“…For example, predicting the risk of derailments(Liu et al, 2011; He et al, 2014;Jafarian et al, 2012;Zarembski et al, 2006); failure of the track(Jamshidi et al, 2016), earthwork(Crapper, 2014; Okada and Sugiyama, 1994), drainage(Usman et al, 2017), bridge(Yang et al, 2018), tunnels(Beard, 2010), level crossings(Berrado et al, 2010), signals(Zhang et al, 2013) and rolling stock. While all these techniques demonstrate the importance of using available datasets to assess the potential risks and predict LCC, there is a paucity of knowledge associated with decision-making when there is a lack or unavailability of data (Chen Yu, 2019;Gai et al, 2019; Lesnaik et al, 2019;Yan et al, 2019;Sasidharan et al, 2017). Various risk assessment techniques such as Monte Carlo simulation(Sasidharan et al, 2020a), Bayesian(Zhang et al, 2014), Fuzzy logic(Elcheikh et al, 2016), Petri nets(Rama et al, 2016) and fault tree analysis(Ma et al, 2014) are employed to deal with such uncertainties within infrastructure asset management practices.…”