Work breakdown structure (WBS) development for underground construction

Abstract A work breakdown structure (WBS) can prove to be pivotal to successful project management planning. There are few published studies about the methodologies or tools to develop the appropriate WBS for a project, and those that are available are limited to the specific areas of construction such as apartment-building construction and boiler manufacturing. This research has an emphasis on developing a methodology with higher generalizability, which has the capability to be customized to complex underground projects. To address this issue, a new methodology that employs hierarchical neural networks to develop the WBS of complex underground projects is presented. This methodology has been applied to several tunnel case studies and it has been shown that for a real project, the model is able to generate the WBS and its activities that are comparable to those generated by a project planner. Consequently, it is concluded that these modeling methods have the capacity to significantly improve the WBSs for complex underground projects and improve key project tasks, such as workload planning, cost estimating and scheduling.

[1]  Iris D. Tommelein,et al.  Product modeling to support case-based construction planning and scheduling , 2004 .

[2]  J. J Reilly,et al.  The management process for complex underground and tunneling projects , 2000 .

[3]  Mostafa Sharifzadeh,et al.  Designing Work Breakdown Structure (WBS) In Tunneling Projects Using Neural Networks , 2011 .

[4]  S. Alireza Hashemi Golpayegani,et al.  Designing work breakdown structures using modular neural networks , 2007, Decis. Support Syst..

[5]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[6]  Tae Hwan Kim,et al.  Case- and Constraint-Based Project Planning for Apartment Construction , 1998, AI Mag..

[7]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition , 2006 .

[8]  Moonseo Park,et al.  Construction Planning Method Using Case-Based Reasoning (CONPLA-CBR) , 2007 .

[9]  Clark A. Mount-Campbell,et al.  Process optimization via neural network metamodeling , 2002 .

[10]  A. Roli.  Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[11]  Richard A. Wysk,et al.  Intelligent reasoning for processing planning , 1987 .

[12]  Daniel L. Thomas,et al.  What is an Expert System , 1988 .

[13]  Saeid R. Dindarloo,et al.  Maximum surface settlement based classification of shallow tunnels in soft ground , 2015 .

[14]  J. David Fuller,et al.  Back propagation in time‐series forecasting , 1995 .

[15]  Greg Indelicato.  Book Review: Work Breakdown Structures: The Foundation for Project Management Excellence , 2010 .

[16]  Birgit Wirtz.  Tunnelling Management By Design , 2016 .

[17]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[18]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[19]  Bimal K. Bose,et al.  Expert System, Fuzzy Logic, and Neural Networks in Power Electronics and Drives , 1997 .

[20]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[21]  Belle R. Upadhyaya,et al.  Application of Neural Networks for Sensor Validation and Plant Monitoring , 1990 .

[22]  Efraim Turban,et al.  Decision support systems and intelligent systems , 1997 .

[23]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[24]  John M. Evans,et al.  Automation in construction , 1986, Robotics.