건설관리 (Construction Engineering and Management)

한국건설관리학회 (Korea Institute of Construction Engineering and Management)
권호 표지

건설산업 내 기계학습 알고리즘 (Machine Learning Algorithm) 활용 동향

Machine Learning Algorithms For Construction Industry

  • 발행 : 2017.06.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

키워드

참고문헌

  1. Asadi, A., Alsubaey, M., and Makatsoris, C. (2015). "A machine learning approach for predicting delays in construction logistics." International Journal of Advanced Logistics, 4(2), 115-130. https://doi.org/10.1080/2287108X.2015.1059920
  2. Catbas, F. N., and Malekzadeh, M. (2016). "A machine learning-based algorithm for processing massive data collected from the mechanical components of movable bridges." Automation in Construction, 72, 269-278. https://doi.org/10.1016/j.autcon.2016.02.008
  3. Cheng, M. Y., Wu, Y. W., and Wu, C. F. (2010). "Project success prediction using an evolutionary support vector machine inference model." Automation in Construction, 19(3), 302-307. https://doi.org/10.1016/j.autcon.2009.12.003
  4. Elazouni, A. M. (2006). "Classifying construction contractors using unsupervised-learning neural networks." Journal of Construction Engineering and Management, 132(12), 1242-1253. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:12(1242)
  5. Huang, Q., Cox, R. F., Shaurette, M., and Wang, J. (2012). "Intelligent building hazard detection using wireless sensor network and machine learning techniques." In Computing in Civil Engineering, 485-492.
  6. Kargah-Ostadi, N. (2014). "Comparison of machine learning techniques for developing performance prediction models." In Computing in Civil and Building Engineering, 1222-1229.
  7. Lam, K. C., Palaneeswaran, E., and Yu, C. Y. (2009). "A support vector machine model for contractor prequalification." Automation in Construction, 18(3), 321-329. https://doi.org/10.1016/j.autcon.2008.09.007
  8. Naganathan, H., Chong, W. O., and Chen, X. (2016). "Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches." Automation in Construction, 72, 187-194. https://doi.org/10.1016/j.autcon.2016.08.002
  9. Skibniewski, M., Arciszewski, T., and Lueprasert, K. (1997). "Constructability analysis: machine learning approach." Journal of computing in civil engineering, 11(1), 8-16. https://doi.org/10.1061/(ASCE)0887-3801(1997)11:1(8)
  10. Son, H., Kim, C., and Kim, C. (2011). "Automated color model-based concrete detection in construction-site images by using machine learning algorithms." Journal of Computing in Civil Engineering, 26(3), 421-433. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000141
  11. Tsanas, A., and Xifara, A. (2012). "Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools." Energy and Buildings, 49, 560-567. https://doi.org/10.1016/j.enbuild.2012.03.003
  12. Tixier, A. J. P., Hallowell, M. R., Rajagopalan, B., and Bowman, D. (2016). "Application of machine learning to construction injury prediction." Automation in construction, 69, 102-114. https://doi.org/10.1016/j.autcon.2016.05.016
  13. Wauters, M., and Vanhoucke, M. (2016). "A comparative study of Artificial Intelligence methods for project duration forecasting." Expert Systems with Applications, 46, 249-261. https://doi.org/10.1016/j.eswa.2015.10.008
  14. Yang, J., Arif, O., Vela, P. A., Teizer, J., and Shi, Z. (2010). "Tracking multiple workers on construction sites using video cameras." Advanced Engineering Informatics, 24(4), 428-434. https://doi.org/10.1016/j.aei.2010.06.008