The search for a design that meets both performance and safety, with minimal cost and lesser environmental impact was always the goal of structural engineers. In general, the design of conventional reinforced concrete structures is an iterative process based on rules of thumb established from the personal experience and intuition of the designer. However, such procedure makes the design process exhaustive and only occasionally leads to the best solution. In such context, this work presents the development and implementation of a mathematical formulation for obtaining optimal sections of reinforced concrete columns subjected to uniaxial flexural compression, based on the verification of strength proposed by the Brazilian standard NBR 6118 (ABNT 2007). To minimize the cost of the reinforced concrete columns, the Simulated Annealing optimization method was used, in which the amount and diameters of the reinforcement bars and the dimensions of the columns cross sections were considered as discrete variables. The results obtained were compared to those obtained from the conventional design procedure and other optimization methods, in an attempt to verify the influence of resistance class, variations in the magnitudes of bending moment and axial force, and material costs on the optimal design of reinforced concrete columns subjected to uniaxial flexural compression.

Numerical studies are performed to predict the stress-strain behavior of rectangular RC columns confined by multi-spiral hoops under axial and eccentric compressions. Using the commercial finite element package ABAQUS, the Drucker-Prager criterion and the yield surface are adopted for damaged plasticity concrete. The proposed finite element models are compared with the published experimental data. Parametric studies on concrete grades, confinement arrangement, diameter and spacing of hoops and eccentricity of load are followed. Numerical results have shown good agreements with experimental values, and indicated a proper constitutive law and model for concrete. Cross-sectional areas and spacing of the hoops have significant effect on the bearing capacity. It can be concluded that rectangular RC columns confined by multi-spiral hoops show better performance than the conventional ones.

The aim of this paper is to develop a procedure able to calculate the long-term stress and strain patterns in modern prestressed composite structures which are largely influenced by creep and shrinkage and whose final static configuration is the result of many phases of loading and restraints conditions. The introduction of equivalent moduli, depending on the viscous and elastic features of materials, can guarantee a significant simplification of the problem presented above. The proposed calculation model has been used to design the "Quattroquercie Viaduct" located on the highway "A3" Salerno-Reggio Calabria, Italy.

The objective of this investigation was to develop rules for automatic categorization of concrete quality using selected artificial intelligence methods based on machine learning. The range of tested materials included concrete containing a new waste material - solid residue from coal combustion in fluidized bed boilers (CFBC fly ash) used as additive. The rapid chloride permeability test - Nordtest Method BUILD 492 method was used for determining chloride ions penetration in concrete. Performed experimental tests on obtained chloride migration provided data for learning and testing of rules discovered by machine learning techniques. It has been found that machine learning is a tool which can be applied to determine concrete durability. The rules generated by computer programs AQ21 and WEKA using J48 algorithm provided means for adequate categorization of plain concrete and concrete modified with CFBC fly ash as materials of good and acceptable resistance to chloride penetration.

In this study, the modulus of elasticity of low, normal and high strength steel fiber reinforced concrete has been predicted by developing a fuzzy logic model. The fuzzy models were formed as simple rules using only linguistic variables. A fuzzy logic algorithm was devised for estimating the elastic modulus of SFRC from compressive strength. Fibers used in all of the mixes were made of steel, and they were in different volume fractions and aspect ratios. Fiber volume fractions of the concrete mixtures have changed between 0.25%-6%. The results of the proposed approach in this study were compared with the results of equations in standards and codes for elastic modulus of SFRC. Error estimation was also carried out for each approach. In the study, the lowest error deviation was obtained in proposed fuzzy logic approach. The fuzzy logic approach was rather useful to quickly and easily predict the elastic modulus of SFRC.