Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions

Farshad BahooToroody, Saeed Khalaj, Leonardo Leoni, Filippo De Carlo, Gianpaolo Di Bona and Antonio Forcina
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Farshad BahooToroody: Department of Civil Engineering, University of Parsian, Qazvin 3176795591, Iran
Saeed Khalaj: Department of Civil Engineering, University of Parsian, Qazvin 3176795591, Iran
Leonardo Leoni: Department of Industrial Engineering (DIEF), University of Florence, 50123 Florence, Italy
Filippo De Carlo: Department of Industrial Engineering (DIEF), University of Florence, 50123 Florence, Italy
Gianpaolo Di Bona: Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy
Antonio Forcina: Department of Engineering, University of Naples “Parthenope”, 80133 Naples, Italy

IJERPH, 2021, vol. 18, issue 2, 1-12

Abstract: 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 × 10 − 5 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods.

Keywords: geotextile-reinforced slopes; failure modeling; drainage system; hierarchical Bayesian modeling (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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