Korean Journal of Construction Engineering and Management (한국건설관리학회논문집)

Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Application of Artificial Neural Network Model for Environmental Load Estimation of Pre-Stressed Concrete Beam Bridge

PSC Beam교 환경부하량 추정을 위한 인공신경망 모델 적용 연구

  • Kim, Eu Wang (Department of Civil Engineering, Chung-Ang University) ;
  • Yun, Won Gun (Department of Civil Engineering, Chung-Ang University) ;
  • Kim, Kyong Ju (Department of Civil Engineering, Chung-Ang University)
  • Received : 2018.05.30
  • Accepted : 2018.06.27
  • Published : 2018.07.31
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Abstract

Considering that earlier stage of construction project has a great influence on the possibility of lowering of environmental load, it is important to build and utilize system that can support effective decision making at the initial stage of the project. In this study, we constructed an environmental load estimation model that can be used at the early stage of the project using basic design factors. The model was constructed by using the artificial neural network to estimate environmental load by applying to planning stage (ANN-1), basic design stage (ANN-2). The result of test, shows that average of absolute measuring efficiency and standard deviation of ANN-1 and ANN-2 were 11.19% / 5.30% and 9.59% / 3.09% each. This result indicates that the model using the input variables extended with the project progress has high reliability and it is considered to be effective in decision support at the initial design stage of the project.

건설사업 초기단계가 환경부하량의 절감 가능성에 큰 영향을 미친다는 것을 감안한다면, 효과적인 의사결정을 지원할 수 있는 시스템을 초기에 구축하고 활용하는 것이 중요하다. 이에 본 연구에서는 기본적인 설계 요인들을 고려하여 초기 설계단계에서 활용될 수 있는 환경부하량 산정 모델을 구축하였다. 이 모델은 인공신경망 기법을 활용하고 설계단계 업무가 진행됨에 따라 얻을 수 있는 가용정보 수준을 고려하여 기획단계 적용 모델(ANN-1)과 기본 설계단계 적용 모델(ANN-2)로 구분하여 환경부하량을 산정하도록 구축되었다. 모델의 실험결과 ANN-1, ANN-2모델의 절대평균오차율과 표준편차는 각각 11.19% / 5.30% 및 9.59% / 3.09%로 높은 신뢰성을 갖는 것으로 나타났다. 본 모델은 프로젝트 초기단계별 기초적인 가용정보만을 활용하여 신속하고 정확하게 환경부하량을 추정하고 합리적인 의사결정을 지원할 수 있을 것으로 기대된다.

Keywords

References

  1. Association for the Advancement of Cost Engineering (AACE) (2011). "Cost estimate classification systemas applied in engineering, procurement, and construction for the process industries." 2(18), R-97.
  2. Christensen, P., and Dysert, L. R. (2005). "Cost Estimate Classification System. as Applied in Engineering, Procurement, and Construction for the Process Industries." AACE International Recommended Practices.
  3. Crawford, R. H. (2011). "Life cycle assessment in the built environment." Spon Press.
  4. Elazouni, A. M., Ali, A. E., and Abdel-Razek, R. H. (2005). "Estimating the Acceptability of New Formwork Systems Using Neural Networks." Journal of Construction Engineering and Management, 131(1), pp. 33-41. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:1(33)
  5. Gunaydin, H. M.,, and Dogan, S. Z. (2004). "A neural network approach for early cost estimation of structural systems of buildings." International Journal of Project Management, 22, pp. 595-602. https://doi.org/10.1016/j.ijproman.2004.04.002
  6. Han, H., Kim, J. H., Yoon, J. H., and Seo, J. W. (2011). "Road Construction Cost Estimation Model in the Planning Phase Using Artificial Neural Network." Journal of the Korean Society of Civil Engineering, KSCE, 31(6), pp. 829-837.
  7. Huang, G. (2003). "Learning capability and storage capacity of two-hidden-layer feedforward networks." IEEE Trans. Neural Networks, 14(2), pp. 274-281. https://doi.org/10.1109/TNN.2003.809401
  8. ISO (2006). "Environmental Management- Life Cycle Assessment- Principles and Framework." ISO: Geneva, Switzerland.
  9. Jeong, K., Ji, C., Hong, T., and Park, H. S. (2015). "A model for predicting the environmental impacts of educational facilities in the project planning phase." Journal of cleaner production, 107, pp. 538-549. https://doi.org/10.1016/j.jclepro.2014.01.027
  10. Kang, M., and Park, H. (2013). "A Study of the Combination Method for Earthwork Equipments Using the Environmental Loads and Costs." Journal of the Korean Society of Civil Engineering, KSCE, 33(3), pp. 1215-1224. https://doi.org/10.12652/Ksce.2013.33.3.1215
  11. Kim, B. S. (2010). "The approximate cost estimating model for railway bridge project in the planning phase using CBR method." Journal of the Korean Society of Civil Engineering, KSCE,, 15(7), pp. 1149-1159.
  12. Kim, K. J., Yun, W. G., Cho, N., and Ha, J. (2017). "Life cycle assessment based environmental impact estimation model for pre-stressed concrete beam bridge in the early design phase." Environmental Impact Assessment Review, 64, pp. 47-56. https://doi.org/10.1016/j.eiar.2017.02.003
  13. Kim, M., Moon, H., and Kang, L. (2013). "Development of an Approximate Cost Estimating Model for Bridge Construction Project using CBR Method." Korean Journal of Construction Engineering and Management, KICEM, 14(3), pp. 42-52. https://doi.org/10.6106/kJCEM.2013.14.3.042
  14. Koo, C., Hong, T., and Hyun, C. (2011). "The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach." Expert Systems with Applications, 38(7), pp. 8597-8606. https://doi.org/10.1016/j.eswa.2011.01.063
  15. Lee, K., and Yang J. (2009). "A Study on the Functional Unit Estimation of Energy Consumption and Carbon Dioxide Emission in the Construction Materials." Journal of the Architectural Institute of Korea Planning & Design, 25(6), pp. 43-50.
  16. Lowe, D. J., Emsley, M. W., and Harding, A. (2006). "Predicting construction cost using multiple regression techniques." Journal of Construction Engineering and Management, 132(7), pp. 750-758. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:7(750)
  17. Moon, H., Hyun, C., and Hong, T. (2014). "Prediction Model of CO2 Emission for Residential Buildings in South Korea." Journal of Management in Engineering, 30(3), pp. 04014001 : 1-04014001 : 7.
  18. Park, K., Hwang, Y., Seo, S., and Seo, H. (2003). "Quantitative Assessment of Environmental Impacts on Life Cycle of Highways." Journal of Construction Engineering and Management, 129(1), pp. 25-31. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:1(25)
  19. Suthaharan, S. (2015). "Machine Learning Models and Algorithms for Big Data Classification." Springer.
  20. Sonmez, R. (2008). "Parametric range estimating of building costs using regression models and bootstrap." Journal of Construction Engineering and Management, 134(12), pp. 1011-1016. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:12(1011)
  21. Trost, S. M., and Oberlender, G. D. (2003). "Predicting Accuracy of Early Cost Estimates Using Factor analysis and Multivariate Regression." Journal of construction engineering and management, 129(2), pp. 198-204. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:2(198)
  22. Wang, X., Duan, Z., Wu, L., and Yanga, D. (2015). "Estimation of carbon dioxide emission in highway construction: a case study in southwest region of China." Journal of Cleaner Production, 103, pp. 705-714. https://doi.org/10.1016/j.jclepro.2014.10.030
  23. Woo, J. (2011). "Sustainable Optimum Design Evaluation System Development by Environmental and Economical Efficiency of Life Cycle of the Apartment Houses." Ph.D. thesis, Hanyang University.
  24. Yun, W. G., Ha, J. K., and Kim, K. J. (2017). "An Analysis of the Characteristics of Standard Work and Design Information on Estimating Environmental Loads of PSC Beam Bridge in the Design Phase." Journal of the Korean Society of Civil Engineering, KSCE, 37(4), pp. 705-716.