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.