Bülent Baradan

Effectiveness of Alkyl Alkoxy Silane Treatment in Mitigating Alkali-Silica Reaction

The possibility of using an alkyl alkoxy silane (AAS) (iso-butyltriethoxysilane) in mitigating alkali-silica reaction (ASR) has been investigated. Cement mortars were prepared with reactive aggregates of basaltic and siliceous origin. AAS was applied to the surface of test specimens. The accelerated mortar bar method (ASTM C1260) was employed for the detection of ASR-related expansions. In addition to the length change, total water absorption (ASTM C642) of mortars and penetration depth of AAS were also measured. Three different curing procedures (short, medium, and long prewaiting periods) were applied to AAS-treated mortar bars before the ASTM C1260 tests. To understand the possible mechanism of prewaiting periods on water repellency of AAS-treated mortars, Fourier-transform infrared spectmscopy (FT-IR) analyses were also performed. Test results indicated that the success of AAS treatment mainly depends on a prewaiting period. AAS treatment is more effective if prewaiting periods are lengthened. In case of shorter precuring periods, mortar contains water, and in the presence of water, silane is very hydrolyzable. Once the silane hydrolyzes, it quickly bonds to substrate and this prevents its penetration to the deeper regions of mortar. In addition to the penetration problem, in the presence of water, the layer of silane molecules on the surface of mortars could not form strong Si-O and Si-O-Si bonds, and unbonded silane molecules detached or washed out. To effectively mitigate with ASR-related expansions, a minimum of 28 days air curing is mandatory. Longer precuring periods will permit the formation of strong oxygen bridging bonds between the silane molecules and mortar substrate, which provide a surface with stable hydrophobic character. In conclusion, it is shown that AAS treatment can be more effective when applied to older concrete structures.

Engineering Properties of Reactive Powder Concrete without Portland Cement

This paper reports the properties of a new type of high-performance composite material developed as an alternative construction material to reactive powder concrete (RPC) with a compressive strength over 200 MPa (29.0 ksi). This composite has been developed by the activation of slag and silica fume, without using portland cement. A comparative experimental study on the mechanical properties, microstructure, fresh-state properties, leaching of ions, bond to steel, and dimensional stability of RPC produced with portland cement (CRPC) and alkali-activated cement (ARPC) has been implemented. Test results show that ARPC has a very different microstructure with a great number of nano-sized pores compared to CRPC. Although these two RPC mixtures have similar compressive strength values, ARPC has significantly higher flexural performance, fracture energy, and bond to steel compared to CRPC. ARPC composites seem to be a good alternative to CRPC because of these superior properties and reduced environmental problems.

Neural network classification of aggregates by means of line laser based 3D acquisition

This study focuses on the development of a new module for a more accurate determination of geometrical properties of aggregates. A laser-based imaging system has been developed for the shape characterization of aggregates by using various digital image analysis techniques. By using this system it is possible to create a three-dimensional (3D) image form of aggregate particles. The system has been optimized to minimize the possible errors during image capturing and processing. The aggregates were classified according to their shape properties as; round, flat, elongated, angular, sphere, and irregular during test procedures. Geometrical properties of each aggregate group were analysed in 3D spatial domain. 3D shape reconstruction and characterization of the aggregates were realized by using digital image processing and analysis techniques based on the laser imaging system. MatLab® Image Processing Toolbox and Neural Network Toolbox were used to extract typical features of the aggregates and classify them according to their geometrical properties. Among the classifier types, multi-layered perceptron that has two hidden layers revealed the best performance (95.83%). The selection and production of appropriate shaped aggregate for various construction purposes seems to be possible by this developed method.