A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Nonlinear data‐driven computational models for response prediction and change detection
Data are used from three relatively large‐scale experimental soil‐foundation‐superstructure interaction (SFSI) systems to develop reduced‐order computational models for response prediction and change‐detection relevant to structural health monitoring and computational mechanics. The three systems under consideration consist of identical superstructures with: (i) fixed base; (ii) box foundation; and (iii) pile foundation. The three SFSI systems were developed and experimentally tested at Tongji University. In the first part of the study, a computational time‐marching prediction framework is proposed by incorporating trained neural network(s) within an ordinary differential equation solver and dynamically predicting the response (i.e., displacement and velocity) of the SFSI systems to various earthquake excitations. Two approaches are investigated: global approach and subsystem approach. Both approaches are tested and validated with linear and nonlinear systems, and their respective pros and cons are discussed. In the second part of the study, the trained neural networks from the global approach are further used for change‐detection in the SFSI systems. The detected changes in the systems are then quantified through a measure of a normalized error index. Challenges related to the physical interpretation of the quantified changes in the SFSI systems are addressed and discussed. It is shown that the general procedures adopted in this paper provide a robust nonlinear model that is reliable for computational studies, as well as furnishing a robust tool for detecting and quantifying inherent change in the system. Copyright © 2014 John Wiley & Sons, Ltd.
Nonlinear data‐driven computational models for response prediction and change detection
Data are used from three relatively large‐scale experimental soil‐foundation‐superstructure interaction (SFSI) systems to develop reduced‐order computational models for response prediction and change‐detection relevant to structural health monitoring and computational mechanics. The three systems under consideration consist of identical superstructures with: (i) fixed base; (ii) box foundation; and (iii) pile foundation. The three SFSI systems were developed and experimentally tested at Tongji University. In the first part of the study, a computational time‐marching prediction framework is proposed by incorporating trained neural network(s) within an ordinary differential equation solver and dynamically predicting the response (i.e., displacement and velocity) of the SFSI systems to various earthquake excitations. Two approaches are investigated: global approach and subsystem approach. Both approaches are tested and validated with linear and nonlinear systems, and their respective pros and cons are discussed. In the second part of the study, the trained neural networks from the global approach are further used for change‐detection in the SFSI systems. The detected changes in the systems are then quantified through a measure of a normalized error index. Challenges related to the physical interpretation of the quantified changes in the SFSI systems are addressed and discussed. It is shown that the general procedures adopted in this paper provide a robust nonlinear model that is reliable for computational studies, as well as furnishing a robust tool for detecting and quantifying inherent change in the system. Copyright © 2014 John Wiley & Sons, Ltd.
Nonlinear data‐driven computational models for response prediction and change detection
Derkevorkian, Armen (author) / Hernandez‐Garcia, Miguel (author) / Yun, Hae‐Bum (author) / Masri, Sami F. (author) / Li, Peizhen (author)
Structural Control and Health Monitoring ; 22 ; 273-288
2015-02-01
16 pages
Article (Journal)
Electronic Resource
English
Computational prediction models for cancer classification using mass spectrometry data
| British Library Online Contents | 2008
Computational prediction models for cancer classification using mass spectrometry data
| British Library Online Contents | 2008