چکیده مقاله
Appropriate concrete mix issue with respect to construction requirement workability and design necessities strength and durability is converted to a complicated, difficult and time consuming problem due to variation in the quality and quantity of concrete constituents and construction practices The extensive application of admixtures and additives in concrete to improve its properties, construction constraints and even the economical aspects of concrete mixes have made this problem more complicated Nowadays, neural networks applications extend in engineering field due to their advantages as a public solution in generation and classification In generation problem,neural network as a free model can approximate nonlinear and complex relation due to concrete mix design nature and trial and error process by learning real record relationship with no assumption The results of the study show the ability of neural networks for concrete mix proportioning Two types of neural networks ie multi layer perceptron MLP as a feed forward neural network and generalized regression neural network GRNN as a radial basis network which is used for generation and function approximation were applied, throughout this study An optimization model subject to construction and design requirements as constraints and material cost as the goal function proportioning concrete mix was designed In the other words, concrete mix design based on cost minimization is introduced Experimental records from 145 different concrete mixes of Tehran Communication Tower 420 m high tower under construction containing four different admixtures and silica fume were used as data input
کلیدواژهها
نویسندگان
شیوه ارجاع
Ali A. Ramezanianpour, and A. Davarpanah, ,1382,Evaluation of Neural Network Performance in Concrete Properties Estimation,6th International Conference on Civil Engineering,Isfahan