the structure and tested in laboratories. In an attempt to facilitate testing of the concrete cover on site, an in situ ion migration test (hereafter referred to as PERMIT ion migration test for the unique identification of the new test) was developed. The PERMIT ion migration test was validated in the lab by carrying out a comparative investigation and correlating the results with the migration coefficient from the one-dimensional chloride migration test, the effective diffusion coefficient from the normal diffusion test and the apparent diffusion coefficient determined from chloride profiles. A range of concrete mixes made with ordinary Portland cement was used for this purpose. In addition, the effects of preferential flow of ions close to the concrete surface and the proximity of reinforcement within the test area on the in situ migration coefficients were investigated. It was observed that the in situ migration index, found in one working day, correlated well with the chloride diffusion coefficients from other tests. The quality of the surface layer of the cover concrete and the location of the reinforcement within the test area were found to affect the flow of ions through the concrete during the test. Based on the data, a procedure to carry out the PERMIT ion migration test was standardised.

Applications of statistical models in proportioning medium strength self-compacting concrete

Self-compacting concrete (SCC) is generally designed with a relatively higher content of finer, which includes cement, and dosage of superplasticizer than the conventional concrete. The design of the current SCC leads to high compressive strength, which is already used in special applications, where the high cost of materials can be tolerated. Using SCC, which eliminates the need for vibration, leads to increased speed of casting and thus reduces labour requirement, energy consumption, construction time, and cost of equipment. In order to obtain and gain maximum benefit from SCC it has to be used for wider applications. The cost of materials will be decreased by reducing the cement content and using a minimum amount of admixtures. This paper reviews statistical models obtained from a factorial design which was carried out to determine the influence of four key parameters on filling ability, passing ability, segregation and compressive strength. These parameters are important for the successful development of medium strength self-compacting concrete (MS-SCC). The parameters considered in the study were the contents of cement and pulverised fuel ash (PFA), water-to-powder ratio (W/P), and dosage of superplasticizer (SP). The responses of the derived statistical models are slump flow, fluidity loss, rheological parameters, Orimet time, V-funnel time, L-box, JRing combined to Orimet, JRing combined to cone, fresh segregation, and compressive strength at 7, 28 and 90 days. The models are valid for mixes made with 0.38 to 0.72 W/P ratio, 60 to 216 kg/m3 of cement content, 183 to 317 kg/m3 of PFA and 0 to 1% of SP, by mass of powder. The utility of such models to optimize concrete mixes to achieve good balance between filling ability, passing ability, segregation, compressive strength, and cost is discussed. Examples highlighting the usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mix parameters on slump flow, loss of fluidity, flow resistance, segregation, JRing combined to Orimet, and compressive strength at 7 and 28 days. Cost analysis is carried out to show trade-offs between cost of materials and specified consistency levels and compressive strength at 7 and 28 days that can be used to identify economic mixes. The paper establishes the usefulness of the mathematical models as a tool to facilitate the test protocol required to optimise medium strength SCC.

A machine vision approach to the grading of crushed aggregate

The grading of crushed aggregate is carried out usually by sieving. We describe a new image-based approach to the automatic grading of such materials. The operational problem addressed is where the camera is located directly over a conveyor belt. Our approach characterizes the information content of each image, taking into account relative variation in the pixel data, and resolution scale. In feature space, we find very good class separation using a multidimensional linear classifier. The innovation in this work includes (i) introducing an effective image-based approach into this application area, and (ii) our supervised classification using wavelet entropy-based features.