ENG  Vol.2 No.6 , June 2010
Experimental Investigation and Development of Artificial Neural Network Model for the Properties of Locally Produced Light Weight Aggregate Concrete
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.

Cite this paper
nullM. 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.
References

[1]   V. J. Ramachandran, et al., “Concrete Science,” Heyden & Sons Ltd., London, 1981.

[2]   ACI 211-2-91, “Standard Practice for Selecting Proportions of Structural Light Weight Concrete,” American Concrete Institute, Michigan, 1991.

[3]   ACI 330-91, “Specifications for Lightweight Aggregate for Structural Concrete,” American Concrete Institute, Michigan, 1991.

[4]   ACI 213-03, “Guide for Structural Light Weight Concrete,” American Concrete Institute, Michigan, 2003.

[5]   A. M. Neville, “Properties of concrete,” John Wiley & Sons Ltd., London, 1997.

[6]   K. Ramanitharan and C. Li, “Forecasting Ocean Waves Using Neural Networks,” Proceeding of the Second International Conference on Hydroinformatics, Zurich, 1996.

[7]   M. Tawfik, A. Ibrahim and H. Fahmy, “Hysteresis Sensitive Neural Network for Modeling Rating Curves,” Jour- nal of Computing in Civil Engineering, ASCE, Vol. 11, No. 3, 1997, pp. 184-189.

[8]   M. A. M. Abdeen, “Neural Network Model for Predicting Flow Characteristics in Irregular Open Channel,” Scientific Journal, Faculty of Engineering-Alexandria University, Alexandria, Vol. 40, No. 4, 2001, pp. 539-546.

[9]   B. S. M. Allam, “Artificial Intelligence Based Predictions of Precautionary Measures for Building Adjacent to Tunnel Rout during Tunneling Process,” Ph. D. Dissertation, Faculty of Engineering, Cairo University, Giza, 2005.

[10]   H. Md. Azmathullah, M. C. Deo and P. B. Deolalikar, “Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket,” Journal of Hydrologic Engineering, ASCE, Vol. 131, No. 10, 2005, pp. 898-908.

[11]   M. A. M. Abdeen, “Development of Artificial Neural Network Model for Simulating the Flow Behavior in Open Channel Infested by Submerged Aquatic Weeds,” Journal of Mechanical Science and Technology, KSME International Journal, Soul, Vol. 20, No. 10, 2006, pp. 1527-1782.

[12]   M. A. M. Mohamed, “Selection of Optimum Lateral Load-Resisting System Using Artificial Neural Networks,” M. Sc. Thesis, Faculty of Engineering, Cairo University, Giza, 2006.

[13]   M. A. M. Abdeen, “Predicting the Impact of Vegetations in Open Channels with Different Distributaries’ Operations on Water Surface Profile using Artificial Neural Networks,” Journal of Mechanical Science and Technology, KSME International Journal, Soul, Korea, Vol. 22, No. 9, 2008, pp. 1830-1842.

[14]   ACI 544.2R-99, “Measurement of Properties of Fiber Reinforced Concrete,” American Concrete Institute, Michigan, 1999.

[15]   ASTM C0469-02E01, “Test Method for Static Modulus of Elasticity and Poisson’s Ratio of Concrete in Compression,” 2001.

[16]   V. M. Malhotra, “Testing Hardened Concrete: Nondestructive Methods,” ACI Monograph No. 9, American Concrete Institute, Michigan, 1986.

[17]   ACI 228.1 R89, “In-Place Methods for Determination of Strength of Concrete,” American Concrete Institute, Michigan, 1989.

[18]   ACI 437 R91, “Strength Evaluation of Existing Concrete Buildings,” American Concrete Institute, Michigan, 1991.

[19]   Y. Shin, “NeuralystTM User’s Guide, Neural Network Technology for Microsoft Excel,” Cheshire Engineering Corporation Publisher, California, 1994.

 
 
Top