Resilience-Driven Road Network Retrofit Optimization Subject to Tropical Cyclones Induced Roadside Tree Blowdown

This article focuses on decision making for retrofit investment of road networks in order to alleviate severe consequences of roadside tree blowdown during tropical cyclones.The consequences include both the physical damage associated with roadside trees and the functional degradation associated with road networks.A trilevel,two-stage,and multiobjective stochastic mathematical model was developed to dispatch limited resources to retrofit the roadside trees of a road network.In the model,a new metric was designed to evaluate the performance of a road network;resilience was considered from robustness and recovery efficiency of a road network.The proposed model is at least a nondeterministic polynomialtime hardness(NP-hard)problem,which is unlikely to be solved by a polynomial time algorithm.Pareto-optimal solutions for this problem can be obtained by a proposed trilevel algorithm.The random forest method was employed to transform the trilevel algorithm into a singlelevel algorithm in order to decrease the computation burden.Roadside tree retrofit of a provincial highway network on Hainan Island,China was selected as a case area because it suffers severely from tropical cyclones every year,where there is an urgency to upgrade roadside trees against tropical cyclones.We found that roadside tree retrofit investment significantly alleviates the expected economic losses of roadside tree blowdown,at the same time that it promotes robustness and expected recovery efficiency of the road network.

A Probabilistic Approach to the Evaluation of Seismic Resilience in Road Asset Management

Road networks are classified as critical infrastructure systems.Their loss of functionality not only hinders residential and commercial activities,but also compromises evacuation and rescue after disasters.Dealing with risks to key strategic objectives is not new to asset management,and risk management is considered one of the core elements of asset management.Risk analysis has recently focused on understanding and designing strategies for resilience,especially in the case of seismic events that present a significant hazard to highway transportation networks.Following a review of risk and resilience concepts and metrics,an innovative methodology to stochastically assess the economic resources needed to restore damaged infrastructures,one that is a relevant and complementary element within a wider resilience-based framework,is proposed.The original methodology is based on collecting and analyzing ex post reconstruction and hazard data and was calibrated on data measured during the earthquake that struck central Italy in 2016 and collected in the following recovery phase.Although further improvements are needed,the proposed approach can be used effectively by road managers to provide useful information in developing seismic retrofitting plans.