Image-Based Modeling of Bridges and Its Applications to Evaluating Resiliency of Transportation Networks

Modern urban areas are heavily dependent on transportation networks to sustain their economic life. Hence, when vital components of a regional network are disrupted, economic losses are inevitable. As evidenced by 1989, Loma Prieta and 1994, Northridge earthquakes, the seismic damages experienced by bridges alone result in extensive traffic delays and rerouting, not only hindering emergency response but also causing indirect economic losses that far surpass the direct cost of damage to infrastructure. Nevertheless, in many areas of the U.S., transportation networks lack the resilience required to sustain the potential demands of natural hazards. Traditional hazard assessment methods, in theory, provide the tools required for predicting the vulnerabilities associated with natural hazards. Nonetheless, due to their abstractions of the complex infrastructure and the coupled regional behavior, they often fall short of that expectation. This study proposes a semi-automated image-based model generation framework for producing structure-specific models and fragility functions of bridges. The framework effectively fuses geometric and semantic information extracted from Google Street View images with centerline curve geometry, surface topology, and various relevant metadata to construct extremely accurate geometric representations of bridges. Then, using class statistics available in the literature for bridge structural properties, the framework generates structural models. Both the performance of the geometry extraction procedure and the structural modeling method proposed here are validated by comparison against the structural model of a real-life bridge developed based on as-built drawings.In principle, these models can be utilized to assess physical damage for any type of hazard, but in this study, the focus is limited to seismic applications. Thus to relate the damage resulting from seismic demands from ground shaking, bridge-specific fragility functions are developed for 100 bridge structures in the immediate surroundings of Ports of Los Angeles and Long Beach. Using these fragility curves, the physical damage resulting from a magnitude 7.3 scenario earthquake on Palos Verdes fault is predicted. Subsequently, the effects of the bridge infrastructure damage to the transportation patterns in the Los Angeles metropolitan area are investigated in terms of various resilience metrics

A Data-Driven Approach for Granular Simulation of Potential Earthquake Damage to Bridge Networks and Resulting Decreases in Mobility

Quantified investigation of resilience in regional transportation networks has been a growing research focus. Despite this increased attention, state-of-the-art studies fall short of devising and utilizing explicit transportation network models where infrastructure components (roads, bridges, etc.) and travel behaviors of network users are modeled in high fidelity. This study presents a novel model-based approach that couples a semi-automated, image-based structure-specific bridge modeling method with a metropolis-scale travel demand model towards achieving a comprehensive and high-resolution resilience assessment. As a result of its data-driven approach, the proposed method is capable of capturing and incorporating many details that are usually omitted in traditional analyses, promising improved accuracy in estimating the resilience and sustainability metrics of transportation networks. As a small-scale testbed for the proposed approach, this study displays the results of a preliminary investigation of potential seismic losses for the Los Angeles Metropolitan Area due to a hazard-consistent scenario earthquake primarily affecting the Ports of Los Angeles and Long Beach. This analysis makes use of structure-specific fragility functions of 200 bridges in the vicinity of the port facilities, generated from street-level imagery, and provides a detailed picture of the expected disruptions to truck freight mobility resulting from the scenario event

Image-Based Modeling of Bridges and Its Applications to Evaluating Resiliency of Transportation Networks

Modern urban areas are heavily dependent on transportation networks to sustain their economic life. Hence, when vital components of a regional network are disrupted, economic losses are inevitable. As evidenced by 1989, Loma Prieta and 1994, Northridge earthquakes, the seismic damages experienced by bridges alone result in extensive traffic delays and rerouting, not only hindering emergency response but also causing indirect economic losses that far surpass the direct cost of damage to infrastructure. Nevertheless, in many areas of the U.S., transportation networks lack the resilience required to sustain the potential demands of natural hazards. Traditional hazard assessment methods, in theory, provide the tools required for predicting the vulnerabilities associated with natural hazards. Nonetheless, due to their abstractions of the complex infrastructure and the coupled regional behavior, they often fall short of that expectation. This study proposes a semi-automated image-based model generation framework for producing structure-specific models and fragility functions of bridges. The framework effectively fuses geometric and semantic information extracted from Google Street View images with centerline curve geometry, surface topology, and various relevant metadata to construct extremely accurate geometric representations of bridges. Then, using class statistics available in the literature for bridge structural properties, the framework generates structural models. Both the performance of the geometry extraction procedure and the structural modeling method proposed here are validated by comparison against the structural model of a real-life bridge developed based on as-built drawings.In principle, these models can be utilized to assess physical damage for any type of hazard, but in this study, the focus is limited to seismic applications. Thus to relate the damage resulting from seismic demands from ground shaking, bridge-specific fragility functions are developed for 100 bridge structures in the immediate surroundings of Ports of Los Angeles and Long Beach. Using these fragility curves, the physical damage resulting from a magnitude 7.3 scenario earthquake on Palos Verdes fault is predicted. Subsequently, the effects of the bridge infrastructure damage to the transportation patterns in the Los Angeles metropolitan area are investigated in terms of various resilience metrics

Machine Learning for City-Scale Building Information Modeling

The speed and accuracy of seismic loss estimation are central to effective post-earthquake emergency response. Inadequate emergency response can increase the number of casualties by a maximum factor of 10, which suggests the need for research on rapid earthquake shaking damage and loss estimation. For maximum utility, these estimates need to be conducted at as fine a scale as is practically possible. To this end, we propose a framework that leverages the advantages of recent breakthroughs in remote sensing, non-linear structural dynamics and Artificial Intelligence (AI) for shaking damage to buildings at a large (regional) scale. The framework provides a method to quickly build a building inventory of a given region, which enables rapid building damage estimation based on nonlinear dynamic analyses