An efficient parallel computing method for random vibration analysis of a three-dimensional train-track-soil coupled model using Seed-PCG algorithm

A Proactive Decision-Making Model for Evaluating the Reliability of Infrastructure Assets of a Railway System

“…A proper diagnostic study, a retrospective assessment for finding vulnerabilities, and a predictive analysis practice are needed for proactive failure prevention in the rail system and network [6] and for addressing reoccurrences of adverse system failure by tracking the causal relationships of the failure from the origin [7]. Some of these practices, however, have been grouped into the following themes: the remaining service life study [8], reliability-centered maintenance and the digital twin approach for detecting faults in some of the key rail infrastructures [9,10], and structural response to random vibration due to the movement of the train [11][12][13]. Finally, the root cause of failure in some selected subsystems and components of the railway infrastructure assets [14].…”