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This study presents the application of Evolutionary Polynomial Regression (EPR) as pattern recognition system to predicate the numerical results of nonlinear and computationally complex aquifer system threatened by seawater intrusion (SWI). The developed EPR models are also linked with the multi objective genetic algorithm to test the efficiency of different arrangements of hydraulic barriers considered to control SWI. For this purpose the developed EPR model for each control scenario are trained and tested on the set of different pumping patterns as inputs and the corresponding set of numerically calculated outputs. The results are compared with those obtained by direct linking of the numerical simulation model with the optimization tool. These two strategies of the simulation-optimization (S/O) show excellent agreement on the obtained set of optimal solutions. The three combined management scenarios have been considered which involve the effects of both abstraction and recharge barriers simultaneously. Minimization of both the economic cost of management process and the salinity level in the aquifer are the two objective functions used for evaluating the cost efficiency of each management scenario studied. By considering the effects of the unsaturated zone, the subsurface pond is used to collect the water and to artificially recharge the aquifer in these scenarios. The main distinguish feature of EPR emerges in its application as metamodel in S/O process where it reduces the overall computational complexity significantly. The results also suggested that the application of cheap source of water such as the TWW and/or storm water instead of desalinated water coupled with continues abstraction water followed by its application for human consumption or irrigation after desalination as most cost effective method to control SWI. The effects of supplying these different external sources of recharge water and also the effects of different recovery ratios of desalination plant on the optimal results are also presented through sensitivity studies by using the developed methodology.

Purpose -Analysis of stability of slopes has been the subject of many research works in the past decades. Prediction of stability of slopes is of great importance in many civil engineering structures including earth dams, retaining walls and trenches. There are several parameters that contribute to the stability of slopes. This paper aims to present a new approach, based on evolutionary polynomial regression (EPR), for analysis of stability of soil and rock slopes. 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 -EPR models are developed and validated using results from sets of field data on the stability status of soil and rock slopes. The developed models are used to predict the factor of safety of slopes against failure for conditions not used in the model building process. The results show that the proposed approach is very effective and robust in modelling the behaviour of slopes and provides a unified approach to analysis of slope stability problems. It is also shown that the models can predict various aspects of behaviour of slopes correctly. Originality/value -In this paper a new evolutionary data mining approach is presented for the analysis of stability of soil and rock slopes. 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.

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Purpose -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.