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2 2023

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Analysis of out-of-plane free vibration of single damaged curved beam based on precise algorithm of structural mechanics

Li Xiaofei, Pan Zhouyang, Guo Hainan

Journal of Theoretical and Applied Mechanics

2023

Vol. 61 nr 2

407--414

Based on a dynamic discrete model of an out-of-plane curved beam with a constant cur- vature, eigen-properties of the spatial curved beam structure in undamaged and damaged configurations are considered in this paper. In the literature, based on the equivalent sec- tion reduction method, a distributed damage modeling method is proposed. Accoding to Euler-Bernoulli beam theory, the stiffness matrix of shear, bending and torsion coupling is derived. Combined with the lumped mass matrix and the characteristic equation of the multi degree of freedom system, natural frequencies of the undamaged and damaged structures are calculated.

A BIM technology-based underwater structure damage identification and management method

Li Xiaofei, Su Rongrong, Cheng Peng, Sun Heming, Meng Qinghang, Song Taiyi, Wei Mengpu, Zhang Chen

Bulletin of the Polish Academy of Sciences. Technical Sciences

2023

Vol. 71, nr 2

art. no. e144602

With the continuous development of bridge technology, the condition assessment of large bridges has gradually attracted attention. Structural Health Monitoring (SHM) technology provides valuable information about a structure's existing health, keeping it safe and uninterrupted use under various operating conditions by mitigating risks and hazards on time. At the same time, the problem of bridge underwater structure disease is becoming more obvious, affecting the safe operation of the bridge structure. It is necessary to test the bridge’s underwater structure. This paper develops a bridge underwater structure health monitoring system by combining building information modeling (BIM) and an underwater structure damage algorithm. This paper is verified by multiple image recognition networks, and compared with the advantages of different networks, the YOLOV4 network is used as the main body to improve, and a lightweight convolutional neural network (Lite-yolov4) is built. At the same time, the accuracy of disease identification and the performance of each network are tested in various experimental environments, and the reliability of the underwater structure detection link is verified.