Browse > Article
http://dx.doi.org/10.7734/COSEIK.2020.33.4.271

Numerical Study on the Development of the Seismic Response Prediction Method for the Low-rise Building Structures using the Limited Information  

Choi, Se-Woon (Dept. of Architectural Engineering, Daegu Catholic Univ.)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.33, no.4, 2020 , pp. 271-277 More about this Journal
Abstract
There are increasing cases of monitoring the structural response of structures using multiple sensors. However, owing to cost and management problems, limited sensors are installed in the structure. Thus, few structural responses are collected, which hinders analyzing the behavior of the structure. Therefore, a technique to predict responses at a location where sensors are not installed to a reliable level using limited sensors is necessary. In this study, a numerical study is conducted to predict the seismic response of low-rise buildings using limited information. It is assumed that the available response information is only the acceleration responses of the first and top floors. Using both information, the first natural frequency of the structure can be obtained. The acceleration information on the first floor is used as the ground motion information. To minimize the error on the acceleration history response of the top floor and the first natural frequency error of the target structure, the method for predicting the mass and stiffness information of a structure using the genetic algorithm is presented. However, the constraints are not considered. To determine the range of design variables that mean the search space, the parameter prediction method based on artificial neural networks is proposed. To verify the proposed method, a five-story structure is used as an example.
Keywords
limited sensor; prediction; genetic algorithm; artificial neural network;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
연도 인용수 순위
1 Choi, S.W., Park, K., Kim, Y., Park, H.S. (2013) A Numerical Study on the Strain Based Monitoring Method for Lateral Structural Response of Buildings using FBG Sensors, J. Comput. Struct. Eng. Inst. Korea, 26(4), pp.263-269.   DOI
2 Choi, S.W., Park, H.S. (2016) Genetic Algorithm based Optimal Seismic Design Method for Inducing the Beam-hinge Mechanism of Steel Moment Frames, J. Comput. Struct. Eng. Inst. Korea, 29(3), pp.253-260.   DOI
3 Choi, S.W. (2018) Estimation of Rotational Stiffness of Connection in Steel Moment Frames by using Artificial Neural Network, J. Korea Inst. Struct. Maint. & Insp., 22(1), pp.107-114.   DOI
4 Jang, J., An, H., Lee, J.H., Shin, S. (2019) Construction of Faster R-CNN Deep Learning Model for Surface Damage Detection of Blade Systems, J. Korea Inst. Struct. Maint. & Insp., 23(7), pp.80-86.   DOI
5 Kang, J.W., Kim, H.S. (2011) Fuzzy Control of Smart TMD using Multi-Objective Genetic Algorithm, J. Comput. Struct. Eng. Inst. Korea, 24(1), pp.69-78.
6 Kang, S., Shin, S. (2016) Determination of Optimal Accelerometer Locations for Bridges using Frequency-Domain Hankel Matrix, J. Korea Inst. Struct. Maint. & Insp., 20(4), pp.27-34.   DOI
7 Kim, J.H., Park, W.J., Park, J.O., Park, S.H. (2018a) LoRa LPWAN Sensor Network for Real-Time Monitoring and It's Control Method, J. Comput. Struct. Eng. Inst. Korea, 31(6), pp.359-366.   DOI
8 Lee, H.M., Park, S.W., Park, H.S. (2009) Selection of Sensing Members in a High-rise Building Structures using Displacement Participation Factors and Strain Energy Density, J. Comput. Struct. Eng. Inst. Korea, 22(4), pp.349-354.
9 Kim, J., Park. S., Lee, H. (2018b) Magnetic Hysteresis Monitoring according to the Change of Tensile Force and Steel Class of PS Tendons, J. Comput. Struct. Eng. Inst. Korea, 31(2), pp.115-120.   DOI
10 Korea Ministry of Government Legislation, http://www.law.go.kr/ (accessed May, 21, 2020).
11 Lee, M.S., Hong, K., Choi, S.W. (2016a) Genetic Algorithm Based Optimal Structural Design Method for Cost and $CO_2$ Emissions of Reinforced Concrete Frames, J. Comput. Struct. Eng. Inst. Korea, 29(5), pp.429-436.   DOI
12 Lee, Y.H., Kim, J.H., Lee, S.H. (2016b) The Optimal Placements and Number of Sensors for Dynamic Monitoring of Tall Buildings, J. Wind Eng. Inst. Korea, 20(2), pp.99-105.
13 Lee, S.Y., Huynh, T.C., Park, J.H., Kim, J.T. (2019) Bolt-Loosening Detection using Vision-Based Deep Learning Algorithm and Image Processing Method, J. Comput. Struct. Eng. Inst. Korea, 32(4), pp.265-272.   DOI
14 OpenSees, http://opensees.berkeley.edu/ (accessed May 21, 2020).
15 Son, I.S., Lee, D.H. (2011) A Study on Optimal Sensor Placement using Sensitivity Analysis, Trans. Korean Soc. Noise & Vib. Eng., 21(3), pp.241-247.   DOI
16 PEER Ground Motion Database, http://ngawest2.berkeley.edu/ (accessed May, 21, 2020).