Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103178
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Title: Simulation-based planning of concrete bridge deck inspection with non-destructive technologies
Authors: Abdelkhalek, S 
Zayed, T 
Issue Date: Nov-2020
Source: Automation in construction, Nov. 2020, v. 119, 103337
Abstract: Inspection process provides beneficial information to assess the current condition of the bridge deck, which is an essential factor in identifying the most appropriate and timely maintenance strategy. Practically, there are a huge number of bridges that need frequent inspection, which makes planning for this process crucial. In this regard, a bridge deck inspection planning (BDIP) model was developed to manage the inspection process of the concrete bridge deck. This model integrates agent-based and discrete event simulation approaches to mimic the sub-processes involved in the inspection using non-destructive technologies. This paper discusses the details and the adopted procedures to build this model starting from real system analysis to model verification, implementation, and validation. Verification and validation of the model demonstrate its ability to accurately estimate inspection duration and cost. The developed model can assist bridge authorities and contractors in planning this process, measure the traffic disorder due to inspection activities, and compare different scenarios for conducting inspection.
Keywords: Agent-based simulation
Concrete bridge deck
Discrete event simulation
Hybrid simulation
Inspection
Non-destructive technologies
Planning
Publisher: Elsevier BV
Journal: Automation in construction 
ISSN: 0926-5805
EISSN: 1872-7891
DOI: 10.1016/j.autcon.2020.103337
Rights: © 2020 Elsevier B.V. All rights reserved.
© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
The following publication Abdelkhalek, S., & Zayed, T. (2020). Simulation-based planning of concrete bridge deck inspection with non-destructive technologies. Automation in Construction, 119, 103337 is available at https://doi.org/10.1016/j.autcon.2020.103337.
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