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2019 | OriginalPaper | Chapter

Machine Learning for Engineering Processes

Author : Christian Koch

Published in: Business Information Systems

Publisher: Springer International Publishing

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Abstract

Buildings are realized through engineering processes in projects, that however tend to result in cost and/or time overrun. Therefore, a need is highlighted by the industry and the literature, to develop predictive models, that can aid in decision-making and guidance, especially in a preparation effort before production is initiated.

This study aims at investigating what are possible applications of machine learning in building engineering projects and how they impact on their performance?

First, a literature review about machine learning (ML) is done. The first case is drawing on a productivity survey of building projects in Sweden (n = 580). The most influential factors behind project performance are identified, to predict performance. Features that are strongly correlated with four performance indicators are identified: cost variance, time variance and client- and contractor satisfaction and a regression analysis is done. Human related factors predict success best, such as the client role, the architect performance and collaboration. But external factors and technical aspects of a building are also important.

The second case combines constructability and risk analysis on a basis on civil engineering project from several different countries and with very different character; a town square, a biogas plant, road bridges and sub projects from an airport. The data encompasses 30 projects. The development build on literature study, expert interview, unsupervised and supervised learning. The strength lies more in the conceptual work of risk sources enabled by ML. Human reasoning is needed in building projects. Also after the introduction of ML.

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previous chapter Balancing Performance Measures in Classification Using Ensemble Learning Methods

Abramowicz, W., Paschke, A. (eds.): BIS 2018. LNBIP, vol. 320. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93931-5CrossRef

Kaplan, A., Haenlein, M.: Siri, Siri in my Hand, who’s the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence. Bus. Horiz. 62(1), 15–25 (2019)CrossRef

McCorduck, P.: Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, 2nd edn. A.K. Peters, Natick (2004)

Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)MathSciNetCrossRef

Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst. Appl. 97(1), 205–227 (2018)CrossRef

Liu, P.C.-Y., El-Gohary, N.: Automatic annotation of web images for domain-specific crack classification. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 553–560. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_66CrossRef

Jebelli, H., Khalili, M.M., Lee, S.H.: Mobile EEG-based workers’ stress recognition by applying deep neural network. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 173–180. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_21CrossRef

Amasyali, K., El-Gohary, N.: Predicting energy consumption of office buildings: a hybrid machine learning-based approach. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 695–700. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_83CrossRef

Petrova, E., Pauwels, P., Svidt, K., Jensen, R.L.: In search of sustainable design patterns: combining data mining and semantic data modelling on disparate building data. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 19–26. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_3CrossRef

10.

Siddula, M., Dai, F., Ye, Y., Fan, J.: Classifying construction site photos for roof detection: a machine-learning method towards automated measurement of safety performance on roof sites. Constr. Innov. 16(3), 368–389 (2016)CrossRef

11.

Wang, L., El-Gohary, N.M.: Machine-learning-based model for supporting energy performance benchmarking for office buildings. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 757–764. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_91CrossRef

12.

Sarkar, D., Bali, R., Sharma, T.: Practical Machine Learning with Python. Apress, New York (2013)

13.

Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatica 31(3), 249–268 (2007)MathSciNetMATH

14.

Habrich, T., Wagner, C., Hellingrath, B.: Qualitative assessment of machine learning techniques in the context of fault diagnostics. In: Abramowicz, W., Paschke, A. (eds.) BIS 2018. LNBIP, vol. 320, pp. 359–370. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93931-5_26CrossRef

15.

Le, T., Le, C., David Jeong, H., Gilbert, Stephen B., Chukharev-Hudilainen, E.: Requirement text detection from contract packages to support project definition determination. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 569–576. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_68CrossRef

16.

Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, Cambridge (2013)MATH

17.

Bryman, A., Bell, E.: Business Research Methods. Oxford University Press, Oxford (2011)

18.

Creswell, J.W., Clark, V.L.P.: Designing and Conducting Mixed Methods Research, 2nd edn. SAGE Publications, Los Angeles (2011)

19.

Mutis, I., Hartmann, T. (eds.): Advances in informatics and computing in civil and construction engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6CrossRef

20.

Frank, E., Hall, M.A., Witten, I.H.: WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann, Burlington (2016)

21.

Kulkarni, S.: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques. IGI Global, Hershey (2012)CrossRef

22.

Mackinsey, A.: The production of prediction: what does machine learning want. Eur. J. Cult. Stud. 18(4–5), 429–445 (2015)

23.

Min, K., Jung, M., Kim, J., Chi, S.: Sound event recognition-based classification model for automated emergency detection in indoor environment. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 529–535. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_63CrossRef

24.

Mutis, I., Ramachandran, A., Martinez, M.G.: The BIMbot: a cognitive assistant in the BIM room. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 155–163. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_19CrossRef

25.

Power, M.: Counting, control and calculation: reflections on measuring and management. Hum. Relat. 57(6), 765–783 (2004)CrossRef

26.

Koch, C., och Lundholm, M.: Produktivitetsläget i svenskt byggande 2014. Lokaler, Grupphus, och anläggning. Chalmers Tekniska Högskola, Prolog, SBUF. Göteborg (2018)

27.

Stilgoe, J.: Machine learning, social learning and the governance of self-driving cars. Soc. Stud. Sci. 48(1), 25–56 (2018)CrossRef

28.

Tixier, A.J.-P., Hallowell, M.R., Rajagopalan, B., Bowman, D.: Application of machine learning to construction injury prediction. Autom. Const. 69, 102–114 (2016)CrossRef

29.

Xu, X., Cai, H.: Semantic frame-based information extraction from utility regulatory documents to support compliance checking. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 223–230. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_27CrossRef

30.

Zhang, R., El-Gohary, N.: A machine learning approach for compliance checking-specific semantic role labeling of building code sentences. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 561–568. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_67CrossRef

Title: Machine Learning for Engineering Processes
Author: Christian Koch
Publisher: Springer International Publishing
Book: Business Information Systems
Print ISBN: 978-3-030-20481-5

Electronic ISBN: 978-3-030-20482-2

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-20482-2_26

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