Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions

Geosynthetics are extensively utilized to improve the stability of geotechnical structures
and slopes in urban areas. Among all existing geosynthetics, geotextiles are widely used to reinforce
unstable slopes due to their capabilities in facilitating reinforcement and drainage. To reduce
settlement and increase the bearing capacity and slope stability, the classical use of geotextiles in
embankments has been suggested. However, several catastrophic events have been reported, including
failures in slopes in the absence of geotextiles. Many researchers have studied the stability of
geotextile-reinforced slopes (GRSs) by employing different methods (analytical models, numerical
simulation, etc.). The presence of source-to-source uncertainty in the gathered data increases the complexity
of evaluating the failure risk in GRSs since the uncertainty varies among them. Consequently,
developing a sound methodology is necessary to alleviate the risk complexity. Our study sought
to develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance
operations by addressing fluctuations that accompany event data. For this purpose, a hierarchical Bayesian approach (HBA) was applied to estimate the failure probabilities of GRSs. Using Markov chain Monte Carlo simulations of likelihood function and prior distribution, the HBA can incorporate the aforementioned uncertainties. The proposed method can be exploited by urban designers, asset managers, and policymakers to predict the mean time to failures, thus directly avoiding unnecessary maintenance and safety consequences. To demonstrate the application of the proposed methodology, the performance of nine reinforced slopes was considered. The results indicate that the average failure probability of the system in an hour is 2.8 105 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods.