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<\/p>\r\n","references":"[1] G. Brabham, D.C., Crowdsourcing as a model for problem solving.\r\nConvergence: The International Journal of Research into New Media\r\nTechnologies, 2008. 14(1): p. 75.\r\n[2] Harris, C., X. Hong, and Q. Gan, Adaptive modelling, estimation, and\r\nfusion from data: a neurofuzzy approach2002: Springer Verlag.\r\n[3] Moselhi, O., T. Hegazy, and P. Fazio, Neural networks as tools in construction. Journal of Construction Engineering and Management,\r\n1991. 117(4): p. 606-625.\r\n[4] Moselhi, O., T. Hegazy, and P. Fazio, DBID: analogy-based DSS for\r\nbidding in construction. Journal of Construction Engineering and\r\nManagement, 1993. 119(3): p. 466-479.\r\n[5] 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.\r\n[6] Kumaraswamy, M. and S. Dissanayaka, Developing a decision support\r\nsystem for building project procurement. Building and Environment,\r\n2001. 36(3): p. 337-349.\r\n[7] Cheng, M.-Y. and C.-H. Ko, Object-Oriented Evolutionary Fuzzy\r\nNeural Inference System for Construction Management. Journal of Construction Engineering and Management, 2003. 129(4): p. 461-469.\r\n[8] Jain, L.C. and N.M. Martin, Fusion of Neural Networks, Fuzzy Sets,\r\nand Genetic Algorithms: Industrial Applications1998: CRC Press, Inc.\r\n354.\r\n[9] Holsapple, C.W. and A.B. Whinston, Decision support systems: theory\r\nand application1987, Berlin; New York: Springer-Verlag. x, 500.\r\n[10] Marakas, G., Decision Support Systems in the 21st Century. Second\r\ned2002: Prenhall. 610.\r\n[11] Beynon, M., S. Rasmequan, and S. Russ, A New Paradigm for\r\nComputer-Based Decision Support. Decision Support Systems, 2002.\r\n33(1): p. 127-142.\r\n[12] Bastias, A., Towards the Application of Learning Systems for Decision\r\nSupport in Construction Engineering and Management, in Civil,\r\nEnvironmental and Architectural Department 2006, University of\r\nColorado at Boulder. p. 312.\r\n[13] Hinton, G., S. Osindero, and Y. Teh, A fast learning algorithm for deep\r\nbelief nets. Neural Computation, 2006. 18(7): p. 1527-1554.\r\n[14] Dumitrescu, D., et al., Evolutionary computation. CRC Press\r\ninternational series on computational intelligence.2000, Boca Raton,\r\nFL: CRC Press. 386.\r\n[15] Eiben, A.E. and J.E. Smith, Introduction to evolutionary computing.\r\nNatural computing series.2003, Berlin; New York: Springer. xv, 299.\r\n[16] H\u00fcllermeier, E., I. Renners, and A. Grauel, An evolutionary approach\r\nto constraint-regularized learning. Mathware & soft computing, 2004.\r\n11(2-3): p. 109-124.\r\n[17] Haidar, A., et al., Genetic Algorithms Application and Testing for\r\nEquipment Selection. Journal of Construction Engineering and\r\nManagement, 1999. 125(1): p. 32-38.\r\n[18] Bastias, A. and K.R. Molenaar, towards the application of learning\r\nsystems for decision support in construction engineering and\r\nmanagement, 2006. p. 312 p.\r\n[19] Taylor, J. and P. Bernstein, Paradigm Trajectories of Building\r\nInformation Modeling Practice in Project Networks. Journal of\r\nManagement in Engineering, 2009. 25: p. 69-76.\r\n[20] Leimeister, J., et al., Leveraging crowdsourcing: activation-supporting\r\ncomponents for IT-based ideas competition. Journal of Management\r\nInformation Systems, 2009. 26(1): p. 197-224.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 71, 2012"}