• Proceedings of the Korean Institute of Building Construction Conference
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• 2006.11a
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• pp.133-137
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• 2006

Can examine cause that the construction industry is faced in crisis of skill exhaustion in two sides. One is the tribe of actuality production manpower by entry evasion and graying of young man class specially new manpower to the tribe of skill manpower. Another is qualitative decline of skill manpower. Generally, problem of the systematic knowledge tribe and the picking up tribe about new technology etc, exists because learn skill for shoulder beyond in spot. In cutting phenomenon of skill that is depended on personal relationships availability with skilled worker in hereafter, problem that worker who is old or enter through rain skilled worker does not learn all life skill is detected. These problems institute entry of young man class and necessity of systematic vocational education training specially new human strength strongly. This study does presentation of way to promote construction industry entry of young man class and improvement way of vocational education training system that can do to train these by function manpower by purpose.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.545-550
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• 2002

Concrete is one of the essential structural materials in the construction. But, concrete consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructor. Therefore, concrete mixes depend on experiences of experts. However, it is more and more difficult to determine concrete mixes design by empirical means because more ingredients like mineral and chemical admixtures are included. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network are used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength and slump are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1570-1572
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• 2005

Is changed from ground signal way to Onboard signal way serving speed elevation of railroad worldwide. Therefore, importance of Onboard signal equipment is rising. Onboard signal equipment value of first time construction important. Domestic railroad and city railroad are built without standard current. Therefore, much expense is paid in maintenance and alternate of equipment. Therefore, establish standard for devices of childhood. Therefore, can have curtailment of operational expenses and specialization of skill human strength.

• Journal of the Korean Society of Hazard Mitigation
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• v.3 no.4 s.11
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• pp.119-129
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• 2003

Concrete and steel are essential structural materials in the construction. But, concrete, different from steel, consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructors. Concrete have two kinds of properties, immediately knowing properties such as slump, air contents and time dependent one like strength. Therefore, concrete mixes depend on experiences of experts. However, at point of time using High Performance Concrete, new method is wanted because of more ingredients like mineral and chemical admixtures and lack of data. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network ate used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength, slump, and air contents are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.