Direct field measurement of the dynamic amplification in a bridge

Carey, Ciaran; O’Brien, Eugene J.; Malekjafarian, Abdollah; Lydon, Myra; Taylor, Su

doi:10.1016/j.ymssp.2016.08.044

Cited by 23 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…17 Subsequently, the ADR was utilized as a lifetime dynamic allowance value to investigate the dynamic impact of traffic loading on short-to-medium span concrete bridges. 18 The Gumbel distribution was demonstrated to be the best fit to the probability model of DAFs of a wide range of bridges. 19 The multivariate extreme value theory and the ADR were developed to investigate the dynamic traffic loading.…”

Section: Introductionmentioning

confidence: 96%

Probabilistic evaluation of maximum dynamic traffic load effects on cable-supported bridges under actual heavy traffic loads

Liu

Wang

2020

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

The traffic load has grown significantly in recent years, which might be a threat for the service safety of existing bridges. Thus, it is an urgent task to assess the actual traffic load effects on bridges, considering actual heavy traffic load instead of design traffic load. This study presents a framework for extrapolating maximum dynamic traffic load effects on large bridges using site-specific traffic monitoring data. The framework involves vehicle–bridge interaction analysis and probabilistic modelling of extreme values. The weigh-in-motion measurements of a busy highway in China were collected for stochastic traffic load modelling. Case studies of two long-span cable-supported bridge based on the weigh-in-motion measurements were conducted to demonstrate the effectiveness of the proposed framework. It is demonstrated that Rice’s level-crossing approach can capture both dynamic and probabilistic characteristics of the traffic load effects. The root-mean-square displacement of the cable-stayed bridge follows a C-type distribution, and the one for the suspension bridge follows an M-type distribution, which is associated with the first-order mode shapes of the two types of bridges. The amplification factors for the cable-stayed bridge and the suspension bridge are 5.9% and 3.6%, respectively. The numerical analysis indicates that the dynamic effect for extrapolation is weaker with the increase in bridge span length, but the effect of traffic volume growth will be more significant.

show abstract

Section: Introductionmentioning

confidence: 96%

Probabilistic evaluation of maximum dynamic traffic load effects on cable-supported bridges under actual heavy traffic loads

Liu

Wang

2020

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…Currently, the majority of SHM methods applicable to bridges are the approaches that directly instrument bridges. These methods generally require many vibration detection sensors to be installed at intervals all along the span of the bridge [2]. Due to the cost and time required for installation of these sensors, the direct methods are limited to use on larger bridges, and their implementation across whole networks is unfeasible [3].…”

Section: Introductionmentioning

confidence: 99%

Drive-by Bridge Health Monitoring Using Multiple Passes and Machine Learning

Malekjafarian

Moloney²,

Golpayegani

2021

Lecture Notes in Civil Engineering

Self Cite

View full text Add to dashboard Cite

This paper studies a machine learning algorithm for bridge damage detection using the responses measured on a passing vehicle. A finite element (FE) model of vehicle bridge interaction (VBI) is employed for simulating the vehicle responses. Several vehicle passes are simulated over a healthy bridge using random vehicle speeds. An artificial neural network (ANN) is trained using the frequency spectrum of the responses measured on multiple vehicle passes over a healthy bridge where the vehicle speed is available. The ANN can predict the frequency spectrum of any passes using the vehicle speed. The prediction error is then calculated using the differences between the predicated and measured spectrums for each passage. Finally, a damage indicator is defined using the changes in the distribution of the prediction errors versus vehicle speeds. It is shown that the distribution of the prediction errors is low when the bridge condition is healthy. However, in presence of a damage on the bridge, a recognisable change in the distribution will be observed. Several data sets are generated using the healthy and damaged bridges to evaluate the performance of the algorithm in presence of road roughness profile and measurement noise. In addition, the impacts of the training set size and frequency range to the performance of the algorithm are investigated.

show abstract

“…The safety of a bridge is accurately assessed when the traffic load and the capacity to carry that load are established. While the capacity of bridge structures has been extensively studied (Attard & Stewart, 1998;Darmawan & Stewart, 2007;Pines & Aktan, 2002;Stewart, 2001;Stewart & Val, 1999), there are far fewer studies about bridge traffic load (Caprani, 2010;Carey, OBrien, Malekjafarian, Lydon, & Taylor, 2017;Paeglitis & Freimanis, 2016). When the probability is low that the load effects (e.g.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Extreme Load Effects on Long-Span Bridges Using Traffic Image Data

Micu

O’Brien

Malekjafarian

et al. 2018

BJRBE

Self Cite

View full text Add to dashboard Cite

This paper proposes an algorithm for the estimation of extreme intensity of traffic load on long-span bridges. Most Weigh-in-Motion technologies do not operate in congested conditions which are the governing cases for these bridges. In the absence of Weigh-in-Motion data on the bridge itself, a correlation between vehicle weights and their lengths is established here using a (freeflowing) Weigh-in-Motion database. Photographic images of congested traffic are modelled here for three bridges using weights estimated from lengths and one year of Weigh-in-Motion data. The actual weights are taken from the Weigh-in-Motion data, and the results are compared to test the method. The gaps between vehicles are firstly set to a constant value and later to Beta-distributed values according to vehicle type. The intensity of traffic load for all pictures is calculated and compared to the loads obtained from the recorded weights. A return period of 75-year is chosen to evaluate the extreme values of intensity. The probability that intensity of load is being exceeded is obtained using normal probability paper for both recorded and simulated weights. This study demonstrates the feasibility of the proposed concept of using lengths to estimate the extreme traffic load events with acceptable accuracy.

show abstract

Direct field measurement of the dynamic amplification in a bridge

Cited by 23 publications

References 24 publications

Probabilistic evaluation of maximum dynamic traffic load effects on cable-supported bridges under actual heavy traffic loads

Probabilistic evaluation of maximum dynamic traffic load effects on cable-supported bridges under actual heavy traffic loads

Drive-by Bridge Health Monitoring Using Multiple Passes and Machine Learning

Estimation of Extreme Load Effects on Long-Span Bridges Using Traffic Image Data

Contact Info

Product

Resources

About