Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

Li, Jinke; Xie, Botao; Zhao, Xuefeng

doi:10.1002/stc.2492

Cited by 29 publications

(26 citation statements)

References 38 publications

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“…Unlike these instruments, the essence of the proposed method is image processing. One of the most important influencing factors in image processing is illumination because the illumination change will cause many image processing algorithms to have poor robustness [17,33,34]. Under a bad illumination environment or no light environment, these methods will have noise in results, or no results would be obtained.…”

Section: Night Testmentioning

confidence: 99%

“…However, it needs 8 MEMS inclinometers in one column, thus the cost will increase. The method in paper [17] is easy to handle and has good accuracy, but it needs normal light and has a low sampling rate. The following methods in papers [19][20][21] have high accuracy and high sampling rates, and they have been verified in full-scale tests.…”

Section: Sensor Comparisonmentioning

confidence: 99%

“…The essence of interstory drift monitoring is to monitor relative displacement between ceiling and floor. There are many relative displacement monitoring methods, such as a linear variable differential transmitter (LVDT) [11], double numerical integration of acceleration data [11][12][13][14], the photogrammetry-assisted Augmented Reality (AR) technique [15], Micro Electro Mechanical Systems (MEMS) inclinometers [16], and the machine vision method [17].…”

Section: Introductionmentioning

confidence: 99%

“…The results turned out that the mean percent error was 7.6%. Li et al [17] used a smartphone that was fixed on the floor with the front camera recording video of the ceiling. A feature point-matching algorithm was used to recognize ceiling objects and calculate the relative movement between the ceiling and floor.…”

Section: Introductionmentioning

confidence: 99%

“…Using inclinometers [16] to monitoring interstory drift will require eight sensors in one column, which will increase the cost. Besides, the machine vision method [17] needs normal light and cannot be used in darkness.…”

Section: Introductionmentioning

confidence: 99%

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A Method of Interstory Drift Monitoring Using a Smartphone and a Laser Device

Xie

Zhao

2020

Sensors

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Interstory drift is an important engineering parameter in building design and building structural health monitoring. However, many problems exist in current interstory drift monitoring methods. The traditional method is imprecise—double numerical integration of acceleration data—and other direct monitoring methods need professional equipment. This paper proposes a method to solve these problems by monitoring the interstory drift with a smartphone and a laser device. In this method, a laser device is installed on the ceiling while a smartphone is fixed on a steel projection plate on the floor. Compared with a reference sensor, the method designed in this study shows that a smartphone is competent in monitoring the interstory drift. This method utilizes a smartphone application (APP) named D-Viewer to implement monitoring and data storage just in one place, which is also inexpensive. The results showed that this method has an average percent error of 3.37%, with a standard deviation of 2.67%. With the popularization of the smartphone, this method is promising in acquiring large amounts of data, which will be significant for building assessment after an earthquake.

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Section: Night Testmentioning

confidence: 99%

Section: Sensor Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method of Interstory Drift Monitoring Using a Smartphone and a Laser Device

Xie

Zhao

2020

Sensors

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Unbiased rank selection for automatic hysteretic response extraction of RC frame buildings using acceleration recordings for post‐earthquake safety evaluations

Yeow

Kusunoki

2021

Earthq Engng Struct Dyn

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A framework to extract a building's hysteretic response based on decomposing floor acceleration recordings into different ranks (i.e., decomposition levels) using the discrete wavelet transform (DWT) and then reconstructing signals considering only ranks which corresponded to the building's predominant response mode was recently proposed. However, current rank selection approaches require subjective judgment. This study looks to develop an unbiased rank selection method to enable automation of the hysteresis extraction framework for easier adoption in practice. The selection methodology was developed using response history analysis of several multistory reinforced concrete (RC) frame buildings and validated using data from large‐scale shake‐table tests performed at the E‐Defense testing facility. It was observed that the proposed approach was able to accurately capture the response of RC frame buildings well in majority of cases. The exception to this was for significantly damaged buildings as the use of double integration of accelerations to estimate displacements and the DWT method meant that residual deformations were not well captured. However, significant strength degradation was still observed in the extracted hysteresis behavior for these cases, and thus these buildings would have been correctly classified as being heavily damaged. The step‐by‐step process to automatically select ranks is presented in the paper, and possible revenues for further improvement of the hysteretic response extraction framework are discussed.

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A model‐driven method based on multisource data for reproducing displacement time history

Wang

Pan

2023

Earthq Engng Struct Dyn

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The seismic displacement time history of a structure is very important for damage assessment. This paper proposes a displacement estimation method based on multisource measurement data. In this method, the structure is simplified to a mass‐spring shear model, where a set of tunable parameters are assigned. The particle swarm optimization algorithm is used to search for the optimal values of the parameters, and the objective of the optimization problem is to minimize the difference between the measured roof response and that calculated by the model. A refined finite element model was established in ABAQUS, and the numerical calculation results were used to verify this proposed method. Results show that the estimation error of the maximum inter‐story drift ratio among all floors is 8.9%, and the average error of the maximum displacement of each floor is 7.0%, representing a satisfactory estimation effectiveness. The source of the errors is discussed, and we find that this method is more suitable for low‐rise buildings. Shaking table tests of a scaled four‐story reinforced concrete (RC) frame were conducted, and the estimation error of the maximum inter‐story drift ratio among all floors was 6.1%, indicating the feasibility of this method in practice.

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Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

Cited by 29 publications

References 38 publications

A Method of Interstory Drift Monitoring Using a Smartphone and a Laser Device

A Method of Interstory Drift Monitoring Using a Smartphone and a Laser Device

Unbiased rank selection for automatic hysteretic response extraction of RC frame buildings using acceleration recordings for post‐earthquake safety evaluations

A model‐driven method based on multisource data for reproducing displacement time history

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