Fernando Martínez-Abella

Optimization of existing equations using a new Genetic Programming algorithm

A method based on Genetic Programming (GP) to improve previously known empirical equations is presented. From a set of experimental data, the GP may improve the adjustment of such formulas through the symbolic regression technique. Through a set of restrictions, and the indication of the terms of the expression to be improved, GP creates new individuals. The methodology allows us to study the need of including new variables in the expression. The proposed method is applied to the shear strength of concrete beams. The results show a marked improvement using this methodology in relation to the classic GP and international code procedures.

Influence of recycled coarse aggregates characteristics on mechanical properties of structural concrete

This work studies with the influence of recycled coarse aggregates features on the mechanical properties of structural concrete. Recycled concrete is characterised by replacing different percentages of natural coarse aggregate with recycled coarse aggregate (0, 20, 50 and 100%) and two different water-to-cement ratios .50 and .65. The experimental program was carried out at the two laboratories: one at the University of A Coruña and another at the Università Politecnica delle Marche (Italy). For said purpose, two different recycled aggregates were used, with different water absorptions and compositions. Results obtained allowed the authors to establish different mechanical behaviour of recycled concrete (compressive strength, tensile splitting strength and modulus of elasticity) due to different replacement percentages and characteristics of the recycled aggregates.

Study of strength tests with computer vision techniques

Knowing the strain response of materials in strength tests is one of the main issues in construction and engineering fields. In these tests, information about displacements and strains is usually carried out using physical devices attached to the material. In this paper, the suitability of Computer Vision techniques to analyse strength tests without interfering with the assay is discussed and a new technique is proposed. This technique measures displacements and deformations from a video sequence of the assay. With this purpose a Block-Matching Optical Flow algorithm is integrated with a calibration process to extract the vectorfield from the displacement in the material. To evaluate the proposed technique, a synthetic image set and a real sequence from a strength tests were analysed.

Bond Behavior of Recycled Concrete: Analysis and Prediction of Bond Stress–Slip Curve

AbstractThis paper studies the bond stress–slip curve of recycled concretes and the influence of recycled coarse aggregate, which leads to the determination of bond behavior at serviceability. In o...

Genetic programming to improvement FIB model: bond and anchorage of reinforcing steel in structural concrete

Starting from the FIB database, this work is aimed to analyze the current equations which predict the main datum that can be provided by bond tests: the ultimate bar stress when the failure is reached. Furthermore, Genetic Programming (GP) techniques are also applied in order to enhance the expression of the FIB, which achieves the best adjustment so far, giving rise to the new Model Code 2010. The final result shown is a highly predictive equation. The results are compared with those included in the Model Code and it is showed the influence of the main variables on the phenomenon (concrete strength, yield strength of steel, concrete cover, transverse reinforcement and diameter of the bar).