Abstract
Inspection and preservation of the aging bridges to extend their service life has been recognized as one of the important tasks of the State Departments of Transportation. Yet manual inspection procedure is not efficient to determine the safety status of the bridges in order to facilitate the implementation of appropriate maintenance. In this paper, a complex model involving a remotely controlled robotic platform is proposed to inspect the safety status of the bridges which will eliminate labor-intensive inspection. Mobile cameras from unmanned airborne vehicles (UAV) are used to collect bridge inspection data in order to record the periodic changes of bridge components. All the UAVs are controlled via a control station and continuously feed image data to a deep learning-based detection algorithm to analyze the data to detect critical structural components. A cellular automata-based pattern recognition algorithm is used to find the pattern of structural damage. A simulation model is developed to validate the proposed method by knowing the frequency and time required for each task involved in bridge inspection and maintenance. The effectiveness of the model is demonstrated by simulating the bridge inspection and maintenance with the proposed model for five years in AnyLogic. The simulated result shows around 80% of man-hour can be saved with the proposed approach.
Recommended Citation
M. M. Karim et al., "Modeling and Simulation of a Robotic Bridge Inspection System," Procedia Computer Science, vol. 168, pp. 177 - 185, Elsevier B.V., May 2020.
The definitive version is available at https://doi.org/10.1016/j.procs.2020.02.276
Meeting Name
Complex Adaptive Systems Conference (2019: Nov. 13-15, Malvern, PA)
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
AnyLogic; Bridge inspection; Cellular automata; Deep learning; Discrete-event simulation; Simulation modeling; UAV
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2020 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
13 May 2020
