水力发电学报2024，Vol.43Issue(3)：120-130,11.DOI:10.11660/slfdxb.20240311
基于深度蒙特卡洛树搜索的拱坝仓面排序研究
Concrete placement sequencing for arch dams based on deep Monte Carlo tree search
摘要
Abstract
Reasonable schemes of concrete placement sequencing have an important impact on accelerating construction progress and optimizing resource allocation.However,previous sequencing methods have simplified this sequential decision-making issue.Most of them adopt multi-attribute decision-making methods,which have the problem of analyzing only the real-time construction state of a dam and neglecting the influence of future concrete placing schemes on the current sequencing strategy;some adopt multi-objective optimization methods for analysis of the multi-objective optimization of the sequencing,but mainly using static weights and neglecting the dynamic changes in the sequencing strategy with the environment.To address these issues,a new concrete placement sequencing method for arch dams based on deep Monte Carlo tree search is presented.First,the constraints and objective function are examined,and a reinforcement learning model of the concrete placement sequencing for arch dams is developed.Then,for this learning model that demands a complex and large discrete state space,to optimize the sequencing strategy with better efficiency,we develop a new Monte Carlo tree search method combined with a deep neural network that is used for the priori action probability distribution prediction and strategy function evaluation.The case study of the Wudongde arch dam in China shows our method is effective in analysis of the sequencing.And compared with the particle swarm method and the evidence theory method,it shortens the construction period by 6 days and 14 days respectively,and raises the average mechanical utilization rate by 1.19%and 1.35%respectively.关键词
拱坝/仓面排序/深度强化学习/蒙特卡洛树搜索/门控循环单元Key words
arch dams/concrete placement sequencing/deep reinforcement learning/Monte Carlo tree search/gated recurrent unit分类
建筑与水利引用本文复制引用
宋文帅,任炳昱,关涛..基于深度蒙特卡洛树搜索的拱坝仓面排序研究[J].水力发电学报,2024,43(3):120-130,11.基金项目
国家自然科学基金(52222907 （）
52379131) （）
