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

Abstract

AbstractRainfall-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.