Collaborative analysis model of construction schedule and quality for RCC dam based on DES and deep learning
DOI: https://doi.org/10.3846/jcem.2025.24061Abstract
Construction schedule simulation is an effective method to analyze the progress of concrete rolling construction and its simulation accuracy is an important indicator that project managers care about. However, existing roller compacter concrete construction simulation research pays less attention to the impact of construction process changes caused by compaction quality on simulation accuracy. Based on the real-time monitoring system of concrete rolling construction, this paper establishes a simulation model of concrete rolling construction considering compaction quality. The model firstly reconstructs the property parameters of roller compacted concrete using the improved generated adversarial network, secondly, considering the nonlinear correlation characteristics of parameters affecting compaction quality, a concrete compaction quality analysis model based on improving grey wolf optimized convolutional neural networks (IGWO-CNN) was built. Finally, the intelligent analysis model of compaction quality was embedded into the simulation model of concrete rolling construction. Based on real-time monitoring data, the simulation model was driven and the simulation process was adjusted adaptively. Taking the construction of a roller compacted concrete dam in China as an example, the validity and superiority of this model are proved.
Keywords:
construction simulation, concrete rolling construction, real-time monitoring, compaction quality, improved generative adversarial networks, improving grey wolf optimized convolutional neural networksHow to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
AbouRizk, S., & Mohamed, Y. (2000). Simphony – an integrated environment for construction simulation. Orlando, FL, USA. https://doi.org/10.1109/WSC.2000.899185
Aghaeipour, A., & Madhkhan, M. (2020). Mechanical properties and durability of roller compacted concrete pavement (RCCP) a review. Road Materials and Pavement Design, 21(7), 1775–1798. https://doi.org/10.1080/14680629.2019.1579754
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
Baldominos, A., Saez, Y., & Isasi, P. (2020). On the automated, evolutionary design of neural networks: Past, present, and future. Neural Computing and Applications, 32(2), 519–545. https://doi.org/10.1007/s00521-019-04160-6
Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning. Springer.
Celik, N., Lee, S., Vasudevan, K., & Son, Y. (2010). Dddas-based multi-fidelity simulation framework for supply chain systems. IIE Transactions, 42(5), 325–341. https://doi.org/10.1080/07408170903394306
Chen, Y., Wang, Y., Kirschen, D., & Zhang, B. (2018). Model-free renewable scenario generation using generative adversarial networks. IEEE Transactions on Power Systems, 33(3), 3265–3275. https://doi.org/10.1109/TPWRS.2018.2794541
Cui, C., & Fearn, T. (2018). Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration. Chemometrics and Intelligent Laboratory Systems, 182, 9–20. https://doi.org/10.1016/j.chemolab.2018.07.008
Du, R., Zhong, D., Yu, J., Tong, D., & Wu, B. (2016). Construction simulation for a core rockfill dam based on optimal construction stages and zones: Case study. Journal of Computing in Civil Engineering, 30(3), Article 05015002. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000523
Faris, H., Aljarah, I., Al-Betar, M. A., & Mirjalili, S. (2018). Grey wolf optimizer: A review of recent variants and applications. Neural Computing and Applications, 30(2), 413–435. https://doi.org/10.1007/s00521-017-3272-5
Flangan, C. (1990). The Bresenham line-drawing algorithm. Science Press.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems (pp. 2672–2680). MIT Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Guan, T., Zhong, D., Ren, B., Song, W., & Chu, Z. (2018). Construction simulation of high arch dams based on fuzzy Bayesian updating algorithm. Journal of Zhejiang University. A. Science, 19(7), 505–520. https://doi.org/10.1631/jzus.A1700372
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved training of Wasserstein GANs. Advances in Neural Information Processing Systems, 30, 5768–5778.
Halpin, D. W. (1977). CYCLONE–A method for modelling job site process. Journal of the Construction Division, 103(3), 489–499. https://doi.org/10.1061/JCCEAZ.0000712
Hong, Y., Tian, Z., & Sun, X. (2020). Dynamic evaluation for compaction quality of roller compacted concrete based on reliability metrics. Journal of Construction Engineering and Management, 146(10), Article 04020123. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001925
Hu, W., Jia, X., Zhu, X., Gong, H., Xue, G., & Huang, B. (2019a). Investigating key factors of intelligent compaction for asphalt paving: a comparative case study. Construction and Building Materials, 229, Article 116876. https://doi.org/10.1016/j.conbuildmat.2019.116876
Hu, W., Zhong, D., Wu, B., & Li, Z. (2019b). 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
Ioannou, P. G. (1990). UM-CYCLONE discrete event simulation system user’s guide (UMCEE Rep. No. 89-12). Civil and Environmental Engineering Department, University of Michigan, USA.
Ji, W., & AbouRizk, S. M. (2018). Data-driven simulation model for quality-induced rework cost estimation and control using absorbing Markov chains. Journal of Construction Engineering and Management, 144(8), Article 04018078. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001534
Law, A. M. (2015). Simulation modeling and analysis (5th ed.). McGraw-Hill Education.
Liu, D., Sun, J., Zhong, D., & Song, L. (2012). Compaction quality control of earth-rock dam construction using real-time field operation data. Journal of Construction Engineering and Management, 138(9), 1085–1094. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000510
Liu, D., Li, Z., & Liu, J. (2015a). Experimental study on real-time control of roller compacted concrete dam compaction quality using unit compaction energy indices. Construction and Building Materials, 96, 567–575. https://doi.org/10.1016/j.conbuildmat.2015.08.048
Liu, Y., Zhong, D., Cui, B., Zhong, G., & Wei, Y. (2015b). Study on real-time construction quality monitoring of storehouse surfaces for RCC dams. Automation in Construction, 49(Part A), 100–112. https://doi.org/10.1016/j.autcon.2014.10.003
Lu, M. (2003). Simplified discrete-event simulation approach for construction simulation. Journal of Construction Engineering and Management, 129(5), 537–546. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:5(537)
Luo, W., Liu, Q., & Hu, Z. (2009). Study on RCC dam construction system coupling based on Petri net. Journal of System Simulation, 21(7), 2053–2056 (in Chinese).
Lv, F., Wang, J., Cui, B., Yu, J., Sun, J., & Zhang, J. (2020). An improved extreme gradient boosting approach to vehicle speed prediction for construction simulation of earthwork. Automation in Construction, 119, Article 103351. https://doi.org/10.1016/j.autcon.2020.103351
Martinez, J. C., & Ioannou P. G. (1994). General purpose simulation with stroboscope. In Proceedings of Winter Simulation Conference (pp. 1159–1166). IEEE. https://doi.org/10.1109/WSC.1994.717503
Mohamed, E., Jafari, P., & Siu, M. F. F. (2017). Data-driven simulation-based model for planning roadway operation and maintenance projects. In Proceedings of 2017 Winter Simulation Conference (pp. 3323–3333). IEEE. https://doi.org/10.1109/WSC.2017.8248049
National Energy Administration of People’s Republic of China. (2009). Construction specifications for hydraulic RCC (DL/T5112-2009). Water Power Press (in Chinese).
Oloufa, A. A. (1993). Modeling operational activities in object-oriented simulation. Journal of Computing in Civil Engineering, 7(1), 94–106. https://doi.org/10.1061/(ASCE)0887-3801(1993)7:1(94)
Panahi, M., Sadhasivam, N., Pourghasemi, H. R., Rezaie, F., & Lee, S. (2020). Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). Journal of Hydrology, 588, Article 125033. https://doi.org/10.1016/j.jhydrol.2020.125033
Shrestha, P., & Behzadan, A. H. (2018). Chaos theory-inspired evolutionary method to refine imperfect sensor data for data-driven construction simulation. Journal of Construction Engineering and Management, 144(3), Article 04018001. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001441
Song, L., & Eldin, N. N. (2012). Adaptive real-time tracking and simulation of heavy construction operations for look-ahead scheduling. Automation in Construction, 27, 32–39. https://doi.org/10.1016/j.autcon.2012.05.007
Song, W., Guan, T., Ren, B., Yu, J., Wang, J., & Wu, B. (2020). Real-time construction simulation coupling a concrete temperature field interval prediction model with optimized hybrid-kernel RVM for arch dams. Energies, 13(17), Article 4487. https://doi.org/10.3390/en13174487
Vahdatikhaki, F., & Hammad, A. (2014). Framework for near real-time simulation of earthmoving projects using location tracking technologies. Automation in Construction, 42, 50–67. https://doi.org/10.1016/j.autcon.2014.02.018
Wang, R., Zhong, D., & Zha, J. (1995). Simulation study on the construction process of high roller compacted concrete dam. Journal of Hydroelectric Engineering, 1, 25–37 (in Chinese).
Wang, H., Li, G., Wang, G., Peng, J., Jiang, H., & Liu, Y. (2017). Deep learning based ensemble approach for probabilistic wind power forecasting. Applied Energy, 188, 56–70. https://doi.org/10.1016/j.apenergy.2016.11.111
Wang, J., Zhong, D., Adeli, H., Wang, D., Liu, M. (2018a). Smart bacteria-foraging algorithm-based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth-rock dam. Expert Systems, 35(6), Article e12357. https://doi.org/10.1111/exsy.12357
Wang, Q., Zhong, D., Wu, B., Yu, J., & Chang, H. (2018b). Construction simulation approach of roller-compacted concrete dam based on real-time monitoring. Journal of Zhejiang University. A. Science, 19(5), 367–383. https://doi.org/10.1631/jzus.A1700042
Xu, Y., Li, F., & Asgari, A. (2022). Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms. Energy, 240, Article 122692. https://doi.org/10.1016/j.energy.2021.122692
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), Article 04013031. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000764
Zhang, J., & Zeng, D. (2018). Experimental research on construction technology of roller compacted concrete. China Water Transport, 18(9), 257–258 (in Chinese).
Zhang, J., Yu, J., Guan, T., Wang, J., Tong, D., & Wu, B. (2020). Adaptive compaction construction simulation based on Bayesian field theory. Sensors, 20(18), Article 5178. https://doi.org/10.3390/s20185178
Zhang, Y., Zhou, B., Cai, X., Guo, W., Ding, X., & Yuan, X. (2021). Missing value imputation in multivariate time series with end-to-end generative adversarial networks. Information Sciences, 551, 67–82. https://doi.org/10.1016/j.ins.2020.11.035
Zhang, H., Zhang, W., Meng, Y., & Li, H. (2022). Deterioration of sea sand roller compacted concrete used in island reef airport runway under salt spray. Construction and Building Materials, 322, Article 126523. https://doi.org/10.1016/j.conbuildmat.2022.126523
Zhang, J., Yu, J., Yu, P., Wang, X., Tong, D., & Wang, J. (2023). Enhanced semi‐supervised ensemble machine learning approach for earthwork construction simulation activity sequence automatically updating driven by weather data. Geological Journal, 58(6), 2231–2253. https://doi.org/10.1002/gj.4631
Zhao, C., Dong, H., & Zhou, Y. (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 (in Chinese).
Zhong, D., Hu, W., Wu, B., Li, Z., & Zhang, J. (2017). Dynamic time-cost-quality tradeoff of rockfill dam construction based on real-time monitoring. Journal of Zhejiang University. A. Science, 18(1), 1–19. https://doi.org/10.1631/jzus.A1600564
Zhong, D., Yang, S., Chang, H., Yu, J., Yan, F. (2015). Construction schedule risk analysis of high roller-compacted concrete dams based on improved CSRAM. Science China Technological Sciences, 58(6), 1021–1030. https://doi.org/10.1007/s11431-015-5781-8
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.