Field testing of indirect displacement estimation using accelerometers

Kandula, Viswanadh; DeBrunner, L.S.; DeBrunner, V.; Rambo-Roddenberry, Michelle

doi:10.1109/acssc.2012.6489361

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…Acceleration is an absolute response that can be easily captured on a structure without having a fixed reference. Theoretically, acceleration can be converted into displacement by double integration in the time domain, while the numerical integration generally brings a significant signal drift (Park et al 2005, Gindy et al 2008, Kandula et al 2012. Lee et al (2010) successfully proposed an FIR filter-based displacement estimation technique which regularizes the signal drift.…”

Section: Healthmentioning

confidence: 99%

Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model

Cho¹,

Yun²,

Sim

2015

Smart Structures and Systems

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Recently, an indirect displacement estimation method using data fusion of acceleration and strain (i.e., acceleration-strain-based method) has been developed. Though the method showed good performance on beam-like structures, it has inherent limitation in applying to more general types of bridges that may have complex shapes, because it uses assumed analytical (sinusoidal) mode shapes to map the measured strain into displacement. This paper proposes an improved displacement estimation method that can be applied to more general types of bridges by building the mapping using the finite element model of the structure rather than using the assumed sinusoidal mode shapes. The performance of the proposed method is evaluated by numerical simulations on a deck arch bridge model and a three-span truss bridge model whose mode shapes are difficult to express as analytical functions. The displacements are estimated by acceleration-based method, strain-based method, acceleration-strain-based method, and the improved method. Then the results are compared with the exact displacement. An experimental validation is also carried out on a prestressed concrete girder bridge. The proposed method is found to provide the best estimate for dynamic displacements in the comparison, showing good agreement with the measurements as well.

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Section: Healthmentioning

confidence: 99%

Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model

Cho¹,

Yun²,

Sim

2015

Smart Structures and Systems

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“…Kandula et al [14] proposed an acceleration signal model that is the sum of exponentially damped sinusoidal signals. The displacement response is obtained by modeling the noiseless acceleration and then integrating it twice.…”

Section: Introductionmentioning

confidence: 99%

Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition

Zou

Chen

Liu

2019

Sensors

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Considering the lack of precision in transforming measured micro-electro-mechanical system (MEMS) accelerometer output signals into elevation signals, this paper proposes a bridge dynamic displacement reconstruction method based on the combination of ensemble empirical mode decomposition (EEMD) and time domain integration, according to the vibration signal traits of a bridge. Through simulating bridge analog signals and verifying a vibration test bench, four bridge dynamic displacement monitoring methods were analyzed and compared. The proposed method can effectively eliminate the influence of low-frequency integral drift and high-frequency ambient noise on the integration process. Furthermore, this algorithm has better adaptability and robustness. The effectiveness of the method was verified by field experiments on highway elevated bridges.

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“…Displacement estimation from the measured acceleration is used for this application by using a proportional relationship between bridge deflection and vehicle weights. Researchers have also developed algorithms to estimate bridge displacement by reducing integration noise and using low‐frequency accelerometers (Gindy et al., ; Kandula et al., ). For such processes, accelerometers must have low high‐frequency noise and must also be frequently calibrated to remove drift errors.…”

Section: Introductionmentioning

confidence: 99%

Acceleration‐Based Automated Vehicle Classification on Mobile Bridges

Yeum

Dyke

Rovira

et al. 2016

Computer aided Civil Eng

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Mobile bridges have been used for a broad range of applications including military transportation or disaster restoration. Because mobile bridges are rapidly deployed under a wide variety of conditions, often remaining in place for just minutes to hours, and have irregular usage patterns, a detailed record of usage history is important for ensuring structural safety. To facilitate usage data collection in mobile bridges, a new acceleration-based vehicle classification technique is proposed to automatically identify the class of each vehicle. Herein we present a new technique that is based on the premise that each class of vehicles produces distinctive dynamic patterns while crossing this mobile bridge, and those patterns can be extracted from the system's acceleration responses. Measured acceleration signals are converted to time-frequency images to extract twodimensional patterns. The Viola-Jones object detection algorithm is applied here to extract and classify those patterns. The effectiveness of the technique is investigated and demonstrated using laboratory and full-scale mobile bridges by simulating realistic scenarios.

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Field testing of indirect displacement estimation using accelerometers

Cited by 7 publications

References 8 publications

Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model

Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model

Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition

Acceleration‐Based Automated Vehicle Classification on Mobile Bridges

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