Guidelines for applied machine learning in construction industry—A case of profit margins estimation

Akintoye, 1991, Profitability of uk construction contractors, Construct. Manage. Econ., 9, 311, 10.1080/01446199100000025 Bagies, 2006, Bid/no-bid decision modelling for construction projects, 511 N. Banaitiene, A. Banaitis, Risk management in construction projects, in: Risk Management-Current Issues and Challenges, IntechOpen, 2012. D. Bau, J.-Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, A. Torralba, Visualizing and understanding generative adversarial networks, arXiv preprint arXiv:1901.09887, 2019. Bilal, 2016, Big data in the construction industry: A review of present status, opportunities, and future trends, Adv. Eng. Informat., 30, 500, 10.1016/j.aei.2016.07.001 Bose, 2001, Business data mining—a machine learning perspective, Informat. Manage., 39, 211, 10.1016/S0378-7206(01)00091-X Chang, 2013, Improving project-profit prediction using a two-stage forecasting system, Comput. Ind. Eng., 66, 800, 10.1016/j.cie.2013.10.001 Christodoulou, 2010, Bid mark-up selection using artificial neural networks and an entropy metric, Eng. Construct. Archit. Manage., 17, 424, 10.1108/09699981011056600 Cox, 2017, Exploratory data analysis, 47 Dikmen, 2004, Neural network model to support international market entry decisions, J. Construct. Eng. Manage., 130, 59, 10.1061/(ASCE)0733-9364(2004)130:1(59) F. Doshi-Velez, B. Kim, Towards a rigorous science of interpretable machine learning, arXiv preprint arXiv:1702.08608, 2017. Dulaimi, 2002, The factors influencing bid mark-up decisions of large-and medium-size contractors in singapore, Construct. Manage. Econ., 20, 601, 10.1080/01446190210159890 Enshassi, 2010, Factors affecting the bid/no bid decision in the palestinian construction industry, J. Financ. Manage. Property Construct., 15, 118, 10.1108/13664381011063421 J. Friedman, T. Hastie, R. Tibshirani, The elements of statistical learning volume 1. Springer series in statistics New York, 2001. Gomber, 2018, On the fintech revolution: interpreting the forces of innovation, disruption, and transformation in financial services, J. Manage. Informat. Syst., 35, 220, 10.1080/07421222.2018.1440766 Hulten, 2018, Machine learning intelligence, 245 Jordan, 2015, Machine learning: Trends, perspectives, and prospects, Science, 349, 255, 10.1126/science.aaa8415 Kim, 2008, Improving project management performance of large contractors using benchmarking approach, Int. J. Project Manage., 26, 758, 10.1016/j.ijproman.2007.10.002 Leon, 2017, System dynamics approach for forecasting performance of construction projects, J. Manage. Eng., 34, 04017049, 10.1061/(ASCE)ME.1943-5479.0000575 Li, 1999, Combining rule-based expert systems and artificial neural networks for mark-up estimation, Construct. Manage. Econ., 17, 169, 10.1080/014461999371664 Li, 1999, Ann-based mark-up estimation system with self-explanatory capacities, J. Construct. Eng. Manage., 125, 185, 10.1061/(ASCE)0733-9364(1999)125:3(185) Lin, 2004, Bid/no-bid decision-making–a fuzzy linguistic approach, Int. J. Project Manage., 22, 585, 10.1016/j.ijproman.2004.01.005 M.-T. Luong, Q.V. Le, I. Sutskever, O. Vinyals, L. Kaiser, Multi-task sequence to sequence learning, arXiv preprint arXiv:1511.06114, 2015. J. Manyika, A future that works: Ai, automation, employment, and productivity, McKinsey Global Institute Research, Tech. Rep., 2017. Mendelson, 2019, Artificial intelligence in breast imaging: Potentials and limitations, Am. J. Roentgenol., 212, 293, 10.2214/AJR.18.20532 Mitchell, 1997, Does machine learning really work?, AI Magazine, 18, 11 Moselhi, 1993, Dbid: analogy-based dss for bidding in construction, J. Construct. Eng. Manage., 119, 466, 10.1061/(ASCE)0733-9364(1993)119:3(466) O. Moselhi, T. Hegazy, P. Fazio, Expert: an expert system for strategic bidding, in: Proceedings of Annual Conference of Canadian Society for Civil Engineering, Fredericton, NB, Canada, 1993. Mullainathan, 2017, Machine learning: an applied econometric approach, J. Econ. Perspect., 31, 87, 10.1257/jep.31.2.87 Ng, 2018 Petruseva, 2016, Predicting bidding price in construction using support vector machine, TEM J. C. Rudin, G.-Y. Vahn, The big data newsvendor: Practical insights from machine learning, 2014. Rui, 2017, Investigation into the performance of oil and gas projects, J. Natural Gas Sci. Eng., 38, 12, 10.1016/j.jngse.2016.11.049 Shapiro, 2017, Reform predictive policing, Nature News, 541, 458, 10.1038/541458a Shash, 1993, Factors considered in tendering decisions by top uk contractors, Construct. Manage. Econ., 11, 111, 10.1080/01446199300000004 Toshev, 2014, Deeppose: Human pose estimation via deep neural networks, 1653 Wanous, 2000, To bid or not to bid: a parametric solution, Construct. Manage. Econ., 18, 457, 10.1080/01446190050024879 Wanous, 2003, A neural network bid/no bid model: the case for contractors in syria, Construct. Manage. Econ., 21, 737, 10.1080/0144619032000093323 Witten, 2016