Techno Press


cac
 
CONTENTS
Volume 5, Number 1, February 2008
 

Abstract
In this study, the effects and the interactions of water content, SP-binder ratio, and water-binder ratio on the workability performance of concrete were investigated. The experiments were designed based on flatted simplex-centroid experiment design modified from standard simplex-centroid one. The data gotten from the design was used to build the concrete slump model using neural networks. Research reported in this paper shows that a small number of slump experiments can be performed and meaningful data obtained with the experiment design. Such data would be suitable for building slump model using neural networks. The trained network can be satisfactorily used for exploring the effects of the components and their interactions on the workability of concrete. It has found that a high water content and a high SP/b ratio is essential for high workability, but achieving this by increasing these parameters will not in itself guarantee high workability. The w/b played a very important role in producing workability and had rather profound effects; however, the medium value about 0.4 is the best w/b to reach high slump without too much effort on trying to find the appropriate water content and SP/b.

Key Words
fly ash; superplasticizer; water-binder ratio; slump; workability; artificial neural networks; design of experiments.

Address
Department of Information Management, Chung-Hua University, Hsin Chu, Taiwan 30067, R.O.C.

Abstract
Steel fiber reinforced concrete is increasingly used day by day in various structural applications. An extensive experimentation was carried out with w/cm ratio ranging from 0.25 to 0.40, and fiber content ranging from zero to1.5 percent by volume with an aspect ratio of 80 and silica fume replacement at 5%, 10% and 15%. The influence of steel fiber content in terms of fiber reinforcing index on the compressive strength of high-performance fiber reinforced concrete (HPFRC) with strength ranging from 45 85 MPa is presented. Based on the test results, equations are proposed using statistical methods to predict 28-day strength of HPFRC effecting the fiber addition in terms of fiber reinforcing index. A strength model proposed by modifying the mix design procedure, can utilize the optimum water content and efficiency factor of pozzolan. To examine the validity of the proposed strength model, the experimental results were compared with the values predicted by the model and the absolute variation obtained was within 5 percent.

Key Words
silica fume; crimped steel fibers; fiber reinforcing index; high-performance fiber reinforced concrete; compressive strength; modeling, prediction.

Address
Structural Engineering Division, Department of Civil Engineering, Anna University, Chennai-600025, India

Abstract
Structural health monitoring of existing infrastructure is currently an important field of research, where elaborate experimental programs and advanced analytical methods are used in identifying the current state of health of critical and important structures. The paper outlines two methods of system identification of beam-like reinforced concrete structures representing bridges, through static measure- ments, in a distributed damage scenario. The first one is similar to the stiffness method, re-cast and the second one to flexibility method. A least square error (LSE) based solution method is used for the estimation of flexural rigidities and damages of simply supported, cantilever and propped cantilever beam from the measured deformation values. The performance of both methods in the presence of measurement errors is demonstrated. An experiment on an un-symmetrically damaged simply supported reinforced concrete beam is used to validate the developed method. A method for damage prognosis is demonstrated using a generalized, indeterminate, propped cantilever beam.

Key Words
damage identification; beam-like structures; bridges; static measurements.

Address
N. Lakshmanan; Structural Engineering Research Centre, CSIR Campus, Taramani, Chennai 600113, India
B. K. Raghuprasad; Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, India
K. Muthumani, N. Gopalakrishnan and D. Basu; Structural Engineering Research Centre, CSIR Campus, Taramani, Chennai 600113, India

Abstract
Digital image processing algorithms for the analysis and characterization of grains and voids in cemented materials were developed using toolbox functions of a mathematical software package. Utilization of grayscale, color and watershed segmentation algorithms and their performances were demonstrated on artificially prepared self-compacting concrete (SCC) samples. It has been found that color segmentation was more advantageous over the gray scale segmentation for the detection of voids whereas the latter method provided satisfying results for the aggregate grains due to the sharp contrast between their colors and the cohesive matrix. The watershed segmentation method, on the other hand, appeared to be very efficient while separating touching objects in digital images.

Key Words
digital image processing algorithms; grain characteristics; segregation; void distribution; segmentation; watershed.

Address
Dokuz Eylul University, Department of Civil Engineering, Kaynaklar Yerleskesi, Buca-Izmir 35160, Turkey

Abstract
This paper presents a mathematical model for strength and porosity of mortars made with ternary blends of ordinary Portland cement (OPC), ground rice husk ash (RHA) and classified fly ash (FA). The mortar mixtures were made with Portland cement Type I containing 0-40% FA and RHA. FA and RHA with 1-3% by weight retained on a sieve No. 325 were used. Compressive strength and porosity of the blended cement mortar at the age of 7, 28 and 90 days were determined. The use of ternary blended cements of RHA and FA produced mixes with good strength and low porosity of mortar. A mathematical analysis and two

Key Words
fly ash; rice husk ash; compressive strength; porosity; mortar.

Address
Department of Civil Engineering, Faculty of Engineering, Graduate School, Khon Kaen University, Khon Kaen 40002, Thailand


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