Structure Design of Cable-Stayed Bridge Intelligent Detection Robot

He, Hongyu; Hu, Guichuan; Ren, Jian; Hu, Jing; Zhao, Hui; Nie, Tao

doi:10.1088/1755-1315/632/2/022061

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…They solved a set of linear equations and obtained the coordinates of the free nodes in the cable network as the shape of the cable network. The authors found that when the force density method was applied to see the formation of the flexible boundary cable network, an appropriate iterative strategy could make the internal force of the side cable very uniform [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Implementation and Optimization of Reverse Suspension Structure Design Model Using Deep Learning

Wang

Wang³

2022

Computational Intelligence and Neuroscience

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The present work aims to improve the design efficiency and optimize the results in the increasingly complex and diversified material design projects to help architects realize the better performance of building structures. According to the characteristics of comprehensive perception and intelligent processing of the Internet of Things, a reverse suspension structure design model is constructed based on the finite element method and simulated annealing algorithm. Besides, deep learning is adopted to train complex functions for performance correction and to optimize the plane structure of shell structure. Moreover, the force is transformed into shape, and the form-finding process is completed to facilitate the operation of designers. Finally, the spatial anchoring ability of the geographic information system is used to match and calculate the relevant characteristics of spatial elements. On this basis, the index construction strategy based on weight distribution is employed to realize the data fusion diagnosis framework and enhance the intelligence of architectural design. The simulation results show that the maximum tensile stress of the physical suspension experiment is 3.71 MPa and the maximum compressive stress is 14.7 MPa. The compressive stress value is much larger than the tensile stress value. The maximum deformation value’s difference between the compressive and tensile stress is 0.07 and 0.11, respectively. The error is within the acceptable range, which is similar to the compression state results obtained from the actual suspension physical experiment, indicating that the initial design model of the reverse suspension structure based on deep learning is reliable. In addition, the evolutionary optimization effect analysis results demonstrate that the load of the design structure is relatively uniform, which verifies the feasibility of the algorithm reported here. The research significance of the reverse suspension structure model constructed here is to provide an accurate and feasible design idea for the reverse design of some complex structures in the building suspension. It can also shorten the creation and improvement cycle of this kind of structure and optimize the performance and construction cycle of the building structure.

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Section: Related Workmentioning

confidence: 99%

Implementation and Optimization of Reverse Suspension Structure Design Model Using Deep Learning

Wang

Wang³

2022

Computational Intelligence and Neuroscience

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Design and Control Method of a Coupled Loading–Damping Mechanism of Cable‐Detecting Robots for Large Bridges

Xu,

Ma,

Zhou

et al. 2024

Journal of Field Robotics

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The climbing stability of cable‐detecting robots holds important significance for bridge detection. To improve the climbing stability of robots under cable vibration, a loading mechanism coupling variable stiffness and damping and a control method were proposed for robots. At first, aiming at the slip of cable‐detecting robots during climbing, the influences of cable vibration on the climbing stability of the robot and the damping mechanism of the variable‐stiffness and variable‐damping mechanism were assessed. Then, a new coupled loading mechanism was designed, and a mechanical model was established. The damping and stiffness were adjusted according to cable vibration, thus automatically adjusting the loading force of the robot. Afterwards, a fuzzy proportional–integral–derivative (PID) control strategy was devised and PID parameters were adjusted in accordance with cable vibration, thereby dynamically adjusting the clamping force of the coupled loading mechanism. Finally, a laboratory vibration test platform was established to conduct experiments on the output force of the coupled loading mechanism and on the robot climbing under vibration. Experimental results show that the maximum fluctuation amplitude of the climbing speed of the proposed robot decreases to 0.018 m/s, and the speed stability improves by 78.9% at the cable‐vibration frequency of 10 Hz and amplitude of 4 mm, when compared with the original common helical‐spring loading mechanism.

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Research on Intelligent Detection Technology for Highway and Bridge Engineering

Meng

2024

Lecture Notes in Civil Engineering

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Bridges are a crucial component of infrastructure, and the detection of their structure and health status has always been a focus of attention in the engineering field. However, there are a series of challenges in the application of traditional bridge detection methods in complex environments, especially in fine detection of special areas, which usually only rely on manual visual inspection, which limits the ability to comprehensively understand the health status of bridges. In response to this issue, research and summary on intelligent detection technology and equipment for highway bridges indicate that timeliness and accuracy are key factors in the process of bridge health diagnosis, evaluation, and maintenance. In modern engineering practice, intelligent detection technology and equipment have been widely applied, and rapid development has made machine vision based detection technology the main means. This trend has brought significant improvements to bridge inspection, and the introduction of various non-destructive testing technologies has provided diversified data support for the inspection process, making the inspection results more comprehensive and reliable. High performance intelligent detection equipment has emerged in this context, with its ability to carry various advanced sensors gradually covering various bridge detection needs.

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Structure Design of Cable-Stayed Bridge Intelligent Detection Robot

Cited by 3 publications

References 3 publications

Implementation and Optimization of Reverse Suspension Structure Design Model Using Deep Learning

Implementation and Optimization of Reverse Suspension Structure Design Model Using Deep Learning

Design and Control Method of a Coupled Loading–Damping Mechanism of Cable‐Detecting Robots for Large Bridges

Research on Intelligent Detection Technology for Highway and Bridge Engineering

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