The sustainability of urban cities is contingent upon the adequate performance of their infrastructure. The transportation system is one of the crucial components of the city infrastructure and the sustainability and the economic development of a city depend on its proper operation. Monitoring bridge structures, as key components of the transportation infrastructure, is a popular topic among researchers. While most conventional bridge monitoring methods focus on using fixed sensors, including accelerometers and strain gauges, to collect data, indirect bridge monitoring methods focus on using a moving sensor inside a vehicle as the data collector. As such, one mobile sensor can collect data from many bridges with no fixed instrumentation required, leading to a more efficient and cost-effective bridge monitoring. However, drive-by collected vibrations are significantly dominated by operational factors, including the vehicle and road features.

Recent technology developments have provided invaluable tools for monitoring urban infrastructure. Smartphones, currently the most popular smart devices, are equipped with many sensors, which data could be used for monitoring urban infrastructure through a crowdsensed framework. In this regard, this thesis aims at proposing a crowdsensing-based indirect bridge monitoring method considering operational effects. A novel inverse filtering method is proposed to eliminate operational effects from drive-by vibrations and enhance bridge feature extraction. The off-bridge vibrations, i.e., when the vehicle is not on the bridge and is moving on the ground, is employed to create a filter capable of eliminating operational effects. Later, this filter is applied to the on-bridge vibrations to magnify bridge dynamic features.

In the initial chapters of this thesis, the focus is on the frequency identification of bridges. Later, a more complex damage detection method using Mel-frequency cepstral analysis is applied in conjunction with inverse filtering. At each stage of this study, a series of controlled laboratory experiments and real-life tests are conducted to investigate the performance of the proposed method. The proposed approach is proved successful in eliminating operational effects from drive-by collected vibrations, addressing one of the major challenges facing indirect bridge monitoring. In addition, the data acquisition devices in the proposed method are passengers’ smartphones, which demonstrates the potential of implementing the proposed method in crowdsensed frameworks for future smart cities.