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PurposeUsing 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/approachEPR 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.FindingsData 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/valueIn 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.

The geometry and material properties of additively manufactured (AM) parts are closely related in a way that any alteration in geometry of the part will change the underlying manufacturing strategy. This in turn, affects the microstructure and consequently, the mechanical behavior of material. This paper aims to evaluate the effect of the AM part’s thickness and geometry on microstructure, surface roughness, and mechanical properties under quasi-static and fatigue loading conditions by performing experimental tests. A series of Ti-6Al-4V specimens with three different thicknesses and two different geometries were fabricated using electron beam melting (EBM). The results of microstructural analyses revealed that specimens with lower build thickness experience finer grain size, higher microhardness, and lower elongation at failure. Although the microstructure of the produced parts was strongly affected by the build thickness, different surface to volume ratios eliminated the effect of microstructural differences and governed the fatigue properties of the parts. The size effect on the microstructural features, geometrical appearance, mechanical properties of the AM parts should be considered for the design and failure analysis of complex structures. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.