An Experimental Research for Developing Prediction Program for Time to Corrosion Reinforcing Steel

2014 ◽  
Vol 1052 ◽  
pp. 335-345
Author(s):  
Do Gyeum Kim
Keyword(s):  
Prediction Model ◽  
Experimental Research ◽  
Concrete Structure ◽  
Field Investigation ◽  
Prediction System ◽  
Reinforcing Steel ◽  
Coastal Structure ◽  
Reinforcing Bar ◽  
Prediction Program ◽  
Diffusion Speed

This research has attempted to predict the level of corrosion of reinforcing bar depending on diffusion speed of chloride in concrete to develop prediction program for the time in which corrosion of reinforcing bar in concrete structure at coast occurs. Based on the results, diffusion algorithm of chloride has been formulated and corrosion prediction system has been developed by utilizing the prediction model for diffusion of chloride. The results from experiment and field investigation on coastal structure indicate that the developed program can predict diffusion speed of chloride relatively accurately, The majority of estimated values are coincide with experimental value apart from those of the surface regarding prediction on content of chloride according to different depth. Therefore, the newly developed program has been found to be useful for interpreting and predicting diffusion of chloride.

scholarly journals Health Care Disease Prediction and Medicine, Exercise and Diet Suggestion using CNN

2021 ◽  
Vol 9 (VI) ◽  
pp. 766-772
Author(s):  
Deepak Kunwar
Keyword(s):  
Health Care ◽  
Prediction Model ◽  
Medical History ◽  
Disease Prediction ◽  
Prediction System ◽  
Prediction Program ◽  
Severity Of Disease ◽  
History Of

The Disease prediction Program is based on a prediction model predicting user disease on the basis of the following indicators the user contributes as an input to the system.. The system analyzes the symptoms provided by the user as input and gives the probability of the disease as an output Disease Prediction is done by implementing the CNN Classifier. CNN Classifier calculates the probability of the disease. Along with disease prediction system also calculates severity of disease and as per severity of disease suggests medicine. Suggesting diet and appropriate exercise is another merit of proposed system. Prediction of disease involves current as well as medical history of user.

Research Progress on Reinforced Concrete Laminated Slab in China

2012 ◽  
Vol 174-177 ◽  
pp. 263-267
Author(s):  
Ming Li ◽  
Zhe Zhe Sun ◽  
Wei Jian Zhao ◽  
Yong Liu
Keyword(s):  
Reinforced Concrete ◽  
Experimental Research ◽  
Concrete Structure ◽  
Early Stage ◽  
Research Progress ◽  
Horizontal Load ◽  
Reinforced Concrete Structure ◽  
Research Results ◽  
Load Carrying ◽  
New Generation

The development of new generation prefabricated reinforced concrete structure is still at an early stage in China. Reinforced concrete laminated slab, as an important horizontal load carrying member, is paid much attention to in research. Based on the research results about it in China, the progress of which is summarized, including the form, characteristics and experimental research of sandwich laminated slab, anti-ribbed laminated slab, and hollow laminated slab etc. Finally, the further research is prospected.

scholarly journals Determination of Significant Features for Building an Efficient Heart Disease Prediction System

2019 ◽  
Vol 8 (2) ◽  
pp. 4499-4504
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Prediction System ◽  
Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

Chemical Deposition of Thin Layers on Reinforcing Steel

2015 ◽  
Vol 660 ◽  
pp. 213-218 ◽  
Author(s):  
Petru Lazăr ◽  
Costică Bejinariu ◽  
Andrei Victor Sandu ◽  
Alin Marian Cazac ◽  
Ioan Gabriel Sandu
Keyword(s):  
Corrosion Resistance ◽  
Reinforced Concrete ◽  
Experimental Research ◽  
Immersion Time ◽  
Chemical Deposition ◽  
Thin Layers ◽  
Phosphate Solution ◽  
Reinforcing Steel ◽  
Good Surface

This paper presents the results of experimental research on the influence of using microcrystalline phosphating process on the OB37 steel used in construction. For this purpose, the samples were immersed in phosphate solution and the structures were characterized using SEM microscope. The results show that the obtained structures, are influenced by pH and immersion time and show a good surface compactity and roughness, which means that the obtained layer on the OB37 reinforcement improves corrosion resistance and adhesion of these reinforcements in the reinforced concrete.

Estimating Breast Height Diameters from Stump Measurements in British Columbia

1982 ◽  
Vol 58 (3) ◽  
pp. 143-145 ◽  
Author(s):  
Julien P. Demaerschalk ◽  
Stephen A. Y. Omule
Keyword(s):  
British Columbia ◽  
Prediction Model ◽  
Tree Species ◽  
Prediction System ◽  
Tree Diameter ◽  
Local Volume ◽  
Breast Height ◽  
Central Canada ◽  
Volume Equation ◽  
Stump Diameter

A means of estimating tree diameter at breast height from stump measurements has many applications. In this paper, metric equations are derived for estimating diameters at breast height from measured stump heights for all commercial tree species in British Columbia by age class and biogeoclimatic zones. The model found best was the same one as used by Alemdag and Honer (1977) for eleven tree species from eastern and central Canada. This prediction system can be incorporated into any local volume equation to derive a tree volume prediction model based on stump diameter and stump height.

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