A Stochastic Modelling of the Dynamical Response of Highway Bridge Decks Under Traffic Loads

Almeida, Ricardo Santos de; Silva, José Guilherme S. da

doi:10.1007/1-4020-5370-3_739

Cited by 3 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, the adopted vehicle mathematical model was developed by Almeida [8,9]. This model is considered discrete and constituted by "mass-spring-damper" systems with three masses and a four freedom degree.…”

Section: Vehicle Modelmentioning

confidence: 99%

Fatigue Analysis of Composite Highway Bridge Decks

Leitão¹,

Silva²,

Andrade³

2014

Proceedings of 10th World Congress on Computational Mechanics

View full text Add to dashboard Cite

show abstract

Section: Vehicle Modelmentioning

confidence: 99%

Fatigue Analysis of Composite Highway Bridge Decks

Leitão¹,

Silva²,

Andrade³

2014

Proceedings of 10th World Congress on Computational Mechanics

View full text Add to dashboard Cite

show abstract

“…Figure 5 illustrates this representation and uses the following notation: u v , is the vehicle vertical displacement of the suspended mass; θ s , is the vehicle rotational displacement related to the suspended mass; u 1 and u 2 are the vehicle vertical displacements of the non-suspended mass; m s is the vehicle suspended mass; m ns is the vehicle i th non-suspended mass (for each axle), respectively; k vs and k vp are the i th stiffness coefficients related to vehicle suspension and tires (for each axle), respectively; c vs and c vp are i th the damping coefficients related to vehicle suspension and tires (for each axle), respectively. The vehicle natural frequencies oscillating on rigid base (vertical motion), corresponding to the vehicle suspended mass (suspension system) and non-suspended mass degrees of freedom (tires), as presented in Figure 5, were made equal to 3.0Hz and 20.0Hz, respectively [8,9]. However, the vehicle model has a lower natural frequency, associated to the vehicle suspended mass (suspension system) degree of freedom which is equal to 2.3Hz (rotational motion).…”

Section: Vehicle Modelmentioning

confidence: 99%

“…The relative damping coefficient of the vehicles vibration mode with predominant movement of the vehicles suspended mass is assumed to be ξ=0.1 (ξ=10%) [8,9]. The total mass used in this vehicle model is equal to 12t (m s = 10.667kg; m ns1 = 666.5kg and m ns1 = 666.5kg), corresponding to 120kN weight.…”

Section: Vehicle Modelmentioning

confidence: 99%

“…The total mass used in this vehicle model is equal to 12t (m s = 10.667kg; m ns1 = 666.5kg and m ns1 = 666.5kg), corresponding to 120kN weight. The relation between the suspended mass and non-suspended masses was considered equal the 8.0 [8,9]. a) Vehicle "TB-12" [10] b) Mathematical model [8,9] …”

Section: Vehicle Modelmentioning

confidence: 99%

“…The relation between the suspended mass and non-suspended masses was considered equal the 8.0 [8,9]. a) Vehicle "TB-12" [10] b) Mathematical model [8,9] …”

Section: Vehicle Modelmentioning

confidence: 99%

See 2 more Smart Citations

Fatigue analysis and life prediction of composite highway bridge decks under traffic loading

Silva

2013

Lat. Am. j. solids struct.

View full text Add to dashboard Cite

Steel and composite (steel-concrete) highway bridges are currently subjected to dynamic actions of variable magnitude due to convoy of vehicles crossing on the deck pavement. These dynamic actions can generate the nucleation of fractures or even their propagation on the bridge deck structure. Proper consideration of all of the aspects mentioned pointed our team to develop an analysis methodology with emphasis to evaluate the stresses through a dynamic analysis of highway bridge decks including the action of vehicles. The design codes recommend the application of the curves S-N associated to the Miner's damage rule to evaluate the fatigue and service life of steel and composite (steel-concrete) bridges. In this work, the developed computational model adopted the usual mesh refinement techniques present in finite element method simulations implemented in the ANSYS program. The investigated highway bridge is constituted by four longitudinal composite girders and a concrete deck, spanning 40.0m by 13.5m. The analysis methodology and procedures presented in the design codes were applied to evaluate the fatigue of the bridge determining the service life of the structure. The main conclusions of this investigation focused on alerting structural engineers to the possible distortions, associated to the steel and composite bridge's service life when subjected to vehicle's dynamic actions.

show abstract

In‐fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data

Shokravi,

Vafaei,

Samali

et al. 2024

Computer aided Civil Eng

View full text Add to dashboard Cite

Drive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality of connected and autonomous vehicles (CAVs) into drive‐by damage detection by introducing In‐Fleet SHM. To the authors’ knowledge, this is the first study that proposes using CAVs for SHM application in civil engineering structures. Each In‐Fleet CAV could automatically collect the vehicle's persistent and temporal data by the embedded sensors and transmit them to edge computing systems for analysis. These persistent data include type and model and temporal parameters encompassing position, speed, heading, and vertical acceleration of CAVs. Knowing the persistent and temporal data of the passing vehicles over the transportation infrastructures enables the identification of the dynamic parameters of the bridge from the vehicles’ vertical acceleration response using drive‐by techniques and, on the other hand, reconstruction of the finite element model of the passing vehicles over the supporting bridges in a near real‐time manner. In contrast to the drive‐by SHM, In‐Fleet monitoring has an expanded spatial and temporal coverage, enabling continuous near real‐time monitoring of highway bridges of the transportation network. The accuracy and resolution of the identified modal components in In‐Fleet SHM are enhanced due to the crowdsensing nature of the collected data. Furthermore, by offering a unique set of characteristics, this method fills the crucial gap in implementing Industry 4.0 technologies and digital twins for SHM of bridges.

show abstract

A Stochastic Modelling of the Dynamical Response of Highway Bridge Decks Under Traffic Loads

Cited by 3 publications

References 2 publications

Fatigue Analysis of Composite Highway Bridge Decks

Fatigue Analysis of Composite Highway Bridge Decks

Fatigue analysis and life prediction of composite highway bridge decks under traffic loading

In‐fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data

Contact Info

Product

Resources

About