Study On Damage Detection Of Continuous Rigid Frame Bridge Based On BIM And Neural Network

The prestressed concrete continuous rigid frame bridge has the advantages of strong spanning ability,convenient construction,smooth driving and low maintenance cost in the later period.It is one of the widely used bridge types in modern bridge engineering.More and more prestressed concrete continuous rigid frame bridges are being put into use in highway and railway projects.In the real operation process,the prestressed concrete continuous rigid frame bridge is affected by many factors including environmental corrosion,material aging,accidental overload and natural disasters.These factors make bridge inevitably have different degrees of damage.Such damage is often aggravated with the increase of operating time,which brings security risks to the operation of the bridge structure.In order to ensure the safety of the bridge structure,it is of great theoretical and practical significance to study and discuss the damage detection of prestressed concrete continuous rigid frame bridge.On the basis of summarizing and drawing on the previous research results,this thesis focuses on the structural damage detection of the existing prestressed concrete continuous rigid frame bridge.Combined with BIM technology and AI technology,the following aspects are mainly completed:(1)Based on the real engineering project,a three-dimensional numerical model was established,and the common structural damage types and causes of existing continuous rigid frame bridges were analyzed.The effects of the overall stiffness of the section and the cracking of the floor on the damage detection parameters and structural forces of the continuous rigid frame bridge are discussed.The relationship between the type,location and extent of damage of the continuous rigid frame bridge structure and structural damage detection parameters is obtained.(2)The neural network structure,type and common neural network algorithm are summarized,and the neural network model suitable for continuous rigid frame bridge damage detection is determined.Aiming at some limitations of the classical neural network algorithm,the corresponding improvement measures are proposed.The calculation example shows that the improved neural network algorithm can improve the damage detection efficiency and accuracy of the continuous rigid frame bridge structure.(3)The development technology about an intelligent and multi-dimensional visual structure damage detection system based on BIM and neural network algorithm for continuous rigid frame bridge is discussed.