Sequential dependency structure matrix based framework for leveling of a tower crane lifting plan

Recent construction projects involving the building of extremely tall and large structures have increased the demand for various major equipment, including tower cranes (TCs). However, most lifting plans for TCs at construction sites are performed based on the experience and intuition of the site manager as opposed to a systematic process of rational work. This study presents a framework for scheduling TCs using sequential characteristics of a dependency structure matrix (DSM) to efficiently improve the lifting plan of TCs. In this research, a real world construction case study involving a TC in charge of three buildings was examined. The results of the case study indicated that the scheduling of TC using sequential DSM was useful in leveling the TC lifting plan in terms of ease of use, especially in the typical floor cycle lifting planning. Therefore, the TC lifting plan based on sequential DSM presents a more precise and systematic TC lifting plan.

[1]  Chi Ming Tam,et al.  Comparative study of artificial neural networks and multiple regression analysis for predicting hoisting times of tower cranes , 2001 .

[2]  Amr A. Oloufa,et al.  Using DSM for modeling information flow in construction design projects , 2004 .

[3]  Ghang Lee,et al.  A BIM- and sensor-based tower crane navigation system for blind lifts , 2012 .

[4]  S D Eppinger,et al.  Innovation at the speed of information. , 2001, Harvard business review.

[5]  Koshy Varghese,et al.  Project Scheduling using Dependency Structure Matrix , 2005 .

[6]  Anil Sawhney,et al.  Adaptive Probabilistic Neural Network-Based Crane Type Selection System , 2002 .

[7]  Tomas Blomquist,et al.  A new approach for project scheduling using fuzzy dependency structure matrix , 2012 .

[8]  Aviad Shapira,et al.  Cranes for Building Construction Projects , 2007 .

[9]  Chi Ming Tam,et al.  Optimization of tower crane and material supply locations in a high-rise building site by mixed-integer linear programming , 2011 .

[10]  C. M. Tam,et al.  Genetic Algorithm for Optimizing Supply Locations around Tower Crane , 2001 .

[11]  Dikai Liu,et al.  Nonlinear Models for Predicting Hoisting Times of Tower Cranes , 2002 .

[12]  Robert P. Smith,et al.  A model-based method for organizing tasks in product development , 1994 .

[13]  Arthur W. T. Leung,et al.  Models for assessing hoisting times of tower cranes , 1999 .

[14]  Mohamed Al-Hussein,et al.  Integrating 3D visualization and simulation for tower crane operations on construction sites , 2006 .

[15]  Gary David Holt,et al.  Location optimization for a group of tower cranes , 1999 .

[16]  Aviad Shapira,et al.  Feasibility of automated monitoring of lifting equipment in support of project control , 2005 .

[17]  Ali A. Yassine,et al.  Engineering design management: An information structure approach , 1999 .

[18]  Javier Irizarry,et al.  Optimizing location of tower cranes on construction sites through GIS and BIM integration , 2012, J. Inf. Technol. Constr..

[19]  Gwang-Hee Kim,et al.  Improving Tower Crane Productivity Using Wireless Technology , 2006, Comput. Aided Civ. Infrastructure Eng..

[20]  Mohamed Marzouk,et al.  Decision support for tower crane selection with building information models and genetic algorithms , 2016 .

[21]  C. M. Tam,et al.  GA-ANN model for optimizing the locations of tower crane and supply points for high-rise public housing construction , 2003 .

[22]  Koshy Varghese,et al.  Application of Dependency Structure Matrix for Activity Sequencing in Concurrent Engineering Projects , 2006 .

[23]  Ali Yassine,et al.  Complex Concurrent Engineering and the Design Structure Matrix Method , 2003, Concurr. Eng. Res. Appl..

[24]  D. V. Steward,et al.  The design structure system: A method for managing the design of complex systems , 1981, IEEE Transactions on Engineering Management.