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 sp...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 展开更多
文摘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
