- author = "A. H. Alavi and A. A. Heshmati and H. Salehzadeh and A. H. Gandomi and A. Askarinejad",
- title = "Soft Computing Based Approaches for High Performance Concrete",
- booktitle = "Proceedings of the Sixth International Conference on Engineering Computational Technology",
- year = "2008",
- editor = "M. Papadrakakis and B. H. V. Topping",
- volume = "89",
- series = "Civil-Comp Proceedings",
- pages = "Paper 86",
- address = "Athens",
- publisher_address = "Stirlingshire, UK",
- month = "2-5 " # sep,
- publisher = "Civil-Comp Press",
- keywords = "genetic algorithms, genetic programming, linear genetic programming, high performance concrete, multilayer perceptron, compressive strength, workability, mix design",
- isbn13 = "978-1905088263",
- ISSN = "1759-3433",
-
URL = "
http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3",
-
URL = "http://www.amazon.co.uk/Proceedings-International-Conference-Engineering-Computational/dp/1905088264",
-
DOI = "
doi:10.4203/ccp.89.86",
-
abstract = "High performance concrete (HPC) is a class of concrete
that provides superior performance than those of
conventional types. The enhanced performance
characteristics of HPC are generally achieved by the
addition of various cementitious materials and chemical
and mineral admixtures to conventional concrete mix
designs. These parameters considerably influence the
compressive strength and workability properties of HPC
mixes. An extensive understanding of the relation
between these parameters and properties of the
resulting matrix is required for developing a standard
mix design procedure for HPC mix.
To avoid testing several mix proportions to generate a successful mix and also simulating the behaviour of strength and workability improvement to an arbitrary degree of accuracy that often lead to savings in cost and time, it is idealistic to develop prediction models so that the performance characteristics of HPC mixes can be evaluated from the influencing parameters. Therefore, in this paper, linear genetic programming (LGP) is used for the first time in the literature to develop mathematical models to be able to predict the strength and slump flow of HPC mixes in terms of the variables responsible. Subsequently, the LGP based prediction results are compared with the results of proposed multilayer perceptron (MLP) in terms of prediction performance. Sand-cement ratio, coarse aggregate-cement ratio, water-cement ratio, percentage of silica fume and percentage of superplasticiser are used as the input variables to the models to predict the strength and slump flow of HPC mixes. A reliable database was obtained from the previously published literature in order to develop the models.
The results of the present study, based on the values of performance measures for the models, demonstrated that for the prediction of compressive strength the optimum MLP model outperforms both the best team and the best single solution that have been created by LGP. It can be seen that for the slump flow the best LGP team solution has produced better results followed by the LGP best single solution and the MLP model. It can be concluded that LGPs are able to reach a prediction performance very close to or even better than the MLP model and as promising candidates can be used for solving such complex prediction problems.",
-
notes = "A.H. Alavi1, A.A. Heshmati1, H. Salehzadeh1, A.H.
Gandomi2 and A. Askarinejad3
1College of Civil Engineering, Iran University of Science & Technology (IUST), Tehran, Iran 2College of Civil Engineering, Tafresh University, Iran 3Department of Civil, Environmental and Geomatic Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland",
