A Data Integration Approach for Assessment of Rainfall-Induced Slope Failure Susceptibility

Baral, Anil; Shahandashti, Mohsen

doi:10.1061/9780784483954.050

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Frequent and intense rainfall events, which are becoming increasingly common with climate change, have threatened the stability of roadside slopes that are crucial for the effective functioning of the road transportation system [37]. Failure of roadside slopes reduces the transportation system efficiency by detours, traffic backups, delays, and additional costs in fuel [2,3]. The transportation agencies must further deal with the financial burden of slope restorations.…”

Section: Introductionmentioning

confidence: 99%

Risk-averse rehabilitation decision framework for roadside slopes vulnerable to rainfall-induced geohazards

Baral

Shahandashti

2022

J Infrastruct Preserv Resil

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Rainfall-induced slope failures disrupt the traffic and warrant urgent slope repair works. The impact of roadside slope failures can be minimized if slopes are proactively rehabilitated. Nonetheless, transportation agencies are constrained in their budget to rehabilitate a limited number of slope segments due to competing maintenance needs among different transportation assets. Therefore, the transportation agencies should identify the critical slope combination that should be proactively rehabilitated under constraint budgets to lessen the impact on the transportation network during extreme rainfall events. The decision-making approach for slope rehabilitation should also ensure low risk associated with the selected rehabilitation strategy. Current slope-rehabilitation decision models do not consider the risk associated with the rehabilitation strategies in the decision-making process. The objective of this study is to develop a risk-averse stochastic combinatorial optimization to facilitate the selection of slope rehabilitation strategies, which leads to the least expected cost and conditional value at risk (CVaR) for extreme rainfall events. The simulated annealing approach is used to solve the risk-averse combinatorial optimization rehabilitation problem with the objective function that measures the total cost of traffic disruption and slope restoration post-failures. The approach is demonstrated using a transportation network in Lamar County, Texas. Unlike a genetic algorithm-based approach in the literature that yields a single slope rehabilitation strategy, the proposed risk-averse simulated annealing approach identifies rehabilitation strategies along the Pareto efficient frontier facilitating the rehabilitation decisions based on the tradeoff between expected cost and CVaR. For the network in Lamar County, the proposed risk-averse simulated annealing provided a solution in the Pareto front that reduced CVaR by 2.0% compared to the solution obtained from the genetic algorithm-based approach while only increasing the expected cost by 0.8%. The risk-averse optimization approach will aid transportation agencies in determining slope rehabilitation strategies for minimizing the impact of rainfall-induced failures at appropriate risk aversion levels.

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Section: Introductionmentioning

confidence: 99%

Risk-averse rehabilitation decision framework for roadside slopes vulnerable to rainfall-induced geohazards

Baral

Shahandashti

2022

J Infrastruct Preserv Resil

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Detection and Classification of Vegetation for Roadside Vegetation Inspection and Rehabilitation Using Deep Learning Techniques

Baral

Nasr

Darghiasi

et al. 2022

International Conference on Transportation and Development 2022

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Deep Learning for Exploring the Relationship Between Geotechnical Properties and Electrical Resistivities

Zamanian,

Asfaw,

Chavda

et al. 2024

Transportation Research Record: Journal of the Transportation R

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Electrical resistivity imaging is gaining popularity in aiding the characterization of subsurface conditions and assessment of the stability of earth materials. Nevertheless, it remains challenging to identify the relationship between geotechnical properties and electrical resistivities because of their nonlinear and complex relationship. This study intends to assess the application of the deep learning model to explore the relationship between electrical resistivities and geotechnical properties of natural clayey soils. A full factorial design was used to study the effects of water content and dry unit weight on the electrical resistivities of soils composed of different fractions of fine and clay particles. A deep learning model with three hidden layers was trained using a dataset comprising 842 observations to investigate the association between electrical resistivities and geotechnical properties. Influencing geotechnical properties were identified by Spearman’s correlation and feature importance. The results show that most variabilities in the electrical resistivity can be explained by the water content and dry unit weight. The results also show that the plasticity index and fine fraction play a more substantial role in predicting the electrical resistivities of clayey soils than the liquid limit and clay fraction. A comparison between the accuracy metrics of the deep learning model with existing models in the literature shows that deep learning outperforms other models in discovering nonlinear and complex relationships between electrical resistivities and geotechnical properties. Enhanced knowledge of the relationship between geotechnical properties and electrical resistivities allows for better characterizing the subsurface conditions to improve reliability and reduce uncertainties caused by inadequate subsurface information.

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A Data Integration Approach for Assessment of Rainfall-Induced Slope Failure Susceptibility

Cited by 3 publications

References 17 publications

Risk-averse rehabilitation decision framework for roadside slopes vulnerable to rainfall-induced geohazards

Risk-averse rehabilitation decision framework for roadside slopes vulnerable to rainfall-induced geohazards

Detection and Classification of Vegetation for Roadside Vegetation Inspection and Rehabilitation Using Deep Learning Techniques

Deep Learning for Exploring the Relationship Between Geotechnical Properties and Electrical Resistivities

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