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Analysis of many civil engineering phenomena is a complex problem due to the participation of a large number of factors involved. Traditional methods usually suffer from a lack of physical understanding. Furthermore, the simplifying assumptions that are usually made in the development of the traditional methods may, in some cases, lead to very large errors. The purpose of this paper is to present a new method, based on evolutionary polynomial regression (EPR) for capturing nonlinear interaction between various parameters of civil engineering systems.

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‐squares method is used to find feasible structures and the appropriate constants for those structures.

Capabilities of the EPR methodology are illustrated by application to two complex practical civil engineering problems including evaluation of uplift capacity of suction caissons and shear strength of reinforced concrete deep beams. The results show that the proposed EPR model provides a significant improvement over the existing models. The EPR models generate a transparent and structured representation of the system. For design purposes, the EPR models, presented in this study, are simple to use and provide results that are more accurate than the existing methods.

In this paper, a new evolutionary data mining approach is presented for the analysis of complex civil engineering problems. The new approach overcomes the shortcomings of the traditional and artificial neural network‐based methods presented in the literature for the analysis of civil engineering systems. 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.

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.

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.

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.

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.