Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions.

Area of Science:

• Geotechnical Engineering
• Civil Engineering
• Risk Management

Background:

• Geosynthetics, particularly geotextiles, are crucial for reinforcing slopes and improving urban geotechnical structures.
• Traditional methods for assessing geotextile-reinforced slopes (GRSs) struggle with source-to-source uncertainty, complicating failure risk evaluation.
• Catastrophic slope failures highlight the need for advanced risk assessment methodologies.

Purpose of the Study:

• To develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations on GRSs.
• To address and incorporate fluctuations and uncertainties present in event data for more accurate risk assessment.
• To provide urban designers, asset managers, and policymakers with a tool for predicting mean time to failure.

Main Methods:

• A hierarchical Bayesian approach (HBA) was employed to estimate failure probabilities of GRSs.
• Markov chain Monte Carlo simulations were utilized to incorporate uncertainties from likelihood functions and prior distributions.
• The methodology was demonstrated by analyzing the performance data of nine reinforced slopes.

Main Results:

• The HBA effectively incorporates uncertainties inherent in GRS data, leading to more realistic failure probability estimations.
• The proposed RBM methodology provides a more accurate assessment compared to traditional methods.
• The average failure probability for the studied system was calculated as 2.8×10⁻⁵ per hour over its lifespan.

Conclusions:

• The developed HBA-based RBM methodology offers a sound approach to alleviate the complexity of risk assessment for GRSs.
• This advanced method enables better prediction of failure times, optimizing maintenance schedules and enhancing safety.
• The findings support improved decision-making for the long-term stability and management of urban geotechnical infrastructure.