Construction phase oriented dynamic simulation: taking RCC dam placement process as an example
Abstract
Construction simulation has been widely applied in schedule analysis. However, traditional simulation is based on static models built in the planning or design phase, which focuses on overall project-level schedule analysis. To provide activity-level simulation for on-site schedule management, a construction phase oriented dynamic simulation method is proposed, which takes roller compacted concrete (RCC) dam placement process as an example. Considering various innerlayer and inter-layer activities and different construction organization modes, a detailed placement process simulation model is built. Based on construction data collected by real-time monitoring, a construction activity modeling method is given. Additionally, Dirichlet process mixture (DPM) models are applied for simulation parameter updates, which endows density estimation with considerable flexibility and robustness. A fast inference algorithm is also proposed to realize the fast posterior computation of DPM models. The proposed method is tested by an RCC dam project in southwest China. The results show that the proposed method can reflect the dynamic features of the actual placement process in the construction phase and provide accurate schedule predictions for on-site construction management.
Keyword : construction phase, dynamic simulation, RCC dam, placement process, real-time monitoring, DPM models
How to Cite
Hu, W., Zhong, D., Wu, B., & Li, Z. (2019). Construction phase oriented dynamic simulation: taking RCC dam placement process as an example. Journal of Civil Engineering and Management, 25(7), 654-672. https://doi.org/10.3846/jcem.2019.7948
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Jain, S., & Neal, R. M. (2004). A split–merge Markov chain Monte Carlo procedure for the Dirichlet Process mixture model. Journal of Computational and Graphical Statistics, 13, 158-182. https://doi.org/10.1198/1061860043001
Law, A. M. (2015). Simulation modeling and analysis (5th ed.). New York: McGraw-Hill.
Lennox, K. P., Dahl, D. B., Vannucci, M., Day, R., & Tsai, J. W. (2010). A Dirichlet process mixture of hidden Markov models for protein structure prediction. The Annals of Applied Statistics, 4(2), 916-942. https://doi.org/10.1214/09-AOAS296
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Lo, A. Y. (1984). On a class of Bayesian nonparametric estimates I: density estimates. The Annals of Applied Statistics, 12, 351-357. https://doi.org/10.1214/aos/1176346412
Lu, M., Dai, F., & Chen, W. (2007). Real-time decision support for planning concrete plant operations enabled by integrating vehicle tracking technology, simulation, and optimization algorithms. Canadian Journal of Civil Engineering, 34(8), 912-922. https://doi.org/10.1139/l07-029
Luo, W., Liu, Q., & Hu, Z. G. (2009). Study on RCC dam construction system coupling based on Petri net. Journal of System Simulation, 21(7), 2053-2056.
Maceachern, S. N. (1994). Estimating normal means with a conjugate style Dirichlet process prior. Communications in Statistics-Simulation and Computation, 23(3), 727-741. https://doi.org/10.1080/03610919408813196
Martinez, J. C., & Ioannou, P. G. (1994). General purpose simulation with stroboscope. In Proceedings of the 26th Conference on Winter Simulation (pp. 1159-1166). https://doi.org/10.1109/WSC.1994.717503
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National Economic and Trade Commission of the People’s Republic of China. (2001). Design guide of construction equipment selection for hydropower and water conservancy project (DL/T 5133–2001). Beijing, China.
Oloufa, A. A. (1993). Modeling operational activities in objectoriented simulation. Journal of Computing in Civil Engineering, 7(1), 94-106. https://doi.org/10.1061/(ASCE)0887-3801(1993)7:1(94)
Pereira, V., Ferré, G., Giremus, A., & Grivel, E. (2014). Relevance of Dirichlet process mixtures for modeling interferences in underlay cognitive radio. In Proceedings of the 22nd European Signal Processing Conference (EUSIPCO) (pp. 176-180).
Rabaoui, A., Viandier, N., Duflos, E., Marais, J., & Vanheeghe, P. (2012). Dirichlet process mixtures for density estimation in dynamic nonlinear modeling: Application to GPS positioning in urban canyons. IEEE Transactions on Signal Processing, 60(4), 1638-1655. https://doi.org/10.1109/TSP.2011.2180901
Son, J., Rojas, E. M., & Shin, S. W. (2015). Application of agentbased modeling and simulation to understanding complex management problems in CEM research. Journal of Civil Engineering and Management, 21(8), 998-1013. https://doi.org/10.3846/13923730.2014.893916
Song, L. G., & Eldin, N. N. (2012). Adaptive real–time tracking and simulation of heavy construction operations for look– ahead scheduling. Automation in Construction, 27(6), 32-39. https://doi.org/10.1016/j.autcon.2012.05.007
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Vahdatikhaki, F., & Hammad, A. (2014). Framework for near real-time simulation of earthmoving projects using location tracking technologies. Automation in Construction, 42(42), 50-67. https://doi.org/10.1016/j.autcon.2014.02.018
Wang, L., & Dunson, D. B. (2011). Fast Bayesian inference in Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 20(1), 196-216. https://doi.org/10.1198/jcgs.2010.07081
Wang, Q. W., Zhong, D. H., Wu, B. P., Yu, J., & Chang, H. T. (2018). Construction simulation approach of roller-compacted concrete dam based on real-time monitoring. Journal of Zhejiang University – Science A, 19(5), 367-383. https://doi.org/10.1631/jzus.A1700042
Wang, R. C., Zhong, D. H., & Zha, J. M. (1995). Simulation study on the construction process of high roller compacted concrete dam. Journal of Hydroelectric Engineering, 1, 25-37.
West, M., & Escobar, M. D. (1993). Hierarchical priors and mixture models, with application in regression and density estimation. Institute of Statistics and Decision Sciences, Duke University.
Zhang, S., Du, C., Sa, W., Wang, C., & Wang, G. (2014). Bayesian–based hybrid simulation approach to project completion forecasting for underground construction. Journal of Construction Engineering and Management, 140(1), 04013031. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000764
Zhao, C. J., Dong, H., & Zhou, Y. H. (2013). Study on simulation and optimization of concrete placement system on construction surface of RCC dam. Water Resources and Hydropower Engineering, 44(1), 79-82.
Zhao, Y., Kang, J., & Yu, T. (2014). A Bayesian nonparametric mixture model for selecting genes and gene subnetworks. The Annals of Applied Statistics, 8(2), 999-1201. https://doi.org/10.1214/14-AOAS719
AbouRizk, S. M., & Hague, S. (2009). An overview of the COSYE environment for construction simulation. In Proceedings of the 2009 Winter Simulation Conference (WSC) (pp. 2624-2634). https://doi.org/10.1109/WSC.2009.5429307
AbouRizk, S., & Mohamed, Y. (2000). Symphony – an integrated environment for construction simulation. In Proceedings of the 2009 Winter Simulation Conference (WSC) (pp. 19071914). https://doi.org/10.1109/WSC.2000.899185
Akhavian, R., & Behzadan, A. H. (2012). An integrated data collection and analysis framework for remote monitoring and planning of construction operations. Advanced Engineering Informatics, 26(4), 749-761. https://doi.org/10.1016/j.aei.2012.04.004
Akhavian, R., & Behzadan, A. H. (2013). Knowledge-based simulation modeling of construction fleet operations using multimodal-process data mining. Journal of Construction Engineering and Management, 139(11), 04013021. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000775
Akhavian, R., & Behzadan, A. H. (2015). Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers. Advanced Engineering Informatics, 29(4), 867-877. https://doi.org/10.1016/j.aei.2015.03.001
Alvanchi, A., Lee, S. H., & AbouRizk, S. (2011). Modeling framework and architecture of hybrid system dynamics and discrete event simulation for construction. Computer-Aided Civil and Infrastructure Engineering, 26(2), 77-91. https://doi.org/10.1111/j.1467-8667.2010.00650.x
Antoniak, C. E. (1974). Mixtures of dirichlet processes with applications to Bayesian nonparametric problems. Annals of Statistics, 2(6), 1152-1174. https://doi.org/10.1214/aos/1176342871
Bandt, C., & Pompe, B. (2002). Permutation entropy: a natural complexity measure for time series. Physical Review Letters, 88(17), 174102. https://doi.org/10.1103/PhysRevLett.88.174102
Chung, T. H., Mohamed, Y., & Abourizk, S. (2006). Bayesian updating application into simulation in the north edmonton sanitary trunk tunnel project. Journal of Construction Engineering and Management, 132(8), 882-894. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:8(882)
Darema, F. (2004). Dynamic data driven applications systems: A new paradigm for application simulations and measurements. In International Conference on Computational Science (pp. 662-669). https://doi.org/10.1007/978-3-540-24688-6_86
Escobar, M. D. (1994). Estimating normal means with a dirichlet process prior. Journal of the American Statistical Association, 89(425), 268-277. https://doi.org/10.1080/01621459.1994.10476468
Escobar, M. D., & West, M. (1995). Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association, 90(430), 577-588. https://doi.org/10.1080/01621459.1995.10476550
Guan, T., Zhong, D. H., Ren, B. Y., Song, W. S., & Chu, Z. Q. (2018). Construction simulation of high arch dams based on fuzzy bayesian updating algorithm. Journal of Zhejiang University – Science A, 19(7), 505-520. https://doi.org/10.1631/jzus.A1700372
Gurgun, A. P., Zhang, Y., & Touran, A. (2013). Schedule contingency analysis for transit projects using a simulation approach. Journal of Civil Engineering and Management, 19(4), 465-475. https://doi.org/10.3846/13923730.2013.768542
Halpin, D. W. (1977). CYCLONE – A method for modeling job site process. Journal of the Construction Division, 103(3), 489-499.
Halpin, D. W. (1990). Micro–CYCLONE user’s manual. Division of Construction Engineering and Management, Purdue University.
Han, S., Ko, Y. H., Hong, T., Koo, C., & Lee, S. (2016). Framework for the validation of simulation-based productivity analysis: focused on curtain wall construction process. Journal of Civil Engineering and Management, 23(2), 163-172. https://doi.org/10.3846/13923730.2014.992468
Ioannou, P. G. (1989). UM-CYCLONE user’s guide. Department of Civil Engineering, University of Michigan.
Ishwaran, H., & James, L. F. (2001). Gibbs sampling methods for stick–breaking priors. Journal of the American Statistical Association, 96, 161-173. https://doi.org/10.1198/016214501750332758
Jain, S., & Neal, R. M. (2004). A split–merge Markov chain Monte Carlo procedure for the Dirichlet Process mixture model. Journal of Computational and Graphical Statistics, 13, 158-182. https://doi.org/10.1198/1061860043001
Law, A. M. (2015). Simulation modeling and analysis (5th ed.). New York: McGraw-Hill.
Lennox, K. P., Dahl, D. B., Vannucci, M., Day, R., & Tsai, J. W. (2010). A Dirichlet process mixture of hidden Markov models for protein structure prediction. The Annals of Applied Statistics, 4(2), 916-942. https://doi.org/10.1214/09-AOAS296
Liu, Y., Zhong, D., Cui, B., Zhong, G., & Wei, Y. (2015). Study on real–time construction quality monitoring of storehouse surface for RCC dams. Automation in Construction, 49(1), 100-112. https://doi.org/10.1016/j.autcon.2014.10.003
Lo, A. Y. (1984). On a class of Bayesian nonparametric estimates I: density estimates. The Annals of Applied Statistics, 12, 351-357. https://doi.org/10.1214/aos/1176346412
Lu, M., Dai, F., & Chen, W. (2007). Real-time decision support for planning concrete plant operations enabled by integrating vehicle tracking technology, simulation, and optimization algorithms. Canadian Journal of Civil Engineering, 34(8), 912-922. https://doi.org/10.1139/l07-029
Luo, W., Liu, Q., & Hu, Z. G. (2009). Study on RCC dam construction system coupling based on Petri net. Journal of System Simulation, 21(7), 2053-2056.
Maceachern, S. N. (1994). Estimating normal means with a conjugate style Dirichlet process prior. Communications in Statistics-Simulation and Computation, 23(3), 727-741. https://doi.org/10.1080/03610919408813196
Martinez, J. C., & Ioannou, P. G. (1994). General purpose simulation with stroboscope. In Proceedings of the 26th Conference on Winter Simulation (pp. 1159-1166). https://doi.org/10.1109/WSC.1994.717503
Müller, P., Quintana, F. A., Jara, A., & Hanson, T. (2015). Bayesian nonparametric data analysis. Springer. https://doi.org/10.1007/978-3-319-18968-0
National Economic and Trade Commission of the People’s Republic of China. (2001). Design guide of construction equipment selection for hydropower and water conservancy project (DL/T 5133–2001). Beijing, China.
Oloufa, A. A. (1993). Modeling operational activities in objectoriented simulation. Journal of Computing in Civil Engineering, 7(1), 94-106. https://doi.org/10.1061/(ASCE)0887-3801(1993)7:1(94)
Pereira, V., Ferré, G., Giremus, A., & Grivel, E. (2014). Relevance of Dirichlet process mixtures for modeling interferences in underlay cognitive radio. In Proceedings of the 22nd European Signal Processing Conference (EUSIPCO) (pp. 176-180).
Rabaoui, A., Viandier, N., Duflos, E., Marais, J., & Vanheeghe, P. (2012). Dirichlet process mixtures for density estimation in dynamic nonlinear modeling: Application to GPS positioning in urban canyons. IEEE Transactions on Signal Processing, 60(4), 1638-1655. https://doi.org/10.1109/TSP.2011.2180901
Son, J., Rojas, E. M., & Shin, S. W. (2015). Application of agentbased modeling and simulation to understanding complex management problems in CEM research. Journal of Civil Engineering and Management, 21(8), 998-1013. https://doi.org/10.3846/13923730.2014.893916
Song, L. G., & Eldin, N. N. (2012). Adaptive real–time tracking and simulation of heavy construction operations for look– ahead scheduling. Automation in Construction, 27(6), 32-39. https://doi.org/10.1016/j.autcon.2012.05.007
Tsiligkaridis, T., & Forsythe, K. (2015). Adaptive low-complexity sequential inference for Dirichlet process mixture models. In NIPS’15 Proceedings of the 28th International Conference on Neural Information Processing Systems (pp. 28-36). MA, USA: MIT Press Cambridge.
Vahdatikhaki, F., & Hammad, A. (2014). Framework for near real-time simulation of earthmoving projects using location tracking technologies. Automation in Construction, 42(42), 50-67. https://doi.org/10.1016/j.autcon.2014.02.018
Wang, L., & Dunson, D. B. (2011). Fast Bayesian inference in Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 20(1), 196-216. https://doi.org/10.1198/jcgs.2010.07081
Wang, Q. W., Zhong, D. H., Wu, B. P., Yu, J., & Chang, H. T. (2018). Construction simulation approach of roller-compacted concrete dam based on real-time monitoring. Journal of Zhejiang University – Science A, 19(5), 367-383. https://doi.org/10.1631/jzus.A1700042
Wang, R. C., Zhong, D. H., & Zha, J. M. (1995). Simulation study on the construction process of high roller compacted concrete dam. Journal of Hydroelectric Engineering, 1, 25-37.
West, M., & Escobar, M. D. (1993). Hierarchical priors and mixture models, with application in regression and density estimation. Institute of Statistics and Decision Sciences, Duke University.
Zhang, S., Du, C., Sa, W., Wang, C., & Wang, G. (2014). Bayesian–based hybrid simulation approach to project completion forecasting for underground construction. Journal of Construction Engineering and Management, 140(1), 04013031. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000764
Zhao, C. J., Dong, H., & Zhou, Y. H. (2013). Study on simulation and optimization of concrete placement system on construction surface of RCC dam. Water Resources and Hydropower Engineering, 44(1), 79-82.
Zhao, Y., Kang, J., & Yu, T. (2014). A Bayesian nonparametric mixture model for selecting genes and gene subnetworks. The Annals of Applied Statistics, 8(2), 999-1201. https://doi.org/10.1214/14-AOAS719