Mobile Impact Testing for Structural Flexibility Identification with only a Single Reference

This article presents a Kalman‐filter‐based estimation algorithm for identification of wind loads on a super‐tall building using limited structural responses. In practice, acceleration responses are most convenient to be measured among wind‐induced dynamic responses of structures. The proposed inverse method allows estimating the unknown wind loads and structural responses of a super‐tall building using limited acceleration measurements. Taipei 101 Tower is a super‐tall building with 101 stories and a height of 508 m. Field measurements and numerical simulations of the wind effects on Taipei 101 Tower are conducted. The wind loads acting on the super‐tall building are estimated based on the wind‐induced responses determined from the numerical simulations and the refined finite‐element model of the structure, which are in good agreement with the exact results. The stability performance of the proposed algorithm is evaluated. The influence of noise levels in the measurements and covariance matrix of noise on the identification accuracy are investigated and discussed based on the L‐curve method. Finally, the wind loads and structural responses are reconstructed based on the field‐measured accelerations during Typhoon Matsa. The accuracy of the identified results is verified by comparing the reconstructed acceleration responses with the field measurements. The results of this study show that the proposed inverse approach can provide accurate predictions of the wind loads and wind‐induced responses of super‐tall buildings based on limited measured responses.

Section: Wind‐induced Response and Wind Loading Estimation Based On Tmentioning

confidence: 99%

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

Zhi

Ming-xin

2016

“…Structural damage in concrete structures subject to service and extreme loadings and environmental attacks is essentially a multiphase process. In the past few decades, vibration‐based damage detection methods have been widely investigated, but they are insensitive to local damage (Xia, Cheng, Zhang, & Zhu, ; J. Zhang et al., ; J. Zhang, Wan, & Sato, ; J. Zhang, Guo, & Zhang, ; J. Zhang et al., ). Concrete damage usually begins with the inception of cracking (showing hairline cracks), evolves to extensive cracking (major cracks or spalling), and ultimately leads to local or global structural deficiency, all of which unfold during a long time if not attacked by extreme loadings in a rapid way.…”

Section: Introductionmentioning

confidence: 99%

Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning

Zhang

Chen

2018

134

Although crack inspection is a routine practice in civil infrastructure management (especially for highway bridge structures), it is time-consuming and safety-concerning to trained engineers and costly to the stakeholders. To automate this in the near future, the algorithmic challenge at the onset is to detect and localize cracks in imagery data with complex scenes. The rise of deep learning (DL) sheds light on overcoming this challenge through learning from imagery big data. However, how to exploit DL techniques is yet to be fully explored. One primary component of practical crack inspection is that it is not merely detection via visual recognition. To evaluate the potential risk of structural failure, it entails quantitative characterization, which usually includes crack width measurement. To further facilitate the automation of machine-vision-based concrete crack inspection, this article proposes a DL-enabled quantitative crack width measurement method. In the detection and mapping phase, dual-scale convolutional neural networks are designed to detect cracks in complex scene images with validated high accuracy. Subsequently, a novel crack width estimation method based on the use of Zernike moment operator is further developed for thin cracks. The experimental results based on a laboratory loading test agree well with the direct measurements, which substantiates the effectiveness of the proposed method for quantitative crack detection.

“…A sledgehammer is a well‐known excitation device, generating high‐frequency band impacting forces; however, its main drawback is that the magnitudes of impacting forces are relatively low (below 20 kN), which cannot be employed in order to fully excite the interested frequency band of a bridge (Tian et al., ). In order to tackle this limitation, a vehicle‐mounted excitation device with drop weight was developed, which can induce impact forces with large magnitudes (around 100 kN) and high‐frequency band (0–200 Hz) (Catbas et al., ; Zhang et al., ). The mentioned device can efficiently excite a bridge.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many algorithms were developed for structural modal properties identification, such as modal responses‐based method (Oh et al., ), modal identification using orthogonality of filtered response vectors (Kim et al., ), discretized synchro‐squeezed wavelet and Hilbert transforms (Li et al., ), MUltiple SIgnal Classification (MUSIC) (Amezquita‐Sanchez and Adeli, ; Amezquita‐Sanchez et al., ), entropy‐based model identification method (Yin et al., ), and dynamic wavelet neural network (Jiang and Adeli, ). Besides, several algorithms were developed for processing the impact‐testing data, such as complex mode indicator function (CMIF), PolyMAX, subspace identification (SI) (Zhang et al., ; Tian et al., ; Sun et al., ; Karami and Akbarabadi, ; Li et al., ), among others. Both CMIF and PolyMAX methods belong to frequency domain parameter identification, and they can be used for a single impact force.…”

Section: Introductionmentioning

confidence: 99%

Rapid Impact Testing and System Identification of Footbridges Using Particle Image Velocimetry

Tian

Zhang

2018

The rapid impact testing of bridges contains unique advantages. For example, structural parameters, including frequency response function and structural flexibility matrix can be identified; however, additional impact-testing instruments are required to excite a bridge, restricting the efficiency of the measurement strategy in terms of experimental cost and time. In this paper, a particle image velocimetry-based method is proposed for the rapid impact testing and system identification of footbridges under pedestrian excitations. The proposed method has shown promising features: (1) pedestrian load is utilized for the impact excitation of footbridges, which is more convenient than the conventional impacttesting method with additional excitation devices; (2) the human-induced impact forces under varying jumping scenarios are calculated from image sequences of human motions acquired by a single camera with its noncontact and target-less characteristics; and (3) both humaninduced impact forces (inputs) and structural responses (outputs) are employed to identify more modal parameters (i.e., scaling factors, modal mass, and structural flexibility). The robustness of the proposed method was successfully validated by a laboratory test of a simply supported beam and field testing of a cable-stayed footbridge. The proposed method not only could improve