[ad_1]
Venkrbec, V. & Klanšek, U. In: Advances and Trends in Engineering Sciences and Technologies II 685–690 (CRC Press, 2016).
Damnjanovic, I. & Reinschmidt, K. Data Analytics for Engineering and Construction Project Risk Management (Springer, 2020).
Google Scholar
Singh, H. Project Management Analytics: A Data-driven Approach to Making Rational and Effective Project Decisions (FT Press, 2015).
Frame, J. D. & Chen, Y. Why Data Analytics in Project Management? (Auerbach Publications, 2018).
Google Scholar
Ong, S. & Uddin, S. Data Science and Artificial Intelligence in Project Management: The Past, Present and Future. J. Mod. Proj. Manag. 7, 26–33 (2020).
Bilal, M. et al. Investigating profitability performance of construction projects using big data: A project analytics approach. J. Build. Eng. 26, 100850 (2019).
Google Scholar
Radziszewska-Zielina, E. & Sroka, B. Planning repetitive construction projects considering technological constraints. Open Eng. 8, 500–505 (2018).
Google Scholar
Neely, A. D., Adams, C. & Kennerley, M. The Performance Prism: The Scorecard for Measuring and Managing Business Success (Prentice Hall Financial Times, 2002).
Kanakaris, N., Karacapilidis, N., Kournetas, G. & Lazanas, A. In: International Conference on Operations Research and Enterprise Systems. 135–155 Springer.
Jordan, M. I. & Mitchell, T. M. Machine learning: Trends, perspectives, and prospects. Science 349, 255–260 (2015).
Google Scholar
Shalev-Shwartz, S. & Ben-David, S. Understanding Machine Learning: From Theory to Algorithms (Cambridge University Press, 2014).
Google Scholar
Rahimian, F. P., Seyedzadeh, S., Oliver, S., Rodriguez, S. & Dawood, N. On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning. Autom. Constr. 110, 103012 (2020).
Google Scholar
Sanni-Anibire, M. O., Zin, R. M. & Olatunji, S. O. Machine learning model for delay risk assessment in tall building projects. Int. J. Constr. Manag. 22, 1–10 (2020).
Cong, J. et al. A machine learning-based iterative design approach to automate user satisfaction degree prediction in smart product-service system. Comput. Ind. Eng. 165, 107939 (2022).
Google Scholar
Li, F., Chen, C.-H., Lee, C.-H. & Feng, S. Artificial intelligence-enabled non-intrusive vigilance assessment approach to reducing traffic controller’s human errors. Knowl. Based Syst. 239, 108047 (2021).
Google Scholar
Mohri, M., Rostamizadeh, A. & Talwalkar, A. Foundations of Machine Learning (MIT press, 2018).
Google Scholar
Whyte, J., Stasis, A. & Lindkvist, C. Managing change in the delivery of complex projects: Configuration management, asset information and ‘big data’. Int. J. Proj. Manag. 34, 339–351 (2016).
Google Scholar
Zangeneh, P. & McCabe, B. Ontology-based knowledge representation for industrial megaprojects analytics using linked data and the semantic web. Adv. Eng. Inform. 46, 101164 (2020).
Google Scholar
Akinosho, T. D. et al. Deep learning in the construction industry: A review of present status and future innovations. J. Build. Eng. 32, 101827 (2020).
Google Scholar
Soman, R. K., Molina-Solana, M. & Whyte, J. K. Linked-Data based constraint-checking (LDCC) to support look-ahead planning in construction. Autom. Constr. 120, 103369 (2020).
Google Scholar
Soman, R. K. & Whyte, J. K. Codification challenges for data science in construction. J. Constr. Eng. Manag. 146, 04020072 (2020).
Google Scholar
Soman, R. K. & Molina-Solana, M. Automating look-ahead schedule generation for construction using linked-data based constraint checking and reinforcement learning. Autom. Constr. 134, 104069 (2022).
Google Scholar
Shi, F., Soman, R. K., Han, J. & Whyte, J. K. Addressing adjacency constraints in rectangular floor plans using Monte-Carlo tree search. Autom. Constr. 115, 103187 (2020).
Google Scholar
Chen, L. & Whyte, J. Understanding design change propagation in complex engineering systems using a digital twin and design structure matrix. Eng. Constr. Archit. Manag. (2021).
Allison, J. T. et al. Artificial intelligence and engineering design. J. Mech. Des. 144, 020301 (2022).
Google Scholar
Dutta, D. & Bose, I. Managing a big data project: The case of ramco cements limited. Int. J. Prod. Econ. 165, 293–306 (2015).
Google Scholar
Bilal, M. & Oyedele, L. O. Guidelines for applied machine learning in construction industry—A case of profit margins estimation. Adv. Eng. Inform. 43, 101013 (2020).
Google Scholar
Tayefeh Hashemi, S., Ebadati, O. M. & Kaur, H. Cost estimation and prediction in construction projects: A systematic review on machine learning techniques. SN Appl. Sci. 2, 1–27 (2020).
Google Scholar
Arage, S. S. & Dharwadkar, N. V. In: International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). 594–599 (IEEE, 2017).
Cheng, C.-H., Chang, J.-R. & Yeh, C.-A. Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost. Technol. Forecast. Soc. Chang. 73, 524–542 (2006).
Google Scholar
Joukar, A. & Nahmens, I. Volatility forecast of construction cost index using general autoregressive conditional heteroskedastic method. J. Constr. Eng. Manag. 142, 04015051 (2016).
Google Scholar
Xu, J.-W. & Moon, S. Stochastic forecast of construction cost index using a cointegrated vector autoregression model. J. Manag. Eng. 29, 10–18 (2013).
Google Scholar
Narbaev, T. & De Marco, A. Combination of growth model and earned schedule to forecast project cost at completion. J. Constr. Eng. Manag. 140, 04013038 (2014).
Google Scholar
Naeni, L. M., Shadrokh, S. & Salehipour, A. A fuzzy approach for the earned value management. Int. J. Proj. Manag. 29, 764–772 (2011).
Google Scholar
Ponz-Tienda, J. L., Pellicer, E. & Yepes, V. Complete fuzzy scheduling and fuzzy earned value management in construction projects. J. Zhejiang Univ. Sci. A 13, 56–68 (2012).
Google Scholar
Yu, F., Chen, X., Cory, C. A., Yang, Z. & Hu, Y. An active construction dynamic schedule management model: Using the fuzzy earned value management and BP neural network. KSCE J. Civ. Eng. 25, 2335–2349 (2021).
Google Scholar
Bonato, F. K., Albuquerque, A. A. & Paixão, M. A. S. An application of earned value management (EVM) with Monte Carlo simulation in engineering project management. Gest. Produção 26, e4641 (2019).
Google Scholar
Batselier, J. & Vanhoucke, M. Empirical evaluation of earned value management forecasting accuracy for time and cost. J. Constr. Eng. Manag. 141, 05015010 (2015).
Google Scholar
Yang, R. J. & Zou, P. X. Stakeholder-associated risks and their interactions in complex green building projects: A social network model. Build. Environ. 73, 208–222 (2014).
Google Scholar
Uddin, S. Social network analysis in project management–A case study of analysing stakeholder networks. J. Mod. Proj. Manag. 5, 106–113 (2017).
Ong, S. & Uddin, S. Co-evolution of project stakeholder networks. J. Mod. Proj. Manag. 8, 96–115 (2020).
Khanzode, K. C. A. & Sarode, R. D. Advantages and disadvantages of artificial intelligence and machine learning: A literature review. Int. J. Libr. Inf. Sci. (IJLIS) 9, 30–36 (2020).
Loyola-Gonzalez, O. Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view. IEEE Access 7, 154096–154113 (2019).
Google Scholar
Abioye, S. O. et al. Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. J. Build. Eng. 44, 103299 (2021).
Google Scholar
Doloi, H., Sawhney, A., Iyer, K. & Rentala, S. Analysing factors affecting delays in Indian construction projects. Int. J. Proj. Manag. 30, 479–489 (2012).
Google Scholar
Alkhaddar, R., Wooder, T., Sertyesilisik, B. & Tunstall, A. Deep learning approach’s effectiveness on sustainability improvement in the UK construction industry. Manag. Environ. Qual. Int. J. 23, 126–139 (2012).
Google Scholar
Gondia, A., Siam, A., El-Dakhakhni, W. & Nassar, A. H. Machine learning algorithms for construction projects delay risk prediction. J. Constr. Eng. Manag. 146, 04019085 (2020).
Google Scholar
Witten, I. H. & Frank, E. Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 2005).
Google Scholar
Kanakaris, N., Karacapilidis, N. I. & Lazanas, A. In: ICORES. 362–369.
Heo, S., Han, S., Shin, Y. & Na, S. Challenges of data refining process during the artificial intelligence development projects in the architecture engineering and construction industry. Appl. Sci. 11, 10919 (2021).
Google Scholar
Bross, I. D. How to use ridit analysis. Biometrics 14, 18–38 (1958).
Google Scholar
Uddin, S., Khan, A., Hossain, M. E. & Moni, M. A. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 19, 1–16 (2019).
Google Scholar
LaValle, S. M., Branicky, M. S. & Lindemann, S. R. On the relationship between classical grid search and probabilistic roadmaps. Int. J. Robot. Res. 23, 673–692 (2004).
Google Scholar
Abdi, H. & Williams, L. J. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433–459 (2010).
Google Scholar
Saxena, A. Survey on Road Construction Delay, https://www.kaggle.com/amansaxena/survey-on-road-construction-delay (2021).
Noble, W. S. What is a support vector machine?. Nat. Biotechnol. 24, 1565–1567 (2006).
Google Scholar
Hosmer, D. W. Jr., Lemeshow, S. & Sturdivant, R. X. Applied Logistic Regression Vol. 398 (John Wiley & Sons, 2013).
Google Scholar
LaValley, M. P. Logistic regression. Circulation 117, 2395–2399 (2008).
Google Scholar
Menard, S. Applied Logistic Regression Analysis Vol. 106 (Sage, 2002).
Google Scholar
Batista, G. E. & Monard, M. C. A study of K-nearest neighbour as an imputation method. His 87, 48 (2002).
Agatonovic-Kustrin, S. & Beresford, R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 22, 717–727 (2000).
Google Scholar
Zupan, J. Introduction to artificial neural network (ANN) methods: What they are and how to use them. Acta Chim. Slov. 41, 327–327 (1994).
Google Scholar
Hopfield, J. J. Artificial neural networks. IEEE Circuits Devices Mag. 4, 3–10 (1988).
Google Scholar
Zou, J., Han, Y. & So, S.-S. Overview of artificial neural networks. Artificial Neural Networks. 14–22 (2008).
Maind, S. B. & Wankar, P. Research paper on basic of artificial neural network. Int. J. Recent Innov. Trends Comput. Commun. 2, 96–100 (2014).
Wolpert, D. H. Stacked generalization. Neural Netw. 5, 241–259 (1992).
Google Scholar
Pavlyshenko, B. In: IEEE Second International Conference on Data Stream Mining & Processing (DSMP). 255–258 (IEEE).
Jović, A., Brkić, K. & Bogunović, N. In: 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). 1200–1205 (Ieee, 2015).
Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002).
Google Scholar
Pedregosa, F. et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar
Louppe, G., Wehenkel, L., Sutera, A. & Geurts, P. Understanding variable importances in forests of randomized trees. Adv. Neural. Inf. Process. Syst. 26, 431–439 (2013).
Al-Hazim, N., Salem, Z. A. & Ahmad, H. Delay and cost overrun in infrastructure projects in Jordan. Procedia Eng. 182, 18–24 (2017).
Google Scholar
Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).
Google Scholar
Shehu, Z., Endut, I. R. & Akintoye, A. Factors contributing to project time and hence cost overrun in the Malaysian construction industry. J. Financ. Manag. Prop. Constr. 19, 55–75 (2014).
Google Scholar
Akomah, B. B. & Jackson, E. N. Contractors’ perception of factors contributing to road project delay. Int. J. Constr. Eng. Manag. 5, 79–85 (2016).
GitHub: Where the world builds software, https://github.com/.
Anbari, F. T. Earned value project management method and extensions. Proj. Manag. J. 34, 12–23 (2003).
Google Scholar
Acebes, F., Pereda, M., Poza, D., Pajares, J. & Galán, J. M. Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques. Int. J. Proj. Manag. 33, 1597–1609 (2015).
Google Scholar
Japkowicz, N. & Stephen, S. The class imbalance problem: A systematic study. Intell. data anal. 6, 429–449 (2002).
Google Scholar
Chen, T. et al. Xgboost: extreme gradient boosting. R Packag. Version 0.4–2.1 1, 1–4 (2015).
Guarino, A., Lettieri, N., Malandrino, D., Zaccagnino, R. & Capo, C. Adam or Eve? Automatic users’ gender classification via gestures analysis on touch devices. Neural Comput. Appl. 1–23 (2022).
Zaccagnino, R., Capo, C., Guarino, A., Lettieri, N. & Malandrino, D. Techno-regulation and intelligent safeguards. Multimed. Tools Appl. 80, 15803–15824 (2021).
Google Scholar
