Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions

International Journal of Environmental Research and Public Health

Saeed Khalaj¹  Farshad BahooToroody¹  Gianpaolo Di Bona²  Antonio Forcina³  Leonardo Leoni⁴  Filippo De Carlo⁴ 

[1] Department of Civil Engineering, University of Parsian, Qazvin 3176795591, Iran;Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy;Department of Engineering, University of Naples “Parthenope”, 80133 Naples, Italy;Department of Industrial Engineering (DIEF), University of Florence, 50123 Florence, Italy;

关键词: geotextile-reinforced slopes;    failure modeling;    drainage system;    hierarchical Bayesian modeling;   

DOI  :  10.3390/ijerph18020373

来源: DOAJ

【 摘 要 】

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 $2.8\times {10}^{-5}$ during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods.

【 授权许可】

Unknown