Systemic reliability of bridge networks with mobile sensing‐based model updating for postevent transportation decisions

Abstract

This paper proposes the upscaling of conventional individual bridge health monitoring problems into urban regions and transportation networks via mobile and smart sensing techniques together with an innovative reconnaissance procedure. The paper associates structural failure probabilities with systemic features and proposes decision criteria to optimize postdisaster actions. Twenty bridges constituting transportation network infrastructure compose the testbed region and utilize smartphone accelerometers for dynamics characterization in a vibration-based framework. In this framework, reconnaissance output serves for model development, and mobile sensor data enable finite element model updating. Structural reliability analyses merged in a chain setting generate the systemic behavior of cascaded bridge performance. Combining systemic reliability with transportation and health services demand, one can optimize the response strategies of the bridge population and strategize disaster-related decisions in a postevent assessment setting. Based on a testbed region with remote access to nearby vicinities, 18 earthquake scenarios are conducted to visualize the optimal evacuation strategies on the network, taking systemic bridge performance into consideration. Cost-free mobile sensing support adds one more fundamental information source for reducing the uncertainty of the models and, therefore, improves associated mitigation actions.

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

“…Apart from multiple techniques integrated into each other, different studies merged multiple smartphone camera data for an enhanced deformation monitoring experience [ 39 , 40 ]. Complexity was not limited to the alternative physical meaning of different sensor technologies but also the scale of the SHM problem, e.g., bridge populations [ 41 ], bridge networks, and high-level analytics, such as decision-making [ 42 ]. Figure 1 presents the timeline of these key progresses in smartphone-based SHM.…”

“…The authors expanded the smartphone clock problem into a probabilistic description and identified a Kernel distribution formulation to represent the smartphone sampling rate imperfections [ 60 ]. At the same time, the authors linked modal identification findings to calibrate bridge finite element models [ 42 ]. Stemming from the benchmark works developed for a seismic shake table test [ 61 , 62 ], the authors interpreted smartphone-based bridge SHM as a cyber-physical process and utilized model updating for structural reliability supporting decision-making [ 63 ].…”

“…Stemming from the benchmark works developed for a seismic shake table test [ 61 , 62 ], the authors interpreted smartphone-based bridge SHM as a cyber-physical process and utilized model updating for structural reliability supporting decision-making [ 63 ]. Eventually, they expanded the model-updating-based decision analysis protocol to a bridge network where they monitored 20 bridges with smartphones [ 42 ].…”

“…As can be seen above, the majority of the examples were focused on the identification stage and its representation as damage-sensitive features. A few examples extrapolated identification knowledge onto the calibration of finite element models, such as [ 74 ] or [ 63 ] and, in some exceptional cases, region-scale bridge models [ 42 ]. Not only the structural state but also the serviceability limit state attracted attention within the smartphone community with a focus on civil infrastructure.…”

Bridge Network Seismic Risk Assessment Using ShakeMap/HAZUS with Dynamic Traffic Modeling

“…Therefore, to represent the actual fragility curves, a detailed analysis is required. However, initial steps to achieve this was made in a parallel research study by the authors in [68].…”