A Computer-Aided Approach to Pozzolanic Concrete Mix Design

doi:10.1155/2018/4398017

CNU IR > College of Sustainable Environment and Recreation > Dept. of Applied Geoinformatics > Periodical Articles > Item 310902800/32216

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/32216

Title:	A Computer-Aided Approach to Pozzolanic Concrete Mix Design
Authors:	Kao, Ching-Yun Shen, Chin-Hung Jan, Jing-Chi Hung, Shih-Lin
Contributors:	Chia Nan Univ Pharm & Sci, Dept Appl Geoinformat Natl Chiao Tung Univ, Dept Civil Engn Chien Hsin Univ Sci & Technol, Dept Comp Sci & Informat Engn
Keywords:	Artificial Neural-Networks Self-Compacting Concrete High-Strength Concrete Blast-Furnace Slag Fly-Ash Concrete Silica Fume Mechanical-Properties Compressive Strength Structural Damage Performance
Date:	2018
Issue Date:	2019-11-15 15:45:32 (UTC+8)
Publisher:	HINDAWI LTD
Abstract:	Pozzolanic concrete has superior properties, such as high strength and workability. The precise proportioning and modeling of the concrete mixture are important when considering its applications. There have been many efforts to develop computer-aided approaches for pozzolanic concrete mix design, such as artificial neural network- (ANN-) based approaches, but these approaches have proven to be somewhat difficult in practical engineering applications. This study develops a two-step computer-aided approach for pozzolanic concrete mix design. The first step is establishing a dataset of pozzolanic concrete mixture proportioning which conforms to American Concrete Institute code, consisting of experimental data collected from the literature as well as numerical data generated by computer program. In this step, ANNs are employed to establish the prediction models of compressive strength and the slump of the concrete. Sensitivity analysis of the ANN is used to evaluate the effect of inputs on the output of the ANN. The two ANN models are tested using data of experimental specimens made in laboratory for twelve different mixtures. The second step is classifying the dataset of pozzolanic concrete mixture proportioning. A classification method is utilized to categorize the dataset into 360 classes based on compressive strength, pozzolanic admixture replacement rate, and material cost. Thus, one can easily obtain mix solutions based on these factors. The results show that the proposed computer-aided approach is convenient for pozzolanic concrete mix design and practical for engineering applications.
???metadata.dc.relation.uri???:	http://dx.doi.org/10.1155/2018/4398017
Relation:	Journal of Healthcare Engineering, v.2018, ID 4398017
Appears in Collections:	[Dept. of Applied Geoinformatics] Periodical Articles

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