| 张君,余佳,任炳昱,王晓玲,俞澎,林威伟.考虑高寒低温影响的高心墙堆石坝仓面施工仿真模型研究[J].水利学报,2022,53(2):200-211 |
| 考虑高寒低温影响的高心墙堆石坝仓面施工仿真模型研究 |
| Study on the simulation model of high-core rockfill dam construction in alpine region considering the influence of low temperature |
| 投稿时间：2021-07-02 |
| DOI：10.13243/j.cnki.slxb.20210597 |
| 中文关键词: 高寒低温 高心墙堆石坝 仓面施工仿真 粒子群优化多层感知机（PSOMLP） 气温预测 |
| 英文关键词: construction in cold area high core rockfill dam construction simulation particle swarm optimization (PSOMLP) temperature forecast |
| 基金项目:国家自然科学基金雅砻江联合基金项目（U1965207）；天津市研究生科研创新项目（2020YJSB094） |
|
| 摘要点击次数: 829 |
| 全文下载次数: 208 |
| 中文摘要: |
| 低温是影响高寒地区高心墙堆石坝施工的关键因素。然而，目前堆石坝施工仿真主要采用工程经验或者统计分析方法获得有效施工时长等参数从而间接反映气温对施工过程的影响，难以准确量化因高寒低温停工导致的施工进度滞后的影响，且缺乏考虑高寒施工环境下的保温工序，无法满足仓面施工仿真的精细化需求。针对以上问题，本文提出一个考虑高寒低温影响的高心墙堆石坝仓面施工仿真模型。首先，提出基于粒子群优化多层感知机（Particle Swarm Optimization-Muti Layer Perception，PSOMLP）的气温时间序列预测方法，通过粒子群优化算法（PSO）优化多层感知机（MLP）的超参数，弥补传统最小梯度下降法训练MLP时超参数难以确定、训练效率低以及精度差的不足，并将其嵌入仓面仿真模型，以获取精确的低温停工时刻和时长。其次，利用自助抽样法（Boot?strap）获得揭膜和覆膜工序活动时长，进而构建同时考虑低温停工和新增工序影响下的高心墙堆石坝仓面施工仿真模型。工程应用表明，相比于实际施工进度，传统仿真模型平均误差为19.74%，提出的仿真模型平均误差仅为1.21%，证明所提出的方法能够有效量化低温停工时长和新增工序对施工进度的影响，为高寒地区高心墙堆石坝施工进度分析提供了新手段。 |
| 英文摘要: |
| Low temperature is a key influencing factor that impedes the construction progress of high-core rock-fill dams in alpine region. However,most of the existing simulation models of construction merely consider the effect of temperature on construction indirectly through parameters such as effective construction time which are acquired from engineering experience or statistical analysis methods. As a result, it is difficult to accurately quantify the impact of low temperature on the construction progress. Besides, such models put little consideration on the heat preservation measures in alpine region, which is incapable of meeting the high accuracy demand of the construction simulation of rockfill dam. To solve the above problems, this paper proposes a simulation model of the high-core rockfill dam construction in alpine region considering the influence of low temperature. First,a temperature time series prediction method based on the Particle Swarm Optimization Multilayer Perceptron (PSOMLP) is established. In this process, the hyperparameters of the multilayer perceptron (MLP) are optimized through the Particle Swarm Optimization algorithm (PSO) to solve the problems encountered in traditional MLP training process, including difficult determination of hyperparameters, low training efficiency and poor accuracy. Then, the prediction method is embedded in the construction simulation model to determine the accurate low-temperature shutdown time. Secondly, based on the Bootstrap method, the activity time of heat preservation measures is sampled and used to construct the simulation model of the high-core rockfill dam in alpine region which simultaneously considers the influence of the low-temperature shutdown and the added heat preservation measures. Engineering application results show that the proposed PSOMLP model has higher prediction accuracy than the traditional temperature prediction models, reducing the average error rate from 19.74% to 1.21%, which demonstrates that the qualification of the proposed method in quantifying the influence of low-temperature showdown and added heat preservation measures on construction progress. Therefore, the proposed model provides a new idea for the simulation of high-core rock-fill dam construction in alpine region. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|