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Since construction industry has a poor project management performance, especially in relation to achieving cost objectives (Al-Jibouri, 2003; Atkinson, 1999; Costa, Formoso, Kagioglou, & Alarcón, 2004; Roztocki & Weistroffer, 2005), construction companies have to be more accurate in predicting total cost of building, still during the tendering phase. Thus nowadays only 34% of projects make it on the time, within the budget and in accordance with the agreed scope (Standish-group, 2009; Vukomanović, Radujković, & Burcar Dunović, 2008). Because of this bad performance the construction has been proclaimed the worst, inefficient, plundering etc (Beatham, Anumba, Thorpe, & Hedges, 2004, 2005).
Neural networks (NN) are a form of artificial intelligence which tries to simulate behavior of human brain. The first model was published in 1943 by McCulloch and Pitts (McCulloch & Pitts, 1943), where the authors analyzed fundamental logic functions. Besides the founders; Hebb (1949), Lashley (1951), Minsky (1988) and many others served as the catalyst of change and as promoters of NNs. After initial fame, up to 1980s and the IT dawn, NN were developing in a slower pace, considering only smaller improvement (Kohonen, 1988; Rumelhart & McClelland, 1982; Werbos, 1994). Rapid development of IT stimulated further development of NN, as well.
Nowadays, NN can be also found in researches in area of construction management. E.g., Chua et al. (Chua, Loh, Kog, & Jaselskis, 1997) studied the influence of critical success factors on planned construction cost. Ling et al. (2004) defined a set of indicators that can predict project performance in Design & Build projects. They found strong correlation among 65 success factors and 11 criteria of project success. Odeh et al. (2002) studied a set of indicators in correlation with project time, in traditional, construction management procurement routes. Kog et al. (Kog, Chua, Loh, & Jaselskis, 1999) designed a model using NN in predicting the deviation [%] of project time. Iyer et al. (2005) and Iyer and Jha (2006) analyzed more than 1500 projects in India and found reliable set of factors for prediction of cost in traditional building projects. NN can be also found in many areas besides construction, e.g.: finance, ICT, medicine, transport etc, as well.
While competitive pressures are forcing construction contractors companies to produce numerous bids every day (Al-Jibouri, 2003; Iyer & Jha, 2005; Radujkovic, 1999), investors (sponsors) do not have any obligation to even consider them. This huge gap between realized and unrealized bids has unfortunately become a common practice, especially in pre-fabricated housing. This only indicates low ability of the companies in calculating the final cost. Therefore we sought to find a solution to this problem using NN approach.