Automatic Classification of Public Investment Megaprojects in Colombia from a Technical, Organizational and Environmental approach

Hugo Gutierrez, Miguel Melgarejo


Context:   the TOE (Technical, Organizational, and Environmental) framework for the analysis of large scale projects is considered as the basis for the development of megaproject progress classification in accordance with the needs of the national planning agency in Colombia.

Method: Classification of a megaproject progress is supported in the selection of several features taken from the TOE. These feature set is used to configure a database from the projects registered in the project-surveillance platform of the national planning agency in Colombia. The database is used to train two classification models. Information about 3200 projects from 2008 to 2012 was used, covering four economic sectors (Environment and sustainable development, Energy and mining, Health and social care and transportation). Debugging of the database was carried out by an analytic and quantitative approach. Model training and validation were computed with 70% and 30% of data respectively.  

Results: obtained models have similar performances beyond 70% in precision and agree in relevant input features.

Conclusions: this work is a starting point to develop an automatic tool that can be used by the national planning agency of Colombia in the a-priori evaluation of delays in public investment Megaprojects. 


megaprojects, complexity, management, neural networks, support vector machines


T. Williams, “How Do Organizations Learn Lessons from Projects — And Do They?”. IEEE Transactions on Engineering Management, vol. 55, no. 2, pp. 248–266, 2008.

E. Maaninen-Olsson and T. Müllern, “A contextual understanding of projects—The importance of space and time”. Scandinavian Journal of Management, vol. 25, no. 3, pp. 327–339, 2009.

T. Williams, “Assessing and Moving on From the Dominant Project Management Discourse in the Light of Project Overruns”. IEEE Transactions on Engineering Management, vol. 52, no. 4, pp. 497–508, 2005.

L.E. Bohórquez, “La comprensión de las organizaciones empresariales y su ambiente como sistemas de complejidad creciente: rasgos e implicaciones”. Ingeniería, vol.21, no. 3, pp. 363-377, 2016.

M. Bosch-Rekveldt, Y. Jongkind, H. Mooi, H. Bakker, and A. Verbraeck, “Grasping Project Complexity in Large Engineering Projects: The TOE (Technical, Organizational and Environmental) Framework”. International Journal of Project Management, vol. 29, no. 6, pp. 728–739, 2011.

H. Jin, J. Zhao, and X. Chen, “The Application of Neuro-Fuzzy Decision Tree in Optimal Selection of Technological Innovation Projects”. Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), pp. 438–443, Jul. 2007.

C.-C. Huang, P.-Y. Chu, and Y.-H. Chiang, “A fuzzy AHP Application in Government-Sponsored R&D Project Selection”. The International Journal of Management Science, vol. 36, no. 6, pp. 1038–1052, 2008.

K. Khalili-Damghani, S. Sadi-Nezhad, F. H. Lotfi, and M. Tavana, “A Hybrid Fuzzy Rule-Based Multi-Criteria Framework for Sustainable Project Portfolio Selection”. Journal of Information Sciences, vol. 220, pp. 442–462, 2013.

N. R. Shankar, P. P. B. Rao, S. Siresha, and K. U. Madhuri, “Critical Path Method in a Project Network using Ant Colony Optimization”. International Journal of Computational Intelligence Research, vol. 7, no. 1, pp. 7–16, 2011.

Y. Wang, “Resource-Constrained Multi-Project Scheduling Based on Ant Colony Neural Network”. The 2010 International Conference on Apperceiving Computing and Intelligence Analysis Proceeding, pp. 179–182, 2010.

A. H. L. Chen and C.-C. Chyu, “A Memetic Algorithm for Maximizing Net Present Value in Resource-Constrained Project Scheduling Problem”. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 2396–2403, Jun. 2008

M.T.Musavi, K. H. Chan, D. M. Hummels, and K. Kalantri. “On the Generalization Ability of Neural Network Classifiers”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No. 6, pp 659-663, 1994.

M. Pal and G. Foody. “Feature Selection for Classification of Hyperspectral Data by SVM”. IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 5, pp. 2297-2307, 2010

Departamento Nacional de Planeación. 2013. [En línea]. Disponible en:

Seguimiento a Proyectos de Inversión (SPI). Departamento Nacional de Planeación. 2013. [En línea]. Disponible en:

F. Costantino, G. Gravio, F. Nonino. “Project Selection in Project Portfolio Management: An Artificial Neural Network Model Based on Critical Success Factors”. International Journal of Project Management, Vol. 33, No 8, pp 1744-1754,2015

D. Wolpert. “The Lack Of A Priori Distinctions Between Learning Algorithms”. Neural Computation, Vol. 8, No. 7, pp. 1341–1390,1996.

D. Wolpert and W.Macready. “No Free Lunch Theorems For Optimization”. IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, pp. 67–82, 1997.

R. Duda, P. Hart and D. Stork, Pattern Classification, John Wiley & Sons, 2001.

I. Steinwart and A. Christman, Support Vector Machines, Springer, 2008.

L. Haitao and Z. Xiaofu, “Introducing a New Method to Predict the Project Time Risk”. 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, no. 1, pp. 27–30, 2009

S. Petruvesa, V. Zileska and V. Zujo, “Predicting construction Project Duration with Support Vector Machine”. International Journal of research in Engineering and Technology, Vol 11, No. 2, pp. 12-24, 2013.

HC Yin and YS Chen”. A Novel Machine Learning Model For Risk Management”. Proceedings oft he first Asia-pacific conference on global business, economics, finance and social sciences, Singapore, August, pp. 1-15, 2014


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Facultad de Ingeniería

Universidad Distrital Francisco José de Caldas

ISSN 0121-750X   E-ISSN 2344-8393