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18 results on '"Wu, Binping"'

6. Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

Author

Yu Jia, Bingyu Ren, Wu Binping, Wang Jiajun, Tao Guan, and Song Wenshuai

Subjects
Schedule, Control and Optimization, Concept drift, Generalization, Computer science, 020209 energy, Energy Engineering and Power Technology, 02 engineering and technology, arch dam, lcsh:Technology, Field (computer science), Arch dam, Relevance vector machine, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Arch, Engineering (miscellaneous), grasshopper optimization algorithm, construction simulation, Renewable Energy, Sustainability and the Environment, lcsh:T, interval prediction, relevance vector machine, concrete temperature field, concept drift, 020201 artificial intelligence & image processing, Hybrid kernel, Algorithm, Energy (miscellaneous)
Abstract

Joint grouting simulation is an important aspect of arch dam construction simulation. However, the current construction simulation model simplifies the temperature factors in joint grouting simulation, which leads to the difference between the simulation results and the actual construction schedule. Furthermore, the majority of existing temperature prediction research is based on deterministic point predictions, which cannot quantify the uncertainties of the prediction values. Thus, this study presents a real-time construction simulation method coupling a concrete temperature field interval prediction model to address these problems. First, a real-time construction simulation model is established. Secondly, this paper proposes a concrete temperature interval prediction method based on the hybrid-kernel relevance vector machine (HK-RVM) with the improved grasshopper optimization algorithm (IGOA). The hybrid-kernel method is adopted to ensure the prediction accuracy and generalization ability of the model. Additionally, the improved grasshopper optimization algorithm (IGOA), which utilizes the tent chaotic map and cosine adaptive method to improve the algorithm performance, is developed for the parameter optimization of HK-RVM. Thirdly, concept drift detection based on variable window technology is proposed to update the prediction model. Finally, an arch dam project in China is used as a case study, by which the superiority and applicability of the proposed method are proven.

Published
2020

17. Earth Dam Construction Simulation Considering Stochastic Rainfall Impact.

Author

Zhang, Jun, Zhong, Denghua, Wu, Binping, Lv, Fei, and Cui, Bo

Subjects
CIVIL engineering, EARTH dams, SYSTEMS engineering, COMPUTER-aided design, RAINFALL, SIMULATION methods & models, METEOROLOGICAL precipitation
Abstract

Abstract: Effective construction scheme planning is critical for schedule management, but heavy rain can affect construction processes. In previous studies, stochastic rainfall characteristics are often ignored, and their impact on macro‐ and microconstruction states are not depicted comprehensively. This research presents a construction simulation model to design reasonable construction schemes considering impact of stochastic rainfall. First, a rainfall model suitable for areas with heavy rainfall and uneven seasonal rainfall distribution is built. Then, multiaspect indicators are defined to intuitively quantify rainfall impact. Two case studies are conducted to evaluate applicability of the proposed method. Results demonstrate that the developed rainfall model aligns closely with observed data. Simulation findings reveal that if stochastic rainfall characteristics are ignored, the schedule and queuing probability of trucks will be underestimated, while machinery utilization will be overestimated. This research provides an effective simulation tool for determining adaptive measures to mitigate impacts of rainfall events. [ABSTRACT FROM AUTHOR]

Published
2018
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18. Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on <italic>K</italic>-nearest Neighbor Algorithm.

Author

Zhong, Denghua, Du, Rongxiang, Cui, Bo, Wu, Binping, and Guan, Tao

Abstract

During the storehouse surface rolling construction of a core rockfill dam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse’ rolling surface and the overall quality of the entire dam. Currently, the method used to monitor and control spreading thickness during the dam construction process is artificial sampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and control theory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditional method can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in real time. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring model based on the K-nearest neighbor algorithm. Taking the LHK core rockfill dam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfill dam storehouse surface. [ABSTRACT FROM AUTHOR]

Published
2018
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