Biomechanically influenced mobile and participatory pedestrian data for bridge monitoring

Abstract

Future structural health monitoring systems are evolving toward crowdsourced, autonomous, sustainable forms based on which damage-indicative structural features can be identified. Unlike conventional sensor systems, they serve as nonstationary, mobile, and distributed sensor network components. For example, smartphone sensors carried by pedestrians decouple from the structure of interest, making it difficult to measure structural vibration. Taking bridges as instances, smartphone sensor data contain not only the bridge vibration but also the pedestrians' biomechanical features. In this article, pedestrians' smartphone data are used to conduct force estimation and modal identification for structural health monitoring purposes. Two major pedestrian activities, walking and standing, are adopted to estimate walk-induced forces on structures and identify modal parameters, respectively. First, vibration time history of a walking pedestrian combined with pedestrian weight is a measure of dynamic forces imposed on the structure. Second, standing pedestrian's smartphone sensors provide spectral peaks which are mixtures of structural and biomechanical vibrations. Eliminating biomechanical content reveals structural modal properties which are sensitive to structural integrity. This study presents the first structural health monitoring application recruiting pedestrians in a testbed bridge monitoring example. Orchestrating pervasive and participatory pedestrian data might bring new frontiers to structural health monitoring through a smart, mobile, and urban sensing framework.

Structural Reliability Estimation with Participatory Sensing and Mobile Cyber-Physical Structural Health Monitoring Systems

“…Previously, it has been shown that actual crowdsourcing results (results from uncontrolled citizens) matched well with the reference identification results [32]. These uncertainties are extensively studied in [48] including spatiotemporal variation of a citizen sensor [33], phone orientation which is subjected to change before, during, and after the measurements [34], and biomechanical distortions caused by human nature [50]. Therefore, in this paper, the main focus is on uncertainties induced by ground motions and FE model parameters.…”

Smartphone Zombie Context Awareness at Crossroads: A Multi-Source Information Fusion Approach

“…Since there is a buffer zone in our position judgment, the positioning accuracy almost meets the demand. But when it comes to mass usage, embedded Geofencing tools in IOS or Android, which can provide a more accurate location by fusing multiple positioning approaches [63], would be a better choice. For other sensors, the accuracy is affected by sensor heterogeneity and pedestrian biomechanical effects.…”

Multi‐output modal identification of landmark suspension bridges with distributed smartphone data: Golden Gate Bridge

“…In fact, a number of studies have shown the feasibility of smartphone accelerometer applications with civil infrastructure examples. [6][7][8][9][10][11][12][13][14][15][16] One significant challenge, however, is whether these systems can be used for very low frequency structures such as long-span bridges. In addition to the measurement limitations of smartphone-embedded MEMS accelerometers for very low frequency motion, smartphone sensors and mobile applications, by default, are embedded with high pass filters.…”