Displacement Estimation for Nonlinear Structures Using Seismic Acceleration Response Data

2021 ◽  
pp. 1-19
Author(s):  
Haoran Pan ◽  
Ka-Veng Yuen ◽  
Koichi Kusunoki
Keyword(s):  
Nonlinear Structures ◽  
Acceleration Response ◽  
Seismic Acceleration ◽  
Displacement Estimation ◽  
Response Data

Localization of damaged cable in a tied‐arch bridge using Arias intensity of seismic acceleration response

2019 ◽  
Vol 27 (3) ◽  
Author(s):  
Hadi Kordestani ◽  
Yi‐Qiang Xiang ◽  
Xiao‐Wei Ye ◽  
Chun‐Bang Yun ◽  
Mahdi Shadabfar
Keyword(s):  
Arias Intensity ◽  
Acceleration Response ◽  
Seismic Acceleration ◽  
Arch Bridge ◽  
Tied Arch Bridge

Identification of Loads on Suspenders in Bridges Based on Measured Response Data in Time Domain

2012 ◽  
Vol 226-228 ◽  
pp. 1640-1644
Author(s):  
Li Tao Zhang ◽  
Yu Feng Zhang
Keyword(s):  
Differential Equation ◽  
Time Domain ◽  
Simulated Data ◽  
Measured Data ◽  
Superposition Method ◽  
Acceleration Response ◽  
Regularization Technique ◽  
Response Data ◽  
Tied Arch Bridge ◽  
Measured Response

The purpose of this paper is to deal with identification of loads on suspenders. Values of the load at every moment were chose as identification parameters, and the objective function was established using measured data of responses and corresponding simulated ones. The vibration differential equation of suspenders was adopted to obtain the formula of relationship between the load and acceleration responses with superposition method. Furthermore, a regularization technique was applied in identification calculations to improve ill-posedness of the problem to be solved. With a tied arch bridge as the real example, the load on one of its suspenders was identified. Results of identification showed that the simulated data of acceleration response were almost identical to measured ones, which indicated validity of the proposed method.

Unsupervised deep learning approach using a deep auto-encoder with an one-class support vector machine to detect structural damage

2020 ◽  
pp. 147592172093405
Author(s):  
Zilong Wang ◽  
Young-Jin Cha
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Training Data ◽  
Support Vector ◽  
Acceleration Response ◽  
Damage Scenarios ◽  
Response Data ◽  
Unsupervised Deep Learning

This article proposes an unsupervised deep learning–based approach to detect structural damage. Supervised deep learning methods have been proposed in recent years, but they require data from an intact structure and various damage scenarios of monitored structures for their training processes. However, the labeling work on the training data is typically time-consuming and costly, and sometimes collecting sufficient training data from various damage scenarios of infrastructures in service is impractical. In this article, the proposed unsupervised deep learning method based on a deep auto-encoder with an one-class support vector machine only uses the measured acceleration response data acquired from intact or baseline structures as training data, which enables future structural damage to be detected. The major contributions and novelties of the proposed method are as follows. First, an appropriate deep auto-encoder is carefully designed through comparative studies on the depth of neural networks. Second, the designed deep auto-encoder is taken as an extractor to obtain damage-sensitive features from the measured acceleration response data, and an one-class support vector machine is used as a damage detector. Third, experimental and numerical studies validate the high accuracy of the proposed method for damage detection: a 97.4% mean average for a 12-story numerical building model and a 91.0% accuracy for a laboratory-scaled steel bridge. Fourth, the proposed method also detects light damage (i.e. a 10% reduction in stiffness) with 96.9% to 99.0% accuracy, which shows its superior performance compared with the current state of the art. Fifth, it provides stable and more robust damage detection performance with reduced tuning parameters.

A Comparison Between Head Impact Response of Hybrid III and THOR-NT Dummy Heads in FMVSS201 Tests

2006 ◽  
Author(s):  
Waseem Jaradat ◽  
Joseph Hassan ◽  
Guy Nusholtz ◽  
Khalil Taraman ◽  
Sanaa Taraman
Keyword(s):  
Block Design ◽  
Head Impact ◽  
Impact Response ◽  
Free Motion ◽  
Acceleration Response ◽  
Melt Temperature ◽  
Response Data ◽  
Operational Variables ◽  
Hybrid Iii ◽  
The Impact

The impact response of the forehead of both the Hybrid III dummy and THOR dummy was designed to the same human surrogate data. Therefore, when the forehead of either dummy is impacted with the same initial conditions, the acceleration response and consequently the head impact criterion HIC should be similar. If the THOR dummy is used in the FMVSS 201 free motion headform tests, then when it strikes the interior trim of the vehicle, as prescribed by the FMVSS 201 procedure, the acceleration response should be similar to that of the Hybrid III, as long as only the forehead engages the vehicle interior. To compare and contrast the response of the two dummy heads under FMVSS 201 testing, a design of experiments (DOE), that is a function of seven variables, is utilized to develop a mathematical model of the Head Impact Response. These independent parameters include five trim manufacturing process variables that relate to the interior that the dummy head hits in 201 testing: mold temperature, melt temperature, packing pressure, hold pressure, and injection speed. Two operational variables were also considered: free motion Headform approach angle and the dummy head drop calibration. An incomplete block design approach is utilized in order to significantly reduce the number of experiments. The DOE approach determines the response in the form of the Head Impact Criterion (HIC) with respect to the seven variables at 99% confidence level. The results describe the response data of both dummy heads. The response data of the dummy heads is described. Results indicate that the Hybrid III dummy head and the THOR dummy head have significantly different response characteristics in terms of magnitude of response, variation to different input conditions, repeatability, HIC values, and acceleration time history.

A Method for Extracting Building Empirical Capacity Curves from Earthquake Response Data

2016 ◽  
Vol 32 (4) ◽  
pp. 2229-2244 ◽  
Author(s):  
Jeffrey Dowgala ◽  
Ayhan Irfanoglu
Keyword(s):  
Small Scale ◽  
Earthquake Response ◽  
Viscous Damping ◽  
Acceleration Response ◽  
Damping Force ◽  
Restoring Force ◽  
Equivalent Viscous Damping ◽  
Strong Base ◽  
Capacity Curves ◽  
Response Data

A method is presented for extracting empirical capacity curves from building earthquake response data. The method can be applied to buildings with acceleration response records from each floor to develop story empirical capacity curves assuming the building has flexible columns and rigid floors. The method can also be applied to buildings with acceleration response records from the roof and ground to develop a fundamental mode empirical capacity curve. The method relies on extracting the restoring force and relative displacement of the system by removing damping force, considered as equivalent viscous damping, from the inertial response, using a proposed viscous damping identification procedure. The method is demonstrated using data from a small-scale, three-story experimental model subjected to strong base motion.

Damage Localization of Frame Structures Using Seismic Acceleration Response Data

2012 ◽  
Vol 452-453 ◽  
pp. 1094-1098
Author(s):  
Ming Chih Huang ◽  
Yen Po Wang ◽  
Chien Liang Lee
Keyword(s):  
System Identification ◽  
Transfer Functions ◽  
Mode Shapes ◽  
Frame Structures ◽  
Damage Localization ◽  
Seismic Acceleration ◽  
Arx Model ◽  
Response Data ◽  
Flexibility Matrix ◽  
Specific Loading

In this study, damage localization of frame structures from seismic acceleration responses is explored using the DLV technique and ARX model for system identification. The concept of the DLV method is to identify the members with zero stress under some specific loading patterns derived by interrogating the changes in flexibility matrix of the structure before and after the damage state. Success of the DLV method for damage localization lies on the ability to identify the flexibility matrix. The ARX model, a discrete-time non-parametric auto-regressive system identification technique is adopted to identify the modal parameters (natural frequencies, transfer functions and mode shapes) from which the flexibility matrices of the intact and damaged structures are constructed. To explore the effectiveness of the DLV method, a five-storey steel model frame with diagonal bracings was considered for seismic shaking table tests. The damage conditions of the structure were simulated by partially removing some of the diagonals. With the flexibility matrices of both the intact and damaged structures synthesized on a truncated modal basis, the damage locations have been successfully identified by the DLV method for either single or multiple damage conditions, regardless of the damage locations. This study confirms the potential of the DLV method in the detection of local damages from global seismic response data for frame structures.

scholarly journals Mitigation Effect of Seismic Acceleration of Nuclear Power Plant Electric Cabinet Using Tuned Mass Damper under Earthquakes

2019 ◽  
Author(s):  
Seongkyu Chang ◽  
Sung Gook Cho
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Tuned Mass Damper ◽  
Wide Frequency Range ◽  
Control Performance ◽  
Frequency Range ◽  
Acceleration Response ◽  
Seismic Acceleration ◽  
Mass Damper

In this study, a tuned mass damper is proposed as a seismic acceleration mitigating technique of an electrical cabinet inside the nuclear power plant. In order to know the mitigation performance, the electrical cabinet and the tuned mass damper were modeled using SAP2000. The sine sweep wave was used to confirm the vibration characteristics of the cabinet over a wide frequency range, and the several various earthquakes were applied to the cabinet to verify the control performance of the tuned mass damper. After analyzing the numerical results, it is summarized that the application of the proposed technique can reduce the acceleration response of the cabinet.

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