International Concrete Abstracts Portal

Foam concrete is a highly cellularized cementitious material that undergoes extensive plastic deformation when loaded to failure. Under compression, the microstructure of low-density foam concrete gets progressively crushed at a steady stress stage, accompanied by substantial energy dissipation. Understanding foam concrete crushing behavior is of special importance for its engineering applications. However, the current studies are insufficient to define key attributes that are important for material characterization and design. This study focuses on low-density foam concrete ranging from 0.4 to 0.8 g/cm3 (25 to 50 lb/ft3), with the crushing behavior investigated using a penetration test and dynamic Young’s modulus determined using a resonant frequency test. Four distinct crushing phases—linear elastic, transitional, plateau, and final densification—are observed for the samples. Furthermore, the yield strength and plateau strength are identified to characterize the foam crushing behavior. Using the experimental inputs, the modulus-strength constitutive relationship is established for predicting the crushing behavior with fundamental material properties. The findings significantly facilitate subsequent foam concrete studies, as well as the engineering design of this material.

This paper presents the stress-strain behavior of structural synthetic fiber-reinforced cellular lightweight concrete (CLC) stack-bonded prisms under axial compression. Masonry compressive strength is typically obtained by testing stack-bonded prisms under compression normal to its bed joint. CLC prisms with cross-sectional dimensions of 200 x 150 mm (7.87 x 5.90 in.) with an overall height of 470 mm (1.54 ft) were cast with and without different dosages of synthetic fiber reinforcement. Polyolefin was used as a structural fiber reinforcement at different volume fractions (vf) of 0.22, 0.33, 0.44, and 0.55% with and without microfiber dosage of 0.02%. Experimental results indicate that the presence of fibers helps in the improvement of strength, stiffness, and ductility of CLC stackbonded prisms under compression. Test results also signify that the hybrid fiber reinforcement provides better crack bridging mechanism both at micro and macro levels when compared to only macrofibers. Simple analytical models were developed for stress-strain behavior of CLC blocks and stack-bonded CLC prisms based on the experimental results with and without fibers under compression.

Rice husk ash (RHA) has significant potential to be used as a supplementary cementing material (SCM). However, RHA contains a cellular, honeycomb-like morphology of amorphous silica and this morphology results in high water absorption. Due to this morphology, the use of RHA in concrete results in reduced workability and higher water demands. Reduced workability and higher water demands can be mitigated by using smaller RHA particles. These smaller particles can be obtained by mechanical grinding. However, this grinding requires significant energy. This paper presents a novel method to transform RHA morphology using a chemical transformation process; specifically, an alkali transformation method. Results indicate that the process can effectively reduce RHA particle size and eliminate the cellular and honeycomb-like morphology.

Results from an experimental investigation of the dynamic response of cellular concrete subjected to blast-pressure loading are presented. The cellular concrete has large entrained porosity in the form of uniformly distributed air cells in a matrix of hardened cement. Under quasi-static loading, once the applied stress exceeds the crushing strength of the cellular concrete, crushing and densification of material results in an upward concave stress-strain response. The shock-tube experimental test setup used for generating blast-pressure loading in a controlled manner is described. Experimental results from the cellular concrete subjected to blast-pressure loading with pressure amplitude greater than its crushing strength indicate that a compression stress wave, which produces compaction of the material due to collapse of the cellular structure, is produced in the material. As the compaction front propagates in the material, there is a continuous decrease in its amplitude. The impulse of the blast pressure wave is conserved. When a sufficient length of the cellular concrete is present, the applied blast pressure wave is completely attenuated to a rectangular stress pulse. The transmitted stress to a substrate from cellular concrete when an applied blast pressure wave is completely attenuated resembles a rectangular stress pulse of amplitude slightly higher than the crushing strength of the material with a duration predicted by the applied blast impulse.

Cellular concrete is a lightweight material consisting of portland cement paste or mortar with a homogeneous void or cell structure created by introducing air or gas in the form of small bubbles (usually 0.1 to 1.0 mm in diameter) during the mixing process. This material has traditionally been used in heat insulation and sound attenuation, nonload bearing walls, roof decks, and is gaining wider acceptance in tunneling and geotechnical applications. A major concern with the production of cellular concrete is achieving product consistency and predictability of performance. Producers of the material have generated extensive experimental data over the years, but the analysis of such data using traditional statistical tools has not produced reliable predictive models. This research investigates the use of artificial neural networks (ANN) to predict the performance of cellular concrete mixtures. The ANN method can capture complex interactions among input/output variables in a system without any prior knowledge of the nature of these interactions and without having to explicitly assume a model form. Indeed, such a model form is generated by the data points themselves. This paper describes the database assembled, the selection and training process of the ANN model, and its validation. Results show that production yield, foamed density, unfoamed density, and compressive strength of cellular concrete mixtures can be predicted much more accurately using the ANN method compared to existing parametric methods.