A time domain method for reconstruction of pedestrian induced loads on vibrating structures

Firus, Andrei; Kemmler, Roman; Berthold, Hagen; Lorenzen, Steven Robert; Schneider, Jens

doi:10.1016/j.ymssp.2022.108887

Cited by 7 publications

(3 citation statements)

References 52 publications

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“…This is partly due to the need to perform detailed verification of the model performance [12][13][14][15][16][17]. Another important factor is that civil engineering structures are generally assessed with the help of computers within the finite element (FE) environment [18,19]. This requires incorporation of sophisticated DBIP model into the FE framework.…”

Section: Introductionmentioning

confidence: 99%

Implementation of damped bipedal inverted pendulum model of pedestrian into FE environment for prediction of vertical structural vibration

et al. 2023

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Section: Introductionmentioning

confidence: 99%

Implementation of damped bipedal inverted pendulum model of pedestrian into FE environment for prediction of vertical structural vibration

et al. 2023

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“…Since knowledge of the actual operational loads is of high importance for the condition assessment of the structures, especially when it comes down to the assessment of fatigue failure and the evaluation of the remaining service life, the determination of the loads is a key aspect in the field of SHM. Since the direct measurement of loads is often technically difficult and usually requires significant financial resources [4][5][6], different methods for load identification based on measured structural responses have been developed [6][7][8][9][10]. In the case of bridges, these methods are referred to as Bridge Weigh-In-Motion (BWIM) [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

Lorenzen

Riedel

Rupp

et al. 2022

Sensors

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In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

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“…As the knowledge of the actual operational loads is of high importance for the condition assessment of the structures, especially when it comes down to the assessment of fatigue failure and the evaluation of the remaining service life, the determination of the loads is a key aspect in the field of SHM. Since direct measurement of the loads is often technically difficult and usually requires a significant financial effort [5,19,9], different methods for load identification based on measured structural responses have been developed [17,14,24,8,9]. In the case of bridges, these techniques are referred to as Bridge Weigh-In-Motion (BWIM) [25,32,33,13].…”

Section: Introductionmentioning

confidence: 99%

Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

Lorenzen¹,

Riedel²,

Rupp³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is in most cases a prerequisite. To avoid the use of conventional axle detectors and bridge type specific methods, we propose a novel method for axle detection through the placement of accelerometers at any point of a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This enables our method to use acceleration signals at any location of the bridge structure serving as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Our results on the measurement data show that our model detects 95% of the axes, thus, 128,599 of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles can be detected with a maximum spatial error of 20 cm, with a maximum velocity of v max = 56, 3 m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

show abstract

A time domain method for reconstruction of pedestrian induced loads on vibrating structures

Cited by 7 publications

References 52 publications

Implementation of damped bipedal inverted pendulum model of pedestrian into FE environment for prediction of vertical structural vibration

Implementation of damped bipedal inverted pendulum model of pedestrian into FE environment for prediction of vertical structural vibration

Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

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