Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Machine Learning Predictive Model for Forecasting Demolition Waste Generation

해체폐기물 발생량 예측을 위한 머신러닝 모델 개발

  • Cha, Gi-Wook (School of Science and Technology Acceleration Engineering, Kyungpook National University) ;
  • Hong, Won-Hwa (School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University)
  • 차기욱 (경북대학교 과학기술실용공학부) ;
  • 홍원화 (경북대학교 건설환경에너지공학부)
  • Received : 2022.11.02
  • Accepted : 2023.01.09
  • Published : 2023.02.28
⟨ Previous Next ⟩

Abstract

Due to the rapid increase in Construction & Demolition (C&D) waste, C&D waste management (WM) management is an important challenge, and Artificial Intelligence (AI) technology is being actively used as a smart WM strategy. Demolition waste (DW) predictive models were developed and tested by applying artificial neural network (ANN) and support vector machine (SVM) based on a dataset consisting of categorical input variables in this study. For this, DW predictive models with optimal performance were derived through hyper-parameter tuning of ANN and SVM algorithms. As a result of this study, the predictive performance of the ANN and SVM models showed mean absolute error (MAE) 71.730 and 79.437, root mean square error (RMSE) 119.414 and 104.979, coefficient of determination (R squared) 0.891 and 0.859 mean square error (MSE) 11020.556 and 14259.820 respectively. Therefore, the ANN model was confirmed to be a better model for predicting the DW than the SVM model in this study. At this time, the mean of the observed values is 987.181 kg·m-2 , and the mean of the predictive values of the ANN and SVM models are 986.180 kg·m-2 and 991.050 kg·m-2 , respectively.

Keywords

Acknowledgement

이 연구는 2022년도 한국연구재단 연구비 지원에 의한 결과의 일부임. 과제번호:NRF-2019R1A2C1088446

References

  1. Abbasi, M., Abduli, M. A., Omidvar, B., & Baghvand, A. (2014). Results uncertainty of support vector machine and hybrid of wavelet transform-support vector machine models for solid waste generation forecasting. Environmental Progress & Sustainable Energy, 33(1), 220-228. https://doi.org/10.1002/ep.11747
  2. Abbasi, M., & El Hanandeh, A., (2016). Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Management, 56, 13-22. https://doi.org/10.1016/j.wasman.2016.05.018
  3. Abdallah, M., Talib, M. A., Feroz, S., Nasir, Q., Abdalla, H., & Mahfood, B. (2020). Artificial intelligence applications in solid waste management: A systematic research review. Waste Management, 109, 231-246. https://doi.org/10.1016/j.wasman.2020.04.057
  4. Abunama, T., Othman, F., Ansari, M., & El-Shafie, A. (2019). Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill. Environmental Science and Pollution Research, 26, 3368-3381. https://doi.org/10.1007/s11356-018-3749-5
  5. Ali Abdoli, M., Falah Nezhad, M., Salehi Sede, R., & Behboudian, S. (2012). Longterm forecasting of solid waste generation by the artificial neural networks. Environmental Progress & Sustainable Energy, 31(4), 628-636. https://doi.org/10.1002/ep.10591
  6. Butera, S., Christensen, T. H., & Astrup, T. F. (2014). Composition and leaching of construction and demolition waste: inorganic elements and organic compounds. Journal of Hazardous Materials, 276, 302-311. https://doi.org/10.1016/j.jhazmat.2014.05.033
  7. Cai, T., Wang, G., & Guo, Z. (2020). Construction and Demolition Waste Generation Forecasting Using a Hybrid Intelligent Method, In 2020 9th International Conference on Industrial Technology and Management (ICITM) (pp. 312-316). IEEE.
  8. Cha, G. W., Moon, H. J., & Kim, Y. C. (2021). Comparison of random forest and gradient boosting machine models for predicting demolition waste based on small datasets and categorical variables. International Journal of Environmental Research and Public Health, 18(16), 8530.
  9. Cha, G. W., Moon, H. J., & Kim, Y. C. (2022). A hybrid machine-learning model for predicting the waste generation rate of building demolition projects. Journal of Cleaner Production, 134096.
  10. Cha, G. W., Moon, H. J., Kim, Y. M., Hong, W. H., Hwang, J. H., Park, W. J., & Kim, Y. C. (2020). Development of a prediction model for demolition waste generation using a random forest algorithm based on small datasets. International Journal of Environmental Research and Public Health, 17(19), 6997.
  11. Cheng, J., Dekkers, J. C., & Fernando, R. L. (2021). Cross-validation of best linear unbiased predictions of breeding values using an efficient leave-one-out strategy. Journal of Animal Breeding and Genetics, 138(5), 519-527. https://doi.org/10.1111/jbg.12545
  12. Choi, S. W. (2019). A study on the determinants and forecasting model of household waste generation, Master's thesis. University of Seoul (UOS)
  13. Dai, C., Li, Y. P., & Huang, G. H. (2011). A two-stage support-vector-regression optimization model for municipal solid waste management-a case study of Beijing, China. Journal of environmental management, 92(12), 3023-3037. https://doi.org/10.1016/j.jenvman.2011.06.038
  14. Golbaz, S., Nabizadeh, R., & Sajadi, H. S. (2019). Comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence. Journal of Environmental Health Science and Engineering, 17(1), 41-51. https://doi.org/10.1007/s40201-018-00324-z
  15. Graus, M., Niemietz, P., Rahman, M. T., Hiller, M., & Pahlenkemper, M. (2018). Machine learning approach to integrate waste management companies in micro grids, In 2018 19th International Scientific Conference on Electric Power Engineering (EPE) (pp. 1-6). IEEE.
  16. Huang, X., & Xu, X. (2011). Legal regulation perspective of eco-efficiency construction waste reduction and utilization. Urban Development Studies, 9, 90-94.
  17. Jo, U. H., Byun, J. H., Won, J. M., Kim, E. A., Kim, Y. Y., Choi, Y. H., You, C. S., & Kim, J. S. (2019). Prediction of MSW Generation in Gyeonggi-do Using Multiple Regression Analysis. Korea society of waste management, 2019(0), 122-122.
  18. Kumar, A., Samadder, S.R., Kumar, N., & Singh, C. (2018). Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling. Waste Management, 79, 781-790. https://doi.org/10.1016/j.wasman.2018.08.045
  19. Li, J., Ding, Z., Mi, X., & Wang, J. (2013). A model for estimating construction waste generation index for building project in China. Resources, Conservation and Recycling, 74, 20-26. https://doi.org/10.1016/j.resconrec.2013.02.015
  20. Liang, G., Panahi, F., Ahmed, A. N., Ehteram, M., Band, S. S., & Elshafie, A. (2021). Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components. Journal of Cleaner Production, 315, 128039.
  21. Llatas, C. (2011). A model for quantifying construction waste in projects according to the European waste list. Waste Management, 31(6), 1261-1276. https://doi.org/10.1016/j.wasman.2011.01.023
  22. Lu, W., Yuan, H., Li, J., Hao, J.J., Mi, X., & Ding, Z. (2011). An empirical investigation of construction and demolition waste generation rates in Shenzhen city, South China. Waste Manag, 31(4), 680-687. https://doi.org/10.1016/j.wasman.2010.12.004
  23. Rani, M., & Gupta, A. (2016). Construction waste management in India. International Journal of Science Technology And Management, June 2016 Vol, (05), 2394-1537.
  24. Shamshiry, E., Mokhtar, M., Abdulai, A., Komoo, I., & Yahaya, N. (2014). Combining artificial neural networkgenetic algorithm and response surface method to predict waste generation and optimize cost of solid waste collection and transportation process in Langkawi island, Malaysia. Malaysian Journal of Science, 33, 118-140. https://doi.org/10.22452/mjs.vol33no2.1
  25. Song, Y., Wang, Y., Liu, F., & Zhang, Y. (2017). Development of a hybrid model to predict construction and demolition waste: China as a case study. Waste Management. 59, 350-361. https://doi.org/10.1016/j.wasman.2016.10.009
  26. Soni, U., Roy, A., Verma, A., & Jain, V. (2019). Forecasting municipal solid waste generation using artificial intelligence models-a case study in India. SN Applied Sciences, 1(2), 1-10. https://doi.org/10.1007/s42452-018-0157-x
  27. Wang, J., Li, Z., & Tam, V. W. (2015). Identifying best design strategies for construction waste minimization. Journal of cleaner production, 92, 237-247. https://doi.org/10.1016/j.jclepro.2014.12.076
  28. Wong, T. T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9), 2839-2846. https://doi.org/10.1016/j.patcog.2015.03.009
  29. Wu, H., Zuo, J., Zillante, G., Wang, J., & Yuan, H. (2019). Status quo and future directions of construction and demolition waste research: A critical review. Journal of Cleaner Production, 240, 118163.