Experimental Comparative and Numerical Predictive Stu-dies on Strength Evaluation of Cement Types: Effect of Specimen Shape and Type of Sand
- 1
- 2
Abstract
Quality of cement is evaluated via group of tests. The most important, and close to understanding, is the compressive strength test. Recently, Egyptian standards adopted the European standards EN-196 and EN-197 for specifying and evaluating quality of cements. This was motivated by the large European investments in the local production of cement. The current study represents a comparative investigation, experimental and numerical, of the effect of different parameters on evaluation of compressive strength. Main parameters are shape of specimens and type of sand used for producing tested mortars. Three sets of specimens were made for ten types of cements. First set were 70.6 mm cubes molded according to old standards using single sized sand. Second group were prisms molded from standard sand (CEN sand) according to the recent standards. Third group were prisms molded from local sand sieved and regenerated to simulate same grading of CEN sand. All specimens were cured according to relevant standards and tested at different ages (2,3,7,10 and 28 days). Results show that CEM-I Type of cement does not fulfill, in all of its grades, the strength requirements of Ordinary Portland cement OPC specified in old standards. Also, the use of simulated CEN sand from local source gives strengths lower than those obtained using standard certified CEN sand. A limited number of tests were made on concrete specimens from two most common CEM-I types to investigate effect on concrete strength and results were also reported. Numerical investigation of the effect of specimen shape and type of sand on evaluation of compressive strength of mortar specimens, presented in the current study, applies one of the artificial intelligence techniques to simulate and predict the strength behavior at different ages. The Artificial Neural Network (ANN) technique is introduced in the current study to simulate the strength behavior using the available experimental data and predict the strength value at any age in the range of the experiments or in the future. The results of the numerical study showed that the ANN method with less effort was very efficiently capable of simulating the effect of specimen shape and type of sand on the strength behavior of tested mortar with different cement types.
- A. M. Neville, “Properties of Concrete,” John Wiley & Sons, New York, 1997.
- S. H. Kosmatka, et al., “Design and Control of Concrete Mixtures,” 5th Edition, Cement Association of Canada, Ottawa, 2002.
- Egyptian Standards (ES) 372, “Standards of Ordinary and Rapid Hardening Portland Cements,” Egyptian General Authority for Standards, Cairo, 1991.
- Egyptian Standards (ES) 5476, “Standards of Ce-ments,” Egyptian General Authority for Standards, Cairo, 2007
- EN 197, “CEMENT: Part 1: Composition, Specifi-cations and Conformity Criteria for Common Cements,” 2004.
- Egyptian Code of Practice 203, “Basics of Design and regulations of Construction of Reinforced Concrete Struc-tures: Appendix III, Guide for Testing of Concrete Materials,” Egyptian Ministry of Housing, Egypt, 2001.
- K. M. Yosri, “Properties of New Cements Produced in Egypt as per ES 4756/2005,” HBRC Journal, Vol. 3, No. 3, 2007, pp. 23-33.
- D. Solomatine, and L. Toorres, “Neural Network Approximation of a Hydrodynamic Model in Optimizing Re-servoir Operation,” Proceedings of the 2nd International Con-ference on Hydroinformatics, Zurich, 1996, pp. 201-206.
- K. A. Kheireldin, “Neural Network Application for Modeling Hydraulic Characteristics of Severe Contraction,” Proceedings of the 3rd Internetional Conference, Hydroinfor-matics, Copenhagen, 24-26 August 1998, pp. 41-48.
- M. A. M. Abdeen, “Neural Network Model for predicting Flow Cha-racteristics in Irregular Open Channel,” Scientific Journal, Faculty of Engineering-Alexandria University, Vol. 40, No. 4, 2001, pp. 539-546.
- B. S. M. Allam, “Artificial Intelligence Based Predictions of Precautionary Measures for Building Adjacent to Tunnel Rout during Tunneling Process,” Ph.D. Thesis, Faculty of Engineering, Cairo University, Giza, 2005.
- H. 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.
- M. A. M. Abdeen, “Development of Artificial Neural Network Model for Simu-lating the Flow Behavior in Open Channel Infested by Sub-merged Aquatic Weeds,” Journal of Mechanical Science and Technology, KSME International Journal, Vol. 20, No. 10, 2006, pp. 1691- 1879.
- 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.