The Application of Learning Systems to Support Decision for Stakeholder and Infrastructures Managers Based On Crowdsourcing
Authors: Alfonso Bastías, Álvaro González
The actual grow of the infrastructure in develop country require sophisticate ways manage the operation and control the quality served. This research wants to concentrate in the operation of this infrastructure beyond the construction. The infrastructure-s operation involves an uncertain environment, where unexpected variables are present every day and everywhere. Decision makers need to make right decisions with right information/data analyzed most in real time. To adequately support their decisions and decrease any negative impact and collateral effect, they need to use computational tools called decision support systems (DSS), but now the main source of information came from common users thought an extensive crowdsourcing
Keywords: Crowdsourcing, Learning Systems, Decision Support Systems, Infrastructure, Construction.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078947Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1471
 G. Brabham, D.C., Crowdsourcing as a model for problem solving. Convergence: The International Journal of Research into New Media Technologies, 2008. 14(1): p. 75.
 Harris, C., X. Hong, and Q. Gan, Adaptive modelling, estimation, and fusion from data: a neurofuzzy approach2002: Springer Verlag.
 Moselhi, O., T. Hegazy, and P. Fazio, Neural networks as tools in construction. Journal of Construction Engineering and Management, 1991. 117(4): p. 606-625.
 Moselhi, O., T. Hegazy, and P. Fazio, DBID: analogy-based DSS for bidding in construction. Journal of Construction Engineering and Management, 1993. 119(3): p. 466-479.
 Chao, L.-C. and M.J. Skibniewski, Neural Network Method of Estimating Construction Technology Acceptability. Journal of Construction Engineering and Management, 1995. 121(1): p. 130-142.
 Kumaraswamy, M. and S. Dissanayaka, Developing a decision support system for building project procurement. Building and Environment, 2001. 36(3): p. 337-349.
 Cheng, M.-Y. and C.-H. Ko, Object-Oriented Evolutionary Fuzzy Neural Inference System for Construction Management. Journal of Construction Engineering and Management, 2003. 129(4): p. 461-469.
 Jain, L.C. and N.M. Martin, Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications1998: CRC Press, Inc. 354.
 Holsapple, C.W. and A.B. Whinston, Decision support systems: theory and application1987, Berlin; New York: Springer-Verlag. x, 500.
 Marakas, G., Decision Support Systems in the 21st Century. Second ed2002: Prenhall. 610.
 Beynon, M., S. Rasmequan, and S. Russ, A New Paradigm for Computer-Based Decision Support. Decision Support Systems, 2002. 33(1): p. 127-142.
 Bastias, A., Towards the Application of Learning Systems for Decision Support in Construction Engineering and Management, in Civil, Environmental and Architectural Department 2006, University of Colorado at Boulder. p. 312.
 Hinton, G., S. Osindero, and Y. Teh, A fast learning algorithm for deep belief nets. Neural Computation, 2006. 18(7): p. 1527-1554.
 Dumitrescu, D., et al., Evolutionary computation. CRC Press international series on computational intelligence.2000, Boca Raton, FL: CRC Press. 386.
 Eiben, A.E. and J.E. Smith, Introduction to evolutionary computing. Natural computing series.2003, Berlin; New York: Springer. xv, 299.
 Hüllermeier, E., I. Renners, and A. Grauel, An evolutionary approach to constraint-regularized learning. Mathware & soft computing, 2004. 11(2-3): p. 109-124.
 Haidar, A., et al., Genetic Algorithms Application and Testing for Equipment Selection. Journal of Construction Engineering and Management, 1999. 125(1): p. 32-38.
 Bastias, A. and K.R. Molenaar, towards the application of learning systems for decision support in construction engineering and management, 2006. p. 312 p.
 Taylor, J. and P. Bernstein, Paradigm Trajectories of Building Information Modeling Practice in Project Networks. Journal of Management in Engineering, 2009. 25: p. 69-76.
 Leimeister, J., et al., Leveraging crowdsourcing: activation-supporting components for IT-based ideas competition. Journal of Management Information Systems, 2009. 26(1): p. 197-224.