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To investigate the vibration characteristics of off-road vehicles resulting from external excitations, this study explores a vibration modelling approach that accounts for the coupling of roll and pitch. A low-frequency vibration test was conducted, and the results were analysed in both the time domain and frequency domains. The mathematical model of the tractor was validated, affirming the accuracy of the off-road vehicle modelling method. This study focuses on the Shandong Wuzheng Group Co., Ltd. Renoman 2104 ratio tractor prototype (1:18) as the research subject. A vibration mathematical model and simulation model were developed, taking into account the coupled vibrations in both roll and pitch directions. Key parameters of the tractor, including damping, stiffness, centroid, inertia, and road roughness, were acquired through experimental testing. Upon comparing the numerical simulation model results with the experimental data, the findings reveal a 6.2% error between the left front axle test data and the simulation, and a 6.1% error for the right front axle. The vertical vibration primary frequency in the test is 13.1Hz, while the numerical simulation model registers 11.8 Hz, resulting in a relative error of 10%. Notably, the numerical simulation model exhibits significant consistency with the experimental results. These outcomes serve as a valuable reference for the parameter design and vibration control of off-road vehicles. • A vibration modelling approach of off-road vehicles is proposed. • The coupling vibration of pitch and roll is considered. • The methodology for acquiring tractor dynamic parameters has been elucidated. • An analysis of both time domain and frequency domain results was conducted to validate the mathematical model of the tractor. [ABSTRACT FROM AUTHOR]

This study aims at proposing a method to estimate the deflection angle (rotation) and deflection of bridges using acceleration measurements without integrating acceleration in the time domain. It applies multiple measurements, filtering, and fitting techniques for a constrained local optimum rather than integrating acceleration in the time domain. First, the acceleration of the bridge is filtered to obtain the time history of the deflection angle at each measurement point. Then, the Karush–Kuhn–Tucker (KKT) algorithm as a constrained least squares (CLS) method is used to estimate the shape curve of the deflection angle and deflection of the bridge. The validity of the proposed acceleration-based deflection angle and deflection identification method is verified through an in-house moving vehicle experiment on a model bridge. The method develops a data-driven model based on a small amount of observational data, enabling accurate predictions of the angular rotation and deflection of beam structures. [ABSTRACT FROM AUTHOR]

Damage detection of bridges using vibrations from a passing vehicle has received a lot of interest recently. Though non-modal parameter-based methods (e.g., data-driven approaches) have shown promising results in this context, their advancement towards a comprehensive and rigorous monitoring system is hampered by their overreliance on machine learning techniques. On this background, this paper proposes a novel automatic physics-guided diagnosis framework for bridge health monitoring utilizing only raw vehicle accelerations. First, numerical studies are conducted to investigate the relationship between vehicle time-domain signals and bridge damage, based on which a new damage index is proposed. At the same time, it also explores the identification of damage locations and proposes a location index. Second, a damage diagnosis framework, which consists of a data processing method and a physics-guided model, is designed to overcome deficiencies from a drive-by measurement and to automate the damage detection process. The proposed framework was validated using datasets acquired from laboratory experiments employing a scale vehicle model and a steel beam. The results affirmed the method's efficacy in damage indication, quantification, and localization. Moreover, the superiority of the proposed damage index and the rationale for the proposed physics-guided approach were also demonstrated through comparisons with machine learning-based methods. [ABSTRACT FROM AUTHOR]