Design life of rigid pavements under dynamic wheel loads

Bayraktarova, Kristina; Eberhardsteiner, Lukas; Aichinger, Claus; Spielhofer, Roland; Blab, Ronald

doi:10.1080/14680629.2022.2136579

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…The transfer function are estimated using the Box-Jenkins method [6], then the physical parameters θ = {a 1 , H, I yy } are recovered according to the methods discussed in Section IV-A. To characterize the quality of the estimation, we look into median values of the estimated parameters (Mdn) 1 , their normalized errors (E), and their normalized Root Mean Square Error (RMSE) for the 1000 simulations. Normalized error and normalized RMSE are expressed as percentages and are defined as:…”

Section: Validation Of the Estimation Methodsmentioning

confidence: 99%

“…When estimating a BJ model, the noise model G(z) is also estimated, so we only need to correctly guess its order. Since the noise here is the sum of an integrated white noise 1 1−z −1 e 2 (k) and an other integrated white noise passing trough the system 1 1−z −1 P (z)e 1 (k), the order of G(z) can be chosen one order higher than P (z).…”

Section: B Model Structurementioning

confidence: 99%

“…The wear and tear of the road due to heavy traffic is mainly related to the load on each wheel and each axle of the vehicle rather than to the overall vehicle weight [1] [2]. For this reason, it is of practical interest to estimate such load during the different phases of heavy vehicle operations, considering unloading and distribution routines, even if a weighing scale is not available.…”

Section: Introductionmentioning

confidence: 99%

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A system identification approach to center of mass estimation in trucks

Benadada,

Massioni,

Di Loreto

et al. 2023

2023 International Conference on Control, Automation and Diagnosis (ICCAD)

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The position of the center of mass of a vehicle is an important parameter as it significantly affects vehicle loads distribution and vehicle dynamics. This article proposes an approach to estimate the longitudinal position of the center of mass and the inertial parameters from offline batches. The proposed algorithm is based on the dynamical modelling of the vehicle and on the measurements of cheap gyroscopes and accelerometers. The estimation procedure uses the Box-Jenkins method for system identification to estimate the parameters given the noisy measurements. Prior knowledge of the vehicle is used to filter the output measurement and simplify the estimation problem. The approach is tested on simulations when one or more gyroscopes and accelerometers are used.Index Terms-System identification, vehicle dynamics, position of the center of mass, measurement noise, Box-Jenkins model.

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Section: Validation Of the Estimation Methodsmentioning

confidence: 99%

Section: B Model Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A system identification approach to center of mass estimation in trucks

Benadada,

Massioni,

Di Loreto

et al. 2023

2023 International Conference on Control, Automation and Diagnosis (ICCAD)

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“…Pavement design plays a crucial role in Vietnam's infrastructure development, impacting transportation efficiency and economic growth. Accurately accounting for traffic loads, particularly axle weight distribution, is essential for designing pavements that are both durable and cost-effective [1][2][3][4][5]. Axle load surveys provide valuable data on these loads, but collecting comprehensive data can be time-consuming and expensive [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A case study on scenario-based optimization for axle load survey data selection for flexible pavement design in Vietnam

Le Xuan,

Chu Tien,

Vu Tuan

2024

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The unique traffic patterns in Vietnam, characterized by a mix of vehicle types, axle configurations, and overloading, pose challenges to current flexible pavement design methods. This study investigates how selecting different vehicle axle load survey data scenarios impacts the accuracy of pavement design and rehabilitation for roads in Vietnam. Evaluation of various data selection methods and their influence on pavement response using the current flexible pavement calculation procedure in Vietnam. By comparing the pavement design criteria calculated under each scenario, this research aims to provide clear guidelines for engineers choosing appropriate axle load data for pavement design. This, in turn, will lead to the design and maintenance of more durable and sustainable pavements, ultimately promoting a more efficient and resilient transportation infrastructure in Vietnam. The research approach involves analyzing various axle load survey data scenarios, including those representing typical traffic conditions, overloaded vehicles, and specific vehicle types. The calculated pavement responses under each scenario are then compared to assess the impact of data selection on design outcomes. This study's findings are expected to provide valuable insights for pavement engineers in Vietnam, enabling them to select appropriate axle load data for accurate pavement design and rehabilitation. This will contribute to building more durable and sustainable road infrastructure, resulting in a more efficient and resilient transportation network

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