Data-Enabled Prediction Framework of Dynamic Characteristics of Rural Footbridges Using Novel Citizen Sensing Approach - Gibbs, Maria M.; Kwon, Dae Kun; Kareem, Ahsan (Frontiers in Built Environment, 2019)
Rural footbridges have proved to be an impetus for growth in vulnerable areas of the developing world, increasingly being built in many isolated communities around continents. Yet, little prior assessment of their dynamic characteristics had been made due to the non-traditional constraints that arise from instrumenting footbridges in rural, off-grid settings across multiple continents. Their characteristics remain largely unknown even if the low mass and flexible nature of rural footbridges make them vulnerable to wind-induced motions. To this end, this study proposes a data-enabled prediction framework based on a novel citizen sensing protocol, which aims at predicting the dynamic properties of rural footbridges during the conceptual design phase to enhance their safety under winds. The protocol is established which enables non-experts including local citizens in isolated communities to collect vibration data of rural footbridges by way of rapidly deployable and low-cost sensing systems in a novel application to full-scale monitoring with the concept of the community engagement. This citizen sensing data helps not only establish database with dynamic properties, but also develop empirical models to predict their dynamic properties of a footbridge in the conceptual design phase without detailed and bridge-specific dynamic modeling. In addition. a simple yet effective batch processing procedure to be done by non-experts is also devised to readily process upcoming citizen sensing data from new footbridges in the future, which offers instant and continuous updates of existing database with minimal efforts for enhancing the knowledge and the prediction of dynamic characteristics of rural footbridges.