Research On Vibration Characteristics Of Simply Supported Footbridge Under Random Crowd Load

China is becoming a bridge-building powerhouse.Footbridge closely related to people’s daily travel is soft and mainly loaded by pedestrians.The relevant vibration problems becomes more and more prominent.Hence,the research on the dynamic characteristics of footbridge under random crowds is important.Single-to-multiple pedestrian equivalent possibility distribution is modeled and used to predict the maximum response of bridge loaded by crowds from single pedestrian,which aims to provide effective methodology for reference to keep the bridge safe and stable and contributes to the study on the predict of bridge response.The main research and results are listed as follows:(1)Monte Carlo method is adopted to sample the characteristic parameters of step load,which helps to construct the time history samples of single-pedestrian discrete and continuous step load.Compared with the continuous step load,the discrete step load is conservative but accurate and closer to the real situation of loading process on the footbridge.(2)Based on the single-pedestrian discrete step load,multiple-pedestrian discrete step load in different walking behaviors of random groups are analyzed and compared by Monte Carlo method.The most unfavorable step load often appears when pedestrians walk side by side.(3)Based on programming,the vibration equation of the simply supported footbridge excited by the step load mentioned above is established and the theoretical footbridge displacement responses in different cases are calculated and compared in time and frequency domain.The probability distribution of the displacement peak is obtained,which helps to determine the equivalent footbridge peak response probability distribution model predicting multiple-pedestrian cases from single-pedestrian cases.(4)Midas finite element models of footbridge,on which the same cases of step load as stated above are applied,are built to be compared with the theoretical displacement responses of the programming and to prove the predictability of the equivalent footbridge peak response probability distribution model for multiple-pedestrian cases.