An Intelligent Analysis Method for Human-Induced Vibration of Concrete Footbridges

Abstract

It is essential to reliably predict the human-induced vibrations in serviceability design of footbridges to ensure the vibration levels to be within the acceptable comfort limits. The human-induced structural responses are dependent on the dynamic properties of structures and human-induced excitations. For concrete footbridges, the elastic modulus of concrete is a vital parameter for determining the dynamic structural properties. To this end, a two-stage machine learning (ML)-based method is first proposed for modeling the elastic modulus of concrete. At the first stage, the ensemble algorithm, i.e. the gradient boosting regression tree (GBRT), is used to predict the compressive strength by selecting eight parameters, including concrete ingredients and curing time, as the inputs. At the second stage, the elastic modulus of concrete is modeled by using the GBRT method with the compressive strength as the input. Pedestrian crowd-induced load is the most common and crucial design load for footbridges. To consider the inter- and intra-subject variability in walking parameters and induced forces among persons in a crowd, a load model is developed by associating a modified social force model with a walking force model. By integrating the two submodels of structure and excitation, an intelligent analysis method for human-induced vibration is finally developed. A concrete footbridge with typical box cross-section subjected to human-induced excitation is analysed to illustrate the application of the proposed method.

Uncertainty quantification and propagation of crowd behaviour effects on pedestrian-induced vibrations of footbridges

“…Even for a single person, he/she can also walk in different ways and thus results in different walking parameters and loads. This is the intra-person variabilit y (Živanović et al 2005 andFu andWei 2020). To consider the inter-and intra-person variabilit y in walking parameters and induced forces among pedestrians in a crowd, the pedestrian crowd model is required to consider the individual and microscopic crowd behaviour.…”

“…To consider the inter-and intra-person variabilit y in walking parameters and induced forces among pedestrians in a crowd, the pedestrian crowd model is required to consider the individual and microscopic crowd behaviour. Furthermore, to reliably predict the pedestrian-induced vibrations of footbridges, it also requires knowing relevant crowd behaviour such as passing trajectories and speeds of each pedestrian in the crowd (e.g., Wei et al 2017 andFu andWei 2020). When passing on a structure, the crowd behaviour is influenced not only by their personal motivation and psychological effects, but also the interactions with their surroundings, i.e.…”

“…Due to its ease of use and its effectiveness to capture the main characteristics of crowd behaviour, the social force model becomes one of the most widely applied models to simulate pedestrian crowd behaviour on pedestrian structures (Helbing et al 1995, 2000a, 2005, Wei et al 2017and Fu and Wei 2020. The basic idea is that each person (represented by a randomly selected pedestrian 𝛼) in a crowd is guided by the social force composed of three force items.…”

“…The response of the footbridge is governed by not only the structure dynamic model but also the crowd-induced load (Wei et al 2017 andFu andWei 2020). Sources of uncertainties exist in the prediction of pedestrian-induced vibrations of footbridges both for the structure and excitation parts.…”

“…To consider the randomness of arrival times, a Poisson distribution is generally employed (Živanović 2012). The importance of considering the inter-and intra-subject variabilities in walking speeds, walking trajectories, step frequencies, and body weights in the prediction of structural responses are investigated by recent research (e.g., Wei et al 2017 andFu andWei 2020). Results also show it is essential to consider crowd behaviour effects in the prediction of pedestrian-induced vibrations.…”