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CONTENTS
Volume 11, Number 3, March 2013
 

Abstract
To select a most desired mix proportion that meets required performances according to the quality of recycled aggregate, a large number of experimental works must be carried out. This paper proposed a new design method for the mix proportion of recycled aggregate concrete to reduce the number of trial mixes. Genetic algorithm is adapted for the method, which has been an optimization technique to solve the multicriteria problem through the simulated biological evolutionary process. Fitness functions for the required properties of concrete such as slump, density, strength, elastic modulus, carbonation resistance, price and carbon dioxide emission were developed based on statistical analysis on conventional data or adapted from various early studies. Then these fitness functions were applied in the genetic algorithm. As a result, several optimum mix proportions for recycled aggregate concrete that meets required performances were obtained.

Key Words
mix proportion design; recycled aggregate; recycled aggregate concrete; genetic algorithm

Address
W.J. Park: Sustainable Building Research Centre, Hanyang University, Ansan, Korea; T. Noguchi: Faculty of Engineering (Architecture), the University of Tokyo, Tokyo, Japan; H.S. Lee: School of Architecture, Hanyang University, Ansan, Korea

Abstract
A comprehensive simulation model for the transport process of fully coupled moisture and multispecies in non-saturated concrete structures is proposed. The governing equations of moisture and ion diffusion are formulated based on Fick's law and the Nernst-Planck equation, respectively. The governing equations are modified by explicitly including the coupling terms corresponding to the coupled mechanisms. The ionic interaction-induced electrostatic potential is described by electroneutrality condition. The model takes into account the two-way coupled effect of moisture diffusion and ion transport in concrete. The coupling parameters are evaluated based on the available experimental data and incorporated in the governing equations. Differing from previous researches, the material parameters related to moisture diffusion and ion transport in concrete are considered not to be constant numbers and characterized by the material models that account for the concrete mix design parameters and age of concrete. Then, the material models are included in the numerical analysis and the governing equations are solved by using finite element method. The numerical results obtained from the present model agree very well with available test data. Thus, the model can predict satisfactorily the ingress of deicing salts into non-saturated concrete.

Key Words
deicing salts; chloride; concrete; coupled effect; Nernst-Planck equation

Address
Nattapong Damrongwiriyanupap: University of Phayao, Thailand; Linyuan Li: University of New Hampshire, USA; Yunping Xi: University of Colorado at Boulder, USA

Abstract
Self-healing (SH) technology of cracking is becoming a promising solution to improve the durability of cement based composites. However, little formula are available in the literature on determining the size and dosage of the self-healing capsules. Supposed that SH capsules will be broken and activated when they met cracks, a theoretical solution is developed to calculate the appropriate length of SH capsules based on Buffon

Key Words
self-healing; geometrical probability; simulation verification

Address
Haifeng Yuan and Huisu Chen: Jiangsu Key Laboratory of Construction Materials, School of Materials Science and Engineering, Southeast University, Nanjing, 211189, China

Abstract
This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN

Key Words
deflection; deep beams; artificial neural network; high strength self compacting concrete; linear regression

Address
Mohammad Mohammadhassani: Department of Civil Engineering, University of Malaya, Malaysia; Hossein Nezamabadi-pour: Department of Electrical Engineering, Shahid Bahonar University of Kerman-Iran; Mohd Zamin Jumaat, Mohammed Jameel and Arul M S Arumugam: Department of Civil Engineering, University of Malaya, Malaysia

Abstract
The aim of the present paper is to show the application of optimization strategies for the cost of beams in reinforced concrete buildings and to propose pre-sizing parameters. In order for these goals to be met, an optimization software program was developed. The program combines the analysis of structures by the grid model, reinforced concrete sizing, and the simulated annealing optimization heuristic. Sizing is compliant with the NBR 6118 (2007) Brazilian standard, according to which flexural, shearing, torsion, and web reinforcements and serviceability limit states (deflection and crack width limitation) are checked. Besides the dimensions of the situations mentioned above, the influence the cost of each material (steel, concrete and formwork) has on the overall cost of structures was also determined.

Key Words
optimization; beams; reinforced concrete; grid model; simulated annealing

Address
Guilherme Fleith de Medeiros and Moacir Kripka: Engineering Graduate Program, University of Passo Fundo, Passo Fundo, Brazil


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