Monitoring Infrastructure Vibrations with a Vehicle Platform: Mobile Sensing versus Conventional Accelerometers

This contribution presents an experimental study investigating the potential of mobile-sensing strategies for vibration-based identification (VBI) of infrastructures. The work focuses on the use of a small-scale vehicle as a mobile sensing unit, instrumented with both conventional accelerometers and a commercial smartphone, with the aim of assessing the feasibility of low-cost and easily deployable monitoring solutions versus professional sensing equipment. The proposed approach relies on vehicle–structure interaction: the instrumented vehicle is driven at controlled speeds over selected infrastructures in the urban area of Palermo (Italy), enabling the acquisition of dynamic response data associated with both the vehicle and the underlying structure. The accelerations recorded by piezoelectric sensors, considered as reference measurements, are complemented by those collected through the embedded sensors of the smartphone. This dual-sensing configuration allows for a systematic evaluation of sensitivity, noise performance, and the capability of extracting key vibration-based features. While the accelerometer-equipped setup demonstrates clear potential for identifying structural dynamic characteristics, the performance of the smartphone presents a more nuanced outcome. Issues related to noise, sampling stability, and environmental interference are considered, with the aim of understanding the limitations and possible preprocessing strategies required for reliable modal identification. The analysis highlights the conditions under which smartphone measurements may approach acceptable accuracy, as well as scenarios where their performance remains insufficient without additional filtering or calibration. While demonstrated on a scaled vehicle platform, the methodology is applicable to other structures and mobile sensing scenarios. Despite these open challenges, the study reinforces the promise of mobile-sensing techniques as scalable and cost-effective tools for structural diagnostics. The results contribute to the ongoing development of practical methodologies for vibration-based assessment that could, in the future, support widespread, rapid, and user-friendly structural monitoring campaigns in transportation networks.