Investigation Of The Span Design Of High Speed Railway Bridge Based On Reinforcement Learning

The construction of high-speed railway plays an important role in the development of modern transportation mode.In order to effectively control subgrade settlement,save land resources,and ensure smooth driving,most road in high-speed railway are replaced by bridges.For high-speed railway bridges,the bridge span is a critical problem in the design process.High-speed railway bridges generally adopt prefabricated standard spans,the traditional manual design method is prone to produce broken spans.The pier height and the amount of work are also not fully considered,especially for long-distance sections,it is difficult for designers to arrange the poets with hundreds of spans reasonably at the same time.Therefore,it is urgent to study the use of intelligent cloth span design instead of the traditional manual way to improve efficiency,save time and cost.This paper studies the design method of high-speed railway bridge span based on reinforcement learning algorithm.The main contents are:The basic principles of high-speed railway bridge span arrangement are studied and the database of span arrangement is established.First,for the problem of high-speed railway bridge span,the basic principles of bridge span and the calculation of curving bridge layout position are investigated.Then,the secondary development of CAD using C# language is carried out to extract the information needed for the bridge span,such as terrain and surface features,and the corresponding database is established.A method for designing the span of high-speed railway bridge based on Q-learning algorithm of reinforcement learning is proposed.In the algorithm design,the high-speed railway line will be discretized to realize the discrete finite state number from the continuous infinite state,to avoid the excessive number of states resulting in the explosion of the calculation memory during the calculation of the Q table value,and ensure the normal update calculation of the Q table value.The action is designed according to the types of standard simply-supported beam and continuous beam that can be selected in the beam library.The number of actions is equal to the number of beams.According to the priority levels of bridge span requirements,the weights of different bridge span conditions are determined,and the reward function is designed.Based on a large number of numerical calculations,the influences of parameters on the algorithm accuracy are analyzed,and the final parameters are determined.By implementation of practical cases,the results show that the scheme obtained by the Q-learning is not only fast in efficiency,but also reduces bridge pier quantities compared with the traditional design scheme.However,due to some shortcomings of the Q-learning algorithm,the algorithm may fall into local optimization,resulting in the design of bridge piers in some areas invading the red line of the road and failing to meet the intersection requirements.A design method of high-speed railway bridge span based on Q-learning algorithm and simulated annealing.To overcome the shortcomings of Q-learning algorithm,a highspeed railway bridge spanning algorithm based on simulated annealing Q-learning(SAQ)is designed.According to the exploration and utilization relationship of reinforcement learning,a new annealing strategy based on the change of Q value is proposed,which enables the algorithm to make full exploration in the early stage and ensure the diversity of spanning results.At the same time,the jumping property of simulated annealing strategy is used to avoid local optimal solutions.By comparing the traditional design method,Q-learning method and SA-Q method,the results of two experimental examples show that the learning efficiency and convergence speed based on SA-Q algorithm are 35% higher than that of Q-learning.Compared with the traditional design,the proposed method can save time cost,require less work,and guarantee the rationality of the layout.