01661313

Component

In earthquake prone areas, transportation networks are critical lifelines in providing access to the affected communities in the aftermath of an earthquake and support response and recovery efforts. Structural damages to transportation networks can disrupt such efforts and cause substantial socio-economic and physical losses. Therefore, evaluation of the transportation network reliability is essential for stakeholders and policy makers in order to facilitate optimal decision making for mitigation, preparedness, response, and recovery practices. Several research efforts have already addressed and quantified the impact of natural disasters on transportation networks, however, existing frameworks still suffer from high computational cost and thus are of limited applicability to large and complex networks. This paper presents a straightforward framework for accelerating simulation-based stochastic assessment of post-earthquake transportation network connectivity via machine-learning-based surrogates. The present framework enables fast risk assessment and real-time risk-informed decision making for large transportation networks. The proposed approach is applied to a simulation-based study of the two-terminal connectivity of a California transportation network subject to extreme probabilistic earthquake events. Different machine-learning-based surrogate models are considered in this study, based on support vector machine, logistic regression, k-nearest neighbors, and deep neural networks. A comprehensive comparison for the performance of these surrogate models is provided. Numerical results highlight the effectiveness of the use of deep neural networks in accelerating the transportation network two-terminal reliability analysis with surrogate accuracies of more than 99%.

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications. Alternate title: Accelerating Stochastic Assessment of Postearthquake Transportation Network Connectivity via Machine Learning–Based Surrogates

18-05162

English

15p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Connectivity; Disaster preparedness; Earthquakes; Emergency management; Networks; Socioeconomic factors

California

Highways; Planning and Forecasting; Security and Emergencies

Transportation Research Board Annual Meeting 2018 Paper #18-05162

TRIS, TRB, ATRI

Jan 8 2018 11:18AM