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Road roughness is an important factor in road network maintenance and ride quality. This paper proposes a road-roughness estimation method using the frequency response function (FRF) of a vehicle. First, based on the motion equation of the vehicle and the time shift property of the Fourier transform, the vehicle FRF with respect to the displacements of vehicle–road contact points, which describes the relationship between the measured response and road roughness, is deduced and simplified. The key to road roughness estimation is the vehicle FRF, which can be estimated directly using the measured response and the designed shape of the road based on the least-squares method. To eliminate the singular data in the estimated FRF, the shape function method was employed to improve the local curve of the FRF. Moreover, the road roughness can be estimated online by combining the estimated roughness in the overlapping time periods. Finally, a half-car model was used to numerically validate the proposed methods of road roughness estimation. Driving tests of a vehicle passing over a known-sized hump were designed to estimate the vehicle FRF, and the simulated vehicle accelerations were taken as the measured responses considering a 5% Gaussian white noise. Based on the directly estimated vehicle FRF and updated FRF, the road roughness estimation, which considers the influence of the sensors and quantity of measured data at different vehicle speeds, is discussed and compared. The results show that road roughness can be estimated using the proposed method with acceptable accuracy and robustness.
Qingxia Zhang; Jilin Hou; Zhongdong Duan; Łukasz Jankowski; Xiaoyang Hu. Road Roughness Estimation Based on the Vehicle Frequency Response Function. Actuators 2021, 10, 89 .
AMA StyleQingxia Zhang, Jilin Hou, Zhongdong Duan, Łukasz Jankowski, Xiaoyang Hu. Road Roughness Estimation Based on the Vehicle Frequency Response Function. Actuators. 2021; 10 (5):89.
Chicago/Turabian StyleQingxia Zhang; Jilin Hou; Zhongdong Duan; Łukasz Jankowski; Xiaoyang Hu. 2021. "Road Roughness Estimation Based on the Vehicle Frequency Response Function." Actuators 10, no. 5: 89.
Structural damage identification plays an important role in providing effective evidence for the health monitoring of bridges in service. Due to the limitations of measurement points and lack of valid structural response data, the accurate identification of structural damage, especially for large-scale structures, remains difficult. Based on additional virtual mass, this paper presents a damage identification method for bridges using a vehicle bump as the excitation. First, general equations of virtual modifications, including virtual mass, stiffness, and damping, are derived. A theoretical method for damage identification, which is based on additional virtual mass, is formulated. The vehicle bump is analyzed, and the bump-induced excitation is estimated via a detailed analysis in four periods: separation, free-fall, contact, and coupled vibrations. The precise estimation of bump-induced excitation is then applied to a bridge. This allows the additional virtual mass method to be used, which requires knowledge of the excitations and acceleration responses in order to construct the frequency responses of a virtual structure with an additional virtual mass. Via this method, a virtual mass with substantially more weight than a typical vehicle is added to the bridge, which provides a sufficient amount of modal information for accurate damage identification while avoiding the bridge overloading problem. A numerical example of a two-span continuous beam is used to verify the proposed method, where the damage can be identified even with 15% Gaussian random noise pollution using a 1-degree of freedom (DOF) car model and 4-DOF model.
Qingxia Zhang; Jilin Hou; Łukasz Jankowski. Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses. Sensors 2020, 20, 394 .
AMA StyleQingxia Zhang, Jilin Hou, Łukasz Jankowski. Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses. Sensors. 2020; 20 (2):394.
Chicago/Turabian StyleQingxia Zhang; Jilin Hou; Łukasz Jankowski. 2020. "Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses." Sensors 20, no. 2: 394.