“…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.…”