Author(s)

Mostafa A. M. Abdeen, Hossam Hodhod

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The developments in the field of construction raise the need for concrete with less weight. This is beneficial for different applications starting from the less load applied to foundations and soil till the reduction of carnage capacity required for lifting precast units. In this paper, the production of light weight concrete from light local weight aggregate is investigated. Three candidate materials are used: crushed fired brick, vermiculite and light exfoliated clay aggregate (LECA). The first is available as the by-product of brick industry and the later two types are produced locally for different applications. Nine concrete mixes were made with same proportions and different aggregate materials. Physical and mechanical properties were measured for concrete in fresh and hardened states. Among these measured ones are unit weight, slump, compressive and tensile strength, and impact resistance. Also, the performance under elevated temperature was measured. Results show that reduction of unit weight up to 45%, of traditional concrete, can be achieved with 50% reduction in compressive strength. This makes it possible to get structural light weight concrete with compressive strength of 130 kg/cm2. Light weight concrete proved also to be more impact and fire resistant. However, as expected, it needs separate calibration curves for non-destructive evaluation. Following this experimental effort, the Artificial Neural Network (ANN) technique was applied for simulating and predicting the physical and mechanical properties of light weight aggregate concrete in fresh and hardened states. The current paper introduced the (ANN) technique to investigate the effect of light local weight aggregate on the performance of the produced light weight concrete. The results of this study showed that the ANN method with less effort was very efficiently capable of simulating the effect of different aggregate materials on the performance of light weight concrete.

Light Weight Concrete, Locally Produced Aggregate, Ultrasonic Pulse Velocity, Modeling,

M. Abdeen and H. Hodhod, "Experimental Investigation and Development of Artificial Neural Network Model for the Properties of Locally Produced Light Weight Aggregate Concrete," Engineering, Vol. 2 No. 6, 2010, pp. 408-419. doi: 10.4236/eng.2010.26054.