Abstract
In order to optimize the technology of the building, the damage identification of the building structure is studied. Firstly, back propagation neural network (BPNN) and information fusion technology are used to build neural network models. Secondly, the established model is trained. Finally, the displacement mode, natural frequency, Modal Assurance Criterion (MAC), and three kinds of information fusion with only one characteristic information are used as input data to analyse the results of BPNN identification damage. The results show that when the natural frequency is used as the sensitive feature of damage, the accuracy is the highest. The difference between the network output value and the expected value is the smallest, the network output is the most stable, and the network recognition effect is the best. The network output of a mixture of two damage depths is compared with the output of a single damage depth. The data of the network training set composed of the feature data with damage depth of 20 mm and 5 mm has higher accuracy and more accurate damage recognition. This research provides a reference for the optimization of building survey technology and has certain practical value.
Highlights
Historical architecture is a treasure produced in the process of human history development
With the continuous development of science and technology, artificial intelligence (AI) is used in the damage identification of structures, which helps to improve the calculation speed of the system
The damage recognition of the building structure is studied, back propagation neural network (BPNN) and information fusion are used as the basis, and the BPNN is established and trained
Summary
Historical architecture is a treasure produced in the process of human history development. Ground fissures can cause a series of hazards such as cracking of the walls of the building structure, damage to Wireless Communications and Mobile Computing the load-bearing components, settlement, and collapse of the foundation. These hazards affect the normal use of buildings and cause underground pipelines to rupture and road dislocations. The combination of artificial neural network (ANN) and information fusion is applied to structural damage identification This is conducive to the visual development of structural health monitoring. The combination of information fusion and ANN is used in structural damage identification This has far-reaching practical significance for the monitoring of structural safety performance
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