- author = "Alireza Ahangar-Asr and Asaad Faramarzi and Akbar A. Javadi and Orazio Giustolisi",
- title = "Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression",
- journal = "Engineering Computation",
- year = "2011",
- volume = "28",
- number = "4",
- pages = "492--507",
- keywords = "genetic algorithms, genetic programming, Mechanical \& Materials Engineering, Concretes, Mechanical behaviour of materials, Rubbers",
- ISSN = "0264-4401",
-
DOI = "
doi:10.1108/02644401111131902",
- publisher = "Emerald Group Publishing Limited",
-
abstract = "Using discarded tyre rubber as concrete aggregate is
an effective solution to the environmental problems
associated with disposal of this waste material.
However, adding rubber as aggregate in concrete mixture
changes, the mechanical properties of concrete,
depending mainly on the type and amount of rubber used.
An appropriate model is required to describe the
behaviour of rubber concrete in engineering
applications. The purpose of this paper is to show how
a new evolutionary data mining technique, evolutionary
polynomial regression (EPR), is used to predict the
mechanical properties of rubber
concrete.
Design/methodology/approach EPR is a data-driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. In this technique, a combination of the genetic algorithm and the least square method is used to find feasible structures and the appropriate constants for those structures.
Findings Data from 70 cases of experiments on rubber concrete are used for development and validation of the EPR models. Three models are developed relating compressive strength, splitting tensile strength, and elastic modulus to a number of physical parameters that are known to contribute to the mechanical behaviour of rubber concrete. The most outstanding characteristic of the proposed technique is that it provides a transparent, structured, and accurate representation of the behaviour of the material in the form of a polynomial function, giving insight to the user about the contributions of different parameters involved. The proposed model shows excellent agreement with experimental results, and provides an efficient method for estimation of mechanical properties of rubber concrete.
Originality/value In this paper, a new evolutionary data mining approach is presented for the analysis of mechanical behaviour of rubber concrete. The new approach overcomes the shortcomings of the traditional and artificial neural network-based methods presented in the literature for the analysis of slopes. EPR provides a viable tool to find a structured representation of the system, which allows the user to gain additional information on how the system performs.",
-
notes = "Research paper.
Computational Geomechanics Group, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK Civil and Environmental Engineering Department, Faculty of Engineering, Technical University of Bari, Taranto, Italy",
