Title :
Tezontle aggregate substitute optimization in building blocks mixture.
Author :
Acevedo-Dávila, J. ; Torres-Treviño, L.M. ; Z, Lauren Y Gómez
Author_Institution :
Corp. Mexicana de Investigation en Mater., Saltillo
Abstract :
The objective of the paper is to motivate the use of neural networks and genetic algorithms to optimize a concrete mixture where it is used the tezontle as raw material in the production of conventional blocks. The paper describes the results of an experimental study to determine the engineering properties of a concrete mix using tezontle in coarse and fine size as aggregate substitutes. In addition, a model is generated and used to optimize the concrete mixture. It is shown that a substitute of a specific proportion of tezontle provides the desirable mechanical properties.
Keywords :
cement industry; concrete; genetic algorithms; mechanical properties; neural nets; building blocks mixture; concrete mixture; conventional blocks production; engineering properties; genetic algorithms; mechanical properties; neural networks; raw material; tezontle aggregate substitute optimization; Aggregates; Building materials; Concrete; Construction industry; Costs; Evolutionary computation; Mechanical factors; Neural networks; Optimization methods; Robots;
Conference_Titel :
Electronics, Robotics and Automotive Mechanics Conference, 2007. CERMA 2007
Conference_Location :
Morelos
Print_ISBN :
978-0-7695-2974-5
DOI :
10.1109/CERMA.2007.4367704