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RISS 인기검색어
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- 저자 : Ali Arefnia (검색결과 2 건)
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Ali Arefnia,Ali Dehghanbanadaki,Khairul Anuar Kassim 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.11
The study examined the use of recycled tire-derived aggregate (TDA) mixed with kaolin for retaining wall applications. The effects of the TDA content on the geotechnical properties of TDA-kaolin specimens such as the internal friction angle, maximum dry density (MDD), optimum moisture content (OMC) and saturated density (SD) were investigated. A total of 13 physical model tests were performed on a polymer concrete retaining wall using kaolin and TDA-kaolin mixtures as backfill material. Powdery, shredded, small-sized granular (1 − 4 mm) and large-sized granular (5 − 8 mm) TDA were mixed with kaolin at contents of 0%, 20%, 40%, and 60% by weight. The lateral wall displacement was measured on a fabricated steel strip footing close to the wall during loading. Lateral wall displacement also was simulated using finite element (FE) software (Optum G2). The results indicated that the addition of 60% TDA decreased the internal friction angle of the samples up to 8% compared to pure kaolin. In addition, the TDA particles caused the MDD of the TDA-kaolin samples to decrease up to 45%. The physical modeling results indicated that the kaolin samples mixed with 60% shredded TDA showed the highest elasticity in all tests at the failure moment of the footing.
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A Study on UCS of Stabilized Peat with Natural Filler: A Computational Estimation Approach
Ali Dehghanbanadaki,Mahdy Khari,Ali Arefnia,Kamarudin Ahmad,Shervin Motamedi 대한토목학회 2019 KSCE JOURNAL OF CIVIL ENGINEERING Vol.23 No.4
This study applied two feed-forward type computational methods to estimate the Unconfined Compression Strength (UCS) of stabilized peat soil with natural filler and cement. For this purpose, experimental data was obtained via testing of 271 samples at different natural filler and cement mixture dosages. The input parameters for the developed UCS (output) model were: 1) binder dosage, 2) coefficient of compressibility, 3) filler dosage, and 4) curing time. The model estimated the UCS through two types of feed-forward Artificial Neural Network (ANN) models that were trained with Particle Swarm Optimization (ANN-PSO) and Back Propagation (ANN-BP) learning algorithms. As a means to validate the precision of the model two performance indices i.e., coefficient of correlation (R2) and Mean Square Error (MSE) were examined. Sensitivity analyses was also performed to investigate the influence of each input parameters and their contribution on estimating the output. Overall, the results showed that MSE(PSO) < MSE(BP) while R2 (PSO) > R2 (BP); suggesting that the ANN-PSO model better estimates the UCS compared to ANN-BP. In addition, on the account of sensitivity analysis, it is found that the binder and filler content were the two most influential factors whilst curing period was the least effective factor in predicting UCS.
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