Evaluation of chloride penetration in high performance concrete using neural network algorithm and micro pore structure

2009 ◽  
Vol 39 (9) ◽  
pp. 814-824 ◽  
Author(s):  
Ha-Won Song ◽  
Seung-Jun Kwon
Keyword(s):  
Neural Network ◽  
Pore Structure ◽  
High Performance ◽  
High Performance Concrete ◽  
Chloride Penetration ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Evaluation Technique of Chloride Penetration Using Apparent Diffusion Coefficient and Neural Network Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yun-Yong Kim ◽  
Byung-Jae Lee ◽  
Seung-Jun Kwon
Keyword(s):  
Neural Network ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Chloride Penetration ◽  
Time Dependent ◽  
Network Algorithm ◽  
Analysis Technique ◽  
Apparent Diffusion ◽  
Neural Network Algorithm

Diffusion coefficient from chloride migration test is currently used; however this cannot provide a conventional solution like total chloride contents since it depicts only ion migration velocity in electrical field. This paper proposes a simple analysis technique for chloride behavior using apparent diffusion coefficient from neural network algorithm with time-dependent diffusion phenomena. For this work, thirty mix proportions of high performance concrete are prepared and their diffusion coefficients are obtained after long term-NaCl submerged test. Considering time-dependent diffusion coefficient based on Fick’s 2nd Law and NNA (neural network algorithm), analysis technique for chloride penetration is proposed. The applicability of the proposed technique is verified through the results from accelerated test, long term submerged test, and field investigation results.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Effect of Copper Slag as a Fine Aggregate on the Durability Characteristics of High-Performance Concrete (HPC) Containing Silica Fume

2020 ◽  
Vol 07 (02) ◽  
pp. 1-10
Author(s):  
Rizwan Ahmad Khan ◽  
Keyword(s):  
Silica Fume ◽  
High Performance ◽  
High Performance Concrete ◽  
Copper Slag ◽  
Chloride Penetration ◽  
Interfacial Transition Zone ◽  
Fine Aggregate ◽  
Fresh Properties ◽  
Fine Aggregates ◽  
Effect Of Copper

This paper investigates the fresh and durability properties of the high-performance concrete by replacing cement with 15% Silica fume and simultaneously replacing fine aggregates with 25%, 50%, 75% and 100% copper slag at w/b ratio of 0.23. Five mixes were analysed and compared with the standard concrete mix. Fresh properties show an increase in the slump with the increase in the quantity of copper slag to the mix. Sorptivity, chloride penetration, UPV and carbonation results were very encouraging at 50% copper slag replacement levels. Microstructure analysis of these mixes shows the emergence of C-S-H gel for nearly all mixes indicating densification of the interfacial transition zone of the concrete.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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