Abstract
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.
Highlights
One of the main disadvantages of using concrete for construction is its high weight
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
Numerical results using Artificial Neural Network (ANN) technique are plotted with the experimental results for the first neural network simulation group versus 28-days compressive strength as shown in Figures 2, 4, 6, 7 and 9, respectively
Summary
One of the main disadvantages of using concrete for construction is its high weight. Recent applications of high rise buildings, long span buildings, and precast elements require reduction in weight to keep concrete a competent construction material. Its small size suggests its application as a replacement of fine aggregate It remains to investigate the structural properties of concrete made from these aggregates since they are not usually applied for structural purposes. It is quite clear from the literature mentioned previously the amount of experimental effort required to accurately investigate and understand the properties of light weight concrete This fact urged the need for utilizing new technology and techniques to facilitate this comprehensive effort and at the same time preserving high accuracy. Abdeen [13] utilized ANN technique for the development of various models to simulate the impacts of different submerged weeds’ densities, different flow discharges, and different distributaries operation scheduling on the water surface profile in an experimental main open channel that supplies water to different distributaries
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
