基于时序概率预测方法的高拱坝施工参数仿真应用  

Simulation Application of Construction Parameters for High Arch Dams Based on Time-series Probability Prediction Method

在线阅读下载全文

作  者:林育佳 LIN Yujia(Guangdong NO.2 Hydropower Engineering Company,Ltd.,Guangzhou 510000,China)

机构地区:[1]广东水电二局集团有限公司,广东广州510000

出  处:《云南水力发电》2024年第12期102-105,共4页Yunnan Water Power

摘  要:研究旨在开发一种基于时序概率预测方法的高拱坝施工参数更新模型,以提高施工的准确性和效率。采用ARIMA-LSTM混合模型,将其应用于高拱坝工程,通过数据采集、异常值处理、数据均值化处理和缺失值处理来准备施工数据。ARIMA模型和LSTM模型的建立与参数设置,以及组合预测流程的设计,使能够更好地预测高拱坝施工参数。在施工参数的优化更新方面,进行了高拱坝施工仿真参数分析,特别关注缆机单循环时长的计算方法。通过规划循环路线、设置运行参数和计算单循环时长,努力优化施工过程,以确保高拱坝施工的高效性和安全性。最后,对模型的实际应用效果进行了全面分析,通过对比模型预测结果与实际值,评估了模型的性能和应用效果。This study aims to develop a high arch dam construction parameter update model based on temporal probability prediction method to improve the accuracy and efficiency of construction.It applies the ARIMALSTM hybrid model to high arch dam engineering,preparing construction data through data collection,outlier processing,data averaging,and missing value processing.The establishment and parameter setting of ARIMA and LSTM models,as well as the design of a combined prediction process,can better predict the construction parameters of high arch dams.In terms of optimizing and updating construction parameters,it conducted simulation parameter analysis for high arch dam construction,with a particular focus on the calculation method for the single cycle duration of cable cranes.By planning the circular route,setting operating parameters,and calculating the duration of a single cycle,efforts can be made to optimize the construction process,ensuring the efficiency and safety of high arch dam construction.Finally,this paper conducted a comprehensive analysis of the actual application effect of the model,evaluating its performance and application effectiveness by comparing the predicted results with the actual values.

关 键 词:时序概率 高拱坝施工 参数仿真应用 应用效果 

分 类 号:TV52[水利工程—水利水电工程]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象