Visual Data-Driven Digital Twin Modeling Framework for Improving the Resilience of Urban Drainage Infrastructure Systems

Understanding the capacity of urban stormwater infrastructure systems before the disaster and evaluating the impact of their possible disruption during the disaster is essential to assess the vulnerability and resilience of interconnected infrastructure systems in urban communities. This paper proposes a novel digital twin modeling framework to identify the up-to-date condition of urban drainage infrastructure systems and then map it into the virtual model for risk analysis. Building upon the two different sources of images—(1) participatory sensing obtained from mobile devices; and (2) large-scale publicly available street-level visual dataset—we first detect the drainage infrastructure in cities and then estimate their geographic location and as-is condition information through deep learning architecture. The identified spatio-temporal information is then fetched into the associated virtual replica toward a digital twin modeling, and finally the associated risk is analyzed. Case studies were conducted in Houston, TX, which demonstrates the update of infrastructure condition and mapping, eventually evaluating the vulnerability of urban stormwater infrastructure systems in Houston, TX. The proposed digital twin approach for urban drainage infrastructure systems has the great potential to improve proactive hazard mitigation and data-driven decision-making in prioritizing the infrastructure improvements for a resilient built environment.