A New Model Updating Procedure for Reliability-Based Damage and Load Identification of Railway Bridges

Shahbaznia, Mahdi; Mirzaee, Akbar; Dehkordi, Morteza Raissi

doi:10.1007/s12205-020-0641-x

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…From equation (18) and equation ( 19), it can be understood that the optimal damping ratio for suspension comfort and the optimal damping ratio for safety affect and constrain each other, in order to take into account the safety and comfort requirements of the vehicle, it is necessary to weigh and match the two, the matching damping ratio is called the optimal ratio for the integration of the best damping ratio for opt , and the method of its value is: when opt ⩽ com , take opt = com ; when opt ⩾ csec , take opt = cse ; when com < opt < sec , the optimal damping ratio control curve as shown in Fig. 4.…”

Section: Suspension Control Methods Under Multi-information Fusionmentioning

confidence: 99%

“…The suspension control of driverless cars is based on road surface identification, and the existing research on road surface identification focuses on two categories, one is the identification of road surface texture and cracks based on laser, radar and other detection means, which is mainly applied to the quality monitoring of damaged roads [16][17][18][19]; and the other is the detection and identification of road surface unevenness based on visual image processing technology, which is mainly applied to the identification of road grades [20][21][22][23][24]. The above pavement inspection methods cannot effectively calculate the length, width, and other details of small obstacles or damaged pavement.…”

Section: Introductionmentioning

confidence: 99%

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An automatic control method for semi-active suspension of driverless vehicle based on multi-sensor information fusion in complex environment

ZHOU,

JING,

LYU

2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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A semi-active suspension control method based on multi-sensor information fusion was proposed to improve the safety and smoothness of driverless A quarter suspension vibration model considering multi-sensor information fusion was established, revealing the relationship between road roughness information and vehicle vibration. A quarter suspension vibration model considering multi-sensor information fusion was established, revealing the relationship between road roughness information and vehicle vibration. The camera and radar wave were used to scan and identify the uneven road conditions, and a mathematical model of road roughness was created. The information fusion and matching of uneven road surface were carried out by The information fusion and matching of uneven road surface were carried out by detecting the edge intersection ratio and Graph Neural Network(GNN) algorithm, obtaining a more reliable mathematical model of uneven road surface in complex environment. It is proposed to calculate the optimal damping ratio of semi-active suspension using the information of vehicle speed and road roughness, and to adjust the suspension to the optimal damping ratio of semi-active suspension. It is proposed to calculate the optimal damping ratio of semi-active suspension using the information of vehicle speed and road roughness, and to adjust the suspension to this damping ratio to adapt to different road conditions in real time. The vehicle ride comfort test under typical road input conditions was carried out. The vehicle ride comfort test under typical road input conditions was carried out, and the vibration acceleration time domain response signals of different suspensions were compared and analysed. The results show that the maximum peak vibration acceleration of the unmanned suspension controlled by multiple information fusion is reduced by 43 % comparing with that of the unmanned suspension. reduced by 43 % comparing with that in the passive suspension under the same conditions, which verifies the superiority of the proposed method.

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Section: Suspension Control Methods Under Multi-information Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An automatic control method for semi-active suspension of driverless vehicle based on multi-sensor information fusion in complex environment

ZHOU,

JING,

LYU

2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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“…The FEMU method depends on the modified parameters of the model. Although some scholars have taken into account the uncertainty of these parameters [135,137,139,149] and the nonlinearity of the structure [141,142,144], in the FEM analysis of complex structures, faced with a large number of correction parameters, a repeated calculation process will lead to a slow updating process or deviation. The substructure method can decompose the whole model correction process into several independent blocks to avoid the double calculation of local damage.…”

Section: Other Femu Methodsmentioning

confidence: 99%

The Current Development of Structural Health Monitoring for Bridges: A Review

et al. 2023

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The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.

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“…Wang et al suggest identifying moving train load parameters using the enhanced classic moving force identification method (Wang et al, 2019). In order to simultaneously identify structural performance and moving load, some researchers have attempted to put forward a finite element model updating method based on the dynamic response sensitivity, a Bayesian inference regularization method, and so on (Lu and Liu, 2011;Feng et al, 2015;Shahbaznia et al, 2020). Many sensing technologies have also been developed and used for the identification of moving load, such as piezoelectric sensors, FBG sensors and camera machine vision (Zhang et al, 2021;Alamandala et al, 2021, Dan et al, 2019.…”

Section: Qingqing Zhang Wenju Zhao Jian Zhangmentioning

confidence: 99%

Moving Load Identification with Long Gauge Fiber Optic Strain Sensing

Zhang

Zhao

Zhang

2021

BJRBE

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Moving load identification has been researched with regard to the analysis of structural responses, taking into consideration that the structural responses would be affected by the axle parameters, which in its turn would complicate obtaining the values of moving vehicle loads. In this research, a method that identifies the loads of moving vehicles using the modified maximum strain value considering the long-gauge fiber optic strain responses is proposed. The method is based on the assumption that the modified maximum strain value caused only by the axle loads may be easily used to identify the load of moving vehicles by eliminating the influence of these axle parameters from the peak value, which is not limited to a specific type of bridges and can be applied in conditions, where there are multiple moving vehicles on the bridge. Numerical simulations demonstrate that the gross vehicle weights (GVWs) and axle weights are estimated with high accuracy under complex vehicle loads. The effectiveness of the proposed method was verified through field testing of a continuous girder bridge. The identified axle weights and gross vehicle weights are comparable with the static measurements obtained by the static weighing.

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A New Model Updating Procedure for Reliability-Based Damage and Load Identification of Railway Bridges

Cited by 11 publications

References 25 publications

An automatic control method for semi-active suspension of driverless vehicle based on multi-sensor information fusion in complex environment

An automatic control method for semi-active suspension of driverless vehicle based on multi-sensor information fusion in complex environment

The Current Development of Structural Health Monitoring for Bridges: A Review

Moving Load Identification with Long Gauge Fiber Optic Strain Sensing

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