Genetic algorithm optimization and sensitivity analysis of bicycle characteristics for vibration monitoring

It is essential for sustainable cities to promote cycling as one of the modes of urban transport. One significant obstacle to this is the discomfort and safety risks caused by direct impacts of inadequate pavement maintenance. The objective of this study is to investigate the impact of the dynamic characteristics of bicycles on vibration data collected for the purpose of monitoring pavement conditions. To this end, experimental tests and numerical simulations have been employed to gain insight into the influence of these characteristics on vibration measurements. In the experimental setup, a smartphone-equipped bicycle collected vertical accelerations at various speeds. An advanced laser profiler and Laser Crack Measurement System (LCMS) modeled the pavement surface along the travel path. The data were synchronized and compared with numerical simulations to calibrate the bicycle model using a genetic algorithm, which then calculated changes in vibration output due to variability in the bicycle's primary dynamic characteristics. This research establishes a comprehensive framework for modeling bicycle dynamics, enhancing the analysis of vibration data across different bicycles. A sensitivity analysis was conducted to explore how variations in bicycle characteristics affect the accuracy of vibration responses. The results indicate that positional variability, speed, and localized high-severity distress along the bicycle path significantly influence the outcome. These findings underscore that while the intrinsic characteristics of bicycles are crucial, the correct selection of bicycle categories and meticulous data cleaning are essential for collecting consistent vibration data through multiple runs, facilitating effective crowdsourcing for pavement condition monitoring.