Prediction of hoisting time for tower cranes for public housing construction in Hong Kong

Abstract

Material transportation for high-rise building construction relies heavily on tower cranes. Hence the proper use of tower cranes is of paramount importance for high-rise residential building construction. In planning and monitoring crane usage in Hong Kong, a schedule is usually prepared to coordinate hoisting operations. Apart from that, little research in optimizing crane usage has been carried out previously, except on operation cycle times, e.g. concreting using a crane and skip and formwork erection. Allocation of time for the hoisting schedule is based on the planners' and operators' experience. The accuracy of the hoisting schedule for crane dominated construction works has significant effects on the materials supply and on concreting operations. Imbalance in the allocation of crane usage for subcontractors may lead to conflicts between trades and idling of workers due to a shortage of materials. Although planners understand that the load hoisting time is proportional to hoisting height and other factors, floor construction cycles usually, for simplicity, are assigned to be constant six day, eight day or ten day cycles without making the necessary compensation for the longer hoisting times for upper floors. The prediction of hoisting times is of great importance to planners to ensure the accuracy of the construction schedule for crane dominated construction. This paper describes the derivation of a mathematical model to predict the hoisting times for a tower crane for public housing construction. Work measurement is used to collect hoisting times data for analysis. Twelve factors considered to influence hoisting time are identified for the model. Multiple regression models are built for predicting supply hoisting times and return hoisting times. The effects of the variables on hoisting time are reviewed. Estimated hoisting times calculated from the models are compared with actual hoisting times, and a worked example illustrating the application of the models is presented.

Keywords:

• Tower Crane
• Multiple Regression Analysis
• Public Housing
• Hoisting Time