Identifying critical combination of roadside slopes susceptible to rainfall-induced failures

The stability of roadside slopes is vital for the smooth operation of the highway transportation system. The failure of slopes adjacent to the highway corridors disrupts the traffic flow and limits the movement of goods, workforce, and resources. Proactive rehabilitation of slopes helps to reduce the rainfall-induced failures of roadside slopes. However, all the slope segments susceptible to rainfall-induced failures cannot be rehabilitated at once due to the limited availability of rehabilitation resources in federal and state transportation agencies. This research aims to develop an approach to identify the optimal combination of slope segments that should be proactively rehabilitated to reduce the vulnerability of transportation networks when only limited slope segments can be rehabilitated. To achieve the objective, first, a probabilistic physically-based model was used to determine the location of all critical slopes in a network and their associated failure probabilities. Then, a stochastic combinatorial optimization problem was formulated with an objective function that measured the combined user and agency cost associated with rainfall-induced slope failures under different proactive rehabilitation constraints. The solution to combinatorial optimization provides the critical combination of slope segments that should be proactively rehabilitated for minimizing the impacts on the traffic and transportation agencies following rainfall-induced instabilities. The proposed approach to identify the optimal combination of critical slope segments was implemented in the transportation network of Lamar County, Texas. The proposed approach outperforms the commonly used index-based methods in the literature for identifying the critical roadside slopes susceptible to rainfall-induced failures.