Mobile phones with embedded sensors provide data streams that could transform the way the public interacts with infrastructure as well as the local government’s role in maintenance. With structural health monitoring tools, mobile smartphone data streams can be processed to determine the natural vibration properties of bridge’s natural vibration properties at a large scale.
Smartphone data streams for bridge health monitoring
Knowledge on the dynamic properties of bridges in a city can improve condition assessments, maintenance scheduling, and emergency planning to better serve the public. Currently, bridge vibration data is obtained primarily by researchers through the use of a sophisticated sensor network that is composed of fixed sensor nodes. Recent studies have supported the alternative of mobile sensor networks, which are capable of delivering important structural information, e.g., modal properties, requiring less setup efforts and using fewer sensors. Simultaneously, digital technology has spawned data initiatives such as crowdsensing, in which individuals can collectively sense the urban environment. The prevalence of smartphones, which contain various advanced sensors, is rapidly restructuring researchers’ perceptions of data collection. This paper discusses the confluence of these emerging technologies, which can provide regular infrastructure data streams, within structural health monitoring (SHM) procedures for the immediate goal of system identification (SID) and towards automated maintenance of bridges. Will researchers continue to install sensor networks and collect their own data or will they start to source resident smartphone data? One of the objectives of this ongoing work is to quantify expected smartphone data stream volumes that would be applicable to SHM processes. As an example, the number of smartphones that traverse the Harvard bridge in a month is quantified.
Crowdsensing Framework for Monitoring Bridge Vibrations Using Moving Smartphones
Cities are encountering extensive deficits in infrastructure service while they are experiencing rapid technological advancements and overhauls in transportation systems. Standard bridge evaluation methods rely on visual inspections, which are infrequent and subjective, ultimately affecting the structural assessments on which maintenance plans are based. The operational behavior of a bridge must be observed more regularly and over an extended period in order to sufficiently track its condition and avoid unexpected rehabilitation. Mobile sensor networks are conducive to monitoring bridges vibrations routinely, with benefits that have been demonstrated in recent structural health monitoring (SHM) research. Though smartphone accelerometers are imperfect sensors, they can contribute valuable information to SHM, especially when aggregated, e.g., via crowdsourcing. In an application on the Harvard Bridge (Boston, MA), it is shown that acceleration data collected using smartphones in moving vehicles contained consistent and significant indicators of the first three modal frequencies of the bridge. In particular, the results became more precise when informatics from several smartphone datasets were combined. This evidence is the first to support the hypothesis that smartphone data, collected within vehicles passing over a bridge, can be used to detect several modal frequencies of the bridge. The result defines an opportunity for local governments to make partnerships that encourage the collection of low-cost bridge vibration data, which can contribute to more effective management and informed decision-making.