Estimation of Road Roughness Based on Tire Pressure Monitoring

Zeng, Qing; Hu, Xiaoyang; Shi, Xiaodong; Ren, Yiting; Li, Yuan; Duan, Zhongdong

doi:10.1142/s0219455422500730

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…The contact forces are calculated by multiplying the relative contact displacement (that between the wheel axle and bridge provided by a laser) and the tire stiffness based on the elastic contact model [ 24 ]. Alternatively, the contact force can be reflected by the dynamic tire pressure [ 44 ].

and

are the system matrices of the vehicle, defined as

respectively.…”

Section: Theoretical Formulationmentioning

confidence: 99%

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Quan,

Zeng,

Jin

et al. 2024

Sensors

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As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle–bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

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and

are the system matrices of the vehicle, defined as

respectively.…”

Section: Theoretical Formulationmentioning

confidence: 99%

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Quan,

Zeng,

Jin

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Few studies explored attaching sensors to vehicle tires to measure pavement roughness. (Zeng et al [136] estimated the IRI using a tire pressure sensor. The study employed a combination of ideal gas low and elastic contact models to derive a nonlinear relationship between tire pressure and contact force.…”

Section: Tire Pressure Sensorsmentioning

confidence: 99%

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Fares

Zayed

2023

Remote Sensing

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Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry.

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“…15 presented sufficient and necessary design conditions for achieving the attacks. Zeng et al 16 proposed a Kalman Filter-based scheme to evaluate the road roughness indirectly. Chen et al 17 proposed a neural network method to identify a vehicle system.…”

Section: Introductionmentioning

confidence: 99%

Vehicle dynamics prediction via adaptive robust unscented particle filter

Liu

Cui

2023

Advances in Mechanical Engineering

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Accurate knowledge of the vehicle dynamics response is a critical aspect to improve handling performance while ensuring safe driving at the same time. However, it poses a challenge since not all the quantities of interest can be directly measured due to cost and/or technological reasons. Therefore, combining the principle of robust filtering and unscented particle filtering algorithm, a filter estimation method of vehicle state is proposed to estimate driving state parameters of a vehicle. The adaptive robust unscented particle filter (ARUPF) is used to realize the longitudinal and lateral velocity as well as the side slip angle of the vehicle. The CarSim and Matlab/Simulink co-simulation platform is established to verify the estimation algorithm. The results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving states can be estimated, the measurement parameters can be effectively filtered, and the estimation accuracy is higher.

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Estimation of Road Roughness Based on Tire Pressure Monitoring

Cited by 11 publications

References 18 publications

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Vehicle dynamics prediction via adaptive robust unscented particle filter

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