• Title/Summary/Keyword: 양중부하산정

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A computation model for Resource-based Lifting loads of the lift-cars for super high-rise buildings (초고층 건축물 리프트카 양중계획수립을 위한 자원기반의 양중부하 산정 모형)

  • Han, Choong-Hee;Lee, Jun-Bok;Won, Seo-Kyung
    • Korean Journal of Construction Engineering and Management
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    • v.13 no.5
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    • pp.135-143
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    • 2012
  • Constructing super-tall buildings is significantly different from constructing general ones in every technological and managerial aspects. Especially lift-car operations planning and management is one of core parts among various management techniques required during the course of the whole construction process of the super-tall buildings because vertical movements of physical resources enormously affect the efficiency of the construction processes. However, discrepancy between lifting plans and actual lifting operations causes serious efficiency problems. As an effort to solve the problem, this research suggests an improved method of estimating resource-based lifting load. The computing model developed as a result of this research facilitates more accurate computation of the total operation time and the maximum lifting capacity of the lift-cars. Further, this research can be developed as a decision support system for the total lift-car operations management.

Models for Predicting Hoisting Times of Tower Crane in the High-rise Building Construction (고층건축공사 타워크레인 양중시간 예측모델)

  • Lee Jong-Ryou;Jeon Yong-Seok;Park Chan-Sik
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • 2004.11a
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    • pp.472-475
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    • 2004
  • The objective of this study is to develope reasonably accurate prediction models to assess hoisting times of tower cranes in the high-rise building construction. The efficient use of the tower crane is critical to achieving the Planned floor cycle time. This research describes the derivation of mathematical models to predict the hoisting times in using a tower crane. 28 factors such as nature of load, characteristics of tower cranes, hoisting movements, operation of cranes, weather conditions and so on is considered to influence hoisting times. In order to develop the predicting hoisting times Correctly, it is divided hoisting upward and downward. Then multiple regression models for predicting supply and return hoisting times have been built up separately.

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