Structural Health Monitoring 2019 2019
DOI: 10.12783/shm2019/32380
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A Decision Support System for Scour Management of Road and Railway Bridges Based on Bayesian Networks

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Cited by 5 publications
(5 citation statements)
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“…The introduction of probabilistic methods may be an important consideration because it is a good way to cover the uncertainty of the actual development and maintenance of the structure in the decision-making scheme, as such uncertainty cannot be completely eliminated [134]. Also, the probabilistic approach allows the obtained information to be scalable, so that the limited SHM information can be utilized well, leading to the monitoring of cost reduction [140]. In addition, event trees may be a commonly used decision-making tool [134,141].…”
Section: The Design Of Decision-making Support Frameworkmentioning
confidence: 99%
“…The introduction of probabilistic methods may be an important consideration because it is a good way to cover the uncertainty of the actual development and maintenance of the structure in the decision-making scheme, as such uncertainty cannot be completely eliminated [134]. Also, the probabilistic approach allows the obtained information to be scalable, so that the limited SHM information can be utilized well, leading to the monitoring of cost reduction [140]. In addition, event trees may be a commonly used decision-making tool [134,141].…”
Section: The Design Of Decision-making Support Frameworkmentioning
confidence: 99%
“…Through collaborative efforts, solutions can be developed that address immediate needs and long-term challenges, enhancing resilience and durability. Integrating climate change considerations into flood estimation is increasingly essential to strengthen the resilience of scour protection systems (Maroni et al 2023). This integration relies on historical flood data and return period analysis.…”
Section: Consideration For the Redesign Of The Bridges To Eliminate T...mentioning
confidence: 99%
“…The decision-making at the network level is governed by multiple bridge performance indicators such as structural health of the bridge, safety and security of users and workers, environmental impact, socio-economic impact on the users, and impact on agency's reputation (Allah Bukhsh, Stipanovic, Klanker, O'Connor, & Doree, 2019;Frangopol, Dong, & Sabatino, 2017;Yavuz, Attanayake, & Aktan, 2017). The decision-making has been supported by academic research that employed different techniques to predict the bridge condition using Markov chains (Le & Andrews, 2013), Petri-net (Yianni, Rama, Neves, Andrews, & Castlo, 2017), analytical hierarchy process (Rashidi et al, 2016), Monte Carlo simulations (Frangopol, Kallen, & Noortwijk, 2004), Bayesian (Maroni et al, 2019) and normal regressions (Babanajad et al, 2018). Different BMSs are also employed to aid decision-makers to keep bridges in an acceptable level of service within the transport networks.…”
Section: Bridge Locationmentioning
confidence: 99%