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CONTENTS
Volume 10, Number 6, December 2012
 

Abstract
This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising.

Key Words
neural networks; principal component analysis; correlation; prediction; concrete; additives.

Address
B. Boukhatem and S. Kenai: Geometrical Laboratory, Civil Engineering Department, University of Blida, Algeria; A.T. Hamou: Civil Engineering Department, University of Sherbrooke, Canada; Dj. Ziou: Department of Informatics, University of Sherbrooke, Canada; M. Ghrici: Civil Engineering Departments, University of Chlef, Algeria

Abstract
In the present paper the problem of monotonically compressed concrete columns is studied numerically, accounting for transverse steel reinforcement and concrete cracking. The positive confinement effect of the ties on the core concrete is modeled explicitly and studied in the case of distributed or concentrated vertical load. The main aim is to investigate the influence of transverse reinforcement steel characteristics on the column load carrying capacity and ductility, in order to provide an evaluation about some standards requirements about the class and ductility of steel to be used for ties. The obtained results show that the influence of transverse reinforcement steel class of ductility is negligible both on the column load carrying capacity and on its ductility. Also the dissipated energy is basically unchanged. In view of these evidences, some standards requirements about the steel class of ductility to be used for ties appear to be rather questionable.

Key Words
smeared cracking; tied concrete columns; confinement.

Address
C. Bosco and S. Invernizzi: Dipartimento di Ingegneria Strutturale e Geotecnica, Politecnico di Torino, Torino, Italy

Abstract
The purpose of this research is to propose a design technique of concrete mix proportions satisfying service life through genetic algorithm (GA) and neural network (NN). For this, thirty mix proportions and the related diffusion coefficients in high performance concrete are analyzed and fitness function for diffusion coefficient is obtained considering mix components like w/b (water to binder ratio), cement content, mineral admixture (slag, flay ash and silica fume) content, sand and coarse aggregate content. Through averaging the results of 10 times GA simulations, relative errors to the previous data decrease lower than 5.0% and the simulated mix proportions are verified with the experimental results. Assuming the durability design parameters, intended diffusion coefficient for intended service life is derived and mix proportions satisfying the service life are obtained. Among the mix proportions, the most optimized case which satisfies required concrete strength and the lowest cost is selected through GA algorithm. The proposed technique would be improved with the enhancement of comprehensive data set including wider the range of diffusion coefficients.

Key Words
concrete; durability; genetic algorithm; chloride diffusion; neural network.

Address
Seung-Jun Kwon: Department of Civil and Environmental Engineering, Hannam University, Daejeon, South Korea; Sang-Chel Kim: Department of Civil Engineering, Hanseo University, Seosan, South Korea

Abstract
Currently, the design of reinforced concrete deep beams with web openings is carried out using empirical or semi-empirical methods and hence their scope of application is limited. In particular, high strength concrete deep beams with various web opening configurations have been given little treatment. In view of this, a nonlinear layered finite element method (LFEM) for cracking and failure analysis of reinforced concrete structures is used to conduct a parametric study to investigate reinforced concrete deep beams various web opening behaviours. This paper initially presents comparisons of LFEM output with published test results to numerical techniques. The paper then focuses on a parametric study on the shear strengths of deep beams with varying web opening configurations such as opening sizes and locations. The results confirm that the current design methods are inadequate in predicting the maximum shear strength when web openings are present. A series of parametric study offers insight into the maximum shear strength of the deep beams being critically influenced by the size and location of web openings.

Key Words
layered finite element; high strength concrete; deep beam; web opening; parametric study.

Address
Jeung-Hwan Doh, Tae-Min Yoo, Dane Miller and Hong Guan: Griffith School of Engineering, Griffith University Gold Coast Campus, Queensland 4222, Australia; Sam Fragomeni: School of Engineering and Science, Victoria University, Melbourne, Australia

Abstract
This paper focuses on the application of Fuller\'s ideal gradation curve to theoretically design blended ratio of all solid materials of high performance concrete (HPC), with the aid of error function, and then to study the effect of rice husk ash (RHA) on the performance of HPC. The residual RHA, generated when burning rice husk pellets at temperatures varying from 600 to 800oC, was collected at steam boilers in Vietnam. The properties of fresh and hardened concrete are reviewed. It is possible to obtain the RHA concrete with comparable or better properties than those of the specimen without RHA with lower cement consumption. High flowing concrete designed by the proposed method was obtained without bleeding or segregation. The application of the proposed method for HPC can save over 50% of the consumption of cement and limit the use of water. Its strength efficiency of cement in HPC is 1.4-1.9 times higher than that of the traditional method. Local standards of durability were satisfied at the age of 91 days both by concrete resistivity and ultrasonic pulse velocity.

Key Words
fuller\'s ideal gradation curve; high performance concrete; rice husk ash; strength efficiency; durability

Address
Chao-Lung Hwang, Le Anh-Tuan Bui and Chun-Tsun Chen: Department of Construction Engineering, National Taiwan University of Science and Technology 43, Keelung Rd., Sec. 4, Taipei 106, Taiwan, R.O.C

Abstract
This paper presents the results of an investigation on the compressive strength and weight loss of mortars containing three types of fillers as cement replacements; Limestone Filler (LF), Silica Fume (SF) and Trass (TR), subjected to elevated temperatures including 400oC, 600oC, 800oC and 1000oC. Results indicate that addition of TR to blended cements, compared to SF addition, leads to higher compressive strength and lower weight loss at elevated temperatures. In order to model the influence of the different parameters on the compressive strength and the weight loss of specimens, artificial neural networks (ANNs) were adopted. Different diagrams were plotted based on the predictions of the most accurate networks to study the effects of temperature, different fillers and cement content on the target properties. In addition to the impressive RMSE and R2 values of the best networks, the data used as the input for the prediction plots were chosen within the range of the data introduced to the networks in the training phase. Therefore, the prediction plots could be considered reliable to perform the parametric study.

Key Words
blended cement mortars; elevated temperature; limestone; silica fume; trass; compressive strength; weight loss; artificial neural network.

Address
A.A. Ramezanianpour: Concrete Technology and Durability Research Center, Amirkabir University of Technology, Tehran, Iran; M.E. Kamel: Department of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran; A. Kazemian, E. Ghiasvand, H. Shokrani and N. Bakhshi: Department of Civil Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract
This paper presents a parametric study of the plastic hinge length of circular reinforced concrete columns using a three-dimensional finite element analysis method, and using the Taguchi robust design method to reduce computational cost. Parameters examined include the longitudinal reinforcing ratio, the shear span-to-depth ratio, the axial force ratio and the concrete compressive strength. The study considers longitudinal reinforcement with yield strengths of 414 MPa and 685 MPa, and proposes simplified formulas for the plastic hinge length of circular reinforced concrete columns, showing that increases in plastic hinge length correlate to increases in the axial load, longitudinal reinforcing and shear span-todepth ratios. As concrete strength increases, the plastic hinge length decreases for the 414 MPa case but increases for the 685 MPa case.

Key Words
reinforced concrete columns; plastic hinge length; Taguchi method; finite element analysis.

Address
Yu-Chen Ou, Raditya Andy Kurniawan, Dimas Pramudya Kurniawan
and Nguyen Dang Nguyen: Department of Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan


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