This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Arditi, D., & Tokdemir, O.B. (1999). Comparison of case-based reasoning and artificial neural networks. Journal of Computing in Civil Engineering, 13(3), pp. 162–169. doi: 10.1061/(asce)0887-3801(1999)13:3(162).ArditiD.TokdemirO.B.1999Comparison of case-based reasoning and artificial neural networks13316216910.1061/(asce)0887-3801(1999)13:3(162)Open DOISearch in Google Scholar
Attalla, M., & Hegazy, T. (2003). Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression. Journal of Construction Engineering and Management, 129(4), pp. 405–411. doi: 10.1061/(asce)0733-9364(2003)129:4(405).AttallaM.HegazyT.2003Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression129440541110.1061/(asce)0733-9364(2003)129:4(405)Open DOISearch in Google Scholar
Berkhahn, V., & Tilleke, S. (2008). Merging neural networks and topological models to re-engineer construction drawings. Advances in Engineering Software, 39(10), pp. 812–820.BerkhahnV.TillekeS.2008Merging neural networks and topological models to re-engineer construction drawings391081282010.1016/j.advengsoft.2007.05.006Search in Google Scholar
Goh, B. (1998). Forecasting residential construction demand in Singapore: a comparative study of the accuracy of time series, regression and artificial neural network techniques. Engineering Construction and Architectural Management (Wiley-Blackwell), 5(3), pp. 261–275 [Business Source Complete, EBSCOhost, viewed 16 June, 2017].GohB.1998Forecasting residential construction demand in Singapore: a comparative study of the accuracy of time series, regression and artificial neural network techniques53261275[Business Source Complete, EBSCOhost, viewed 16 June, 2017]10.1108/eb021080Search in Google Scholar
Kim, G.-H., An, S.-H., & Kang, K.-I. (2004). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment, 39(10), pp. 1235–1242. doi: 10.1016/j.buildenv.2004.02.013.KimG.-H.AnS.-H.KangK.-I.2004Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning39101235124210.1016/j.buildenv.2004.02.013Open DOISearch in Google Scholar
Petroutsatou, K., Georgopoulos, E., Lambropoulos, S., & Pantouvakis, J. P. (2012). Early cost estimating of road tunnel construction using neural networks. Journal of Construction Engineering and Management, 138(6), pp. 679–687. Available from: 10.1061/(ASCE)CO.1943-7862.0000479 on 16 June, 2017.PetroutsatouK.GeorgopoulosE.LambropoulosS.PantouvakisJ. P.2012Early cost estimating of road tunnel construction using neural networks1386679687Available from: 10.1061/(ASCE)CO.1943-7862.0000479 on 16 June, 201710.1061/(ASCE)CO.1943-7862.0000479Search in Google Scholar
Volná, E. (2008). Neuronovésítě 1. Available at http://www1.osu.cz/~volna/Neuronove_site_skripta.pdf on 21 November, 2016.VolnáE.2008Available at http://www1.osu.cz/~volna/Neuronove_site_skripta.pdf on 21 November, 2016.Search in Google Scholar
Xu, L., Zhang, T., & Ren, Q. (2015). Intelligent autofeedback and safety early-warning for underground cavern engineering during construction based on BP neural network and FEM. Mathematical Problems in Engineering [serial online], 2015(2015), pp. 1–8. Available at Academic Search Complete, Ipswich, MA. Accessed 16 June, 2017.XuL.ZhangT.RenQ.2015Intelligent autofeedback and safety early-warning for underground cavern engineering during construction based on BP neural network and FEM2015201518Available at Academic Search Complete, Ipswich, MA. Accessed 16 June, 2017.10.1155/2015/873823Search in Google Scholar