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Modeling and Computation in Vibration Problems, Volume 2: Soft computing and uncertainty [Kõva köide]

Edited by (Texas A&M University Aerospace Engineering), Edited by (University of Salento (Italy)), Edited by (National Institute of Technology Rourkela (India))
  • Formaat: Hardback, 259 pages, kõrgus x laius x paksus: 254x178x19 mm, kaal: 767 g, With figures in colour and in black and white; 80 Illustrations
  • Sari: IOP ebooks
  • Ilmumisaeg: 22-Dec-2021
  • Kirjastus: Institute of Physics Publishing
  • ISBN-10: 0750334851
  • ISBN-13: 9780750334853
Teised raamatud teemal:
Modeling and Computation in Vibration Problems, Volume 2: Soft computing and uncertaintySuurem pilt
  • Formaat: Hardback, 259 pages, kõrgus x laius x paksus: 254x178x19 mm, kaal: 767 g, With figures in colour and in black and white; 80 Illustrations
  • Sari: IOP ebooks
  • Ilmumisaeg: 22-Dec-2021
  • Kirjastus: Institute of Physics Publishing
  • ISBN-10: 0750334851
  • ISBN-13: 9780750334853
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780750334853.html
Märksõnad:
Preface xii
Editor biographies xvii
List of contributors
xxii
1 Deep learning for solution and inversion of structural mechanics and vibrations
1(1)
Ehsan Haghighat
Ali Can Bekar
Erdogan Madenci
Ruben Juanes
1.1 Introduction
1(1)
1.2 Deep learning
2(1)
1.3 Physics-informed neural networks
3(2)
1.4 Training neural networks
5(1)
1.5 Applications of deep learning for data representation
6(2)
1.5.1 Polynomial regression
6(1)
1.5.2 Smoothing noisy vibration measurements
7(1)
1.6 Deep learning for solution and inversion of vibration problems
8(7)
1.6.1 Forced vibration spring-mass problem
8(2)
1.6.2 Free vibration of rectangular membrane
10(1)
1.6.3 Free vibration of a rectangular plate
11(4)
1.7 Discussions and final remarks
15
References
15
2 Artificial neural network based technique for solving nonlinear eigenvalue problems of structural dynamics with fuzzy parameters
1(1)
S. K. Jeswal
2.1 Introduction
1(1)
2.2 Preliminaries
2(2)
2.2.1 Nonlinear eigenvalue problems (NEPs)
2(1)
2.2.2 Fuzzy nonlinear eigenvalue problems (FNEPs)
2(1)
2.2.3 Linearization of FNEPs
3(1)
2.2.4 Conversion of the fuzzy matrix into interval form
3(1)
2.3 ANN methodology
4(4)
2.4 Numerical example
8(5)
2.5 Conclusion
13
References
13
3 Multilayer unsupervised symplectic artificial neural network model for solving Duffing and Van der Pol--Duffing oscillator equations arising in engineering problems
1(1)
Arup Kumar Sahoo
S. Chakraverty
3.1 Introduction
1(1)
3.2 Architecture of multi-layer feed-forward neural network
2(1)
3.3 Duffing oscillator equations
3(1)
3.4 Van der Pol--Duffing oscillator equations
3(1)
3.5 General formulation for differential equations with respect to ANN
4(1)
3.5.1 Construction of symplectic neural network method for initial value problem
4(1)
3.6 Numerical experiments and results
5(6)
3.7 Conclusion
11
Acknowledgment
12(1)
References
13
4 Estimation of structural parameters using Chebyshev neural network model
1(1)
Deepti Moyi Sahoo
Snehashish Chakraverty
4.1 Introduction
1(3)
4.2 Modelling for system identification of multistorey shear buildings
4(1)
4.3 Chebyshev neural network
5(2)
4.3.1 Structure of Chebyshev neural network
5(1)
4.3.2 Learning algorithm of Chebyshev neural network
6(1)
4.4 Results and discussion
7(8)
4.5 Conclusion
15
References
15
5 Inverse problems in vehicle--bridge interaction dynamics with application to bridge health monitoring
1(1)
O. A. Shereena
C. G. Krishnanunni
G. Sai Kumar
B. N. Rao
Preface
1(1)
Symbols
2(1)
5.1 Introduction
2(2)
5.2 Part I: Roughness and vehicle parameter identification problem
4(12)
5.2.1 Proposed technique
4(1)
5.2.2 Unbiased minimum variance estimator for unknown input
4(2)
5.2.3 Optimization scheme
6(1)
5.2.4 Objective functions
6(1)
5.2.5 Problem statement and state space formulation
7(1)
5.2.6 Problem statement
7(1)
5.2.7 State space formulation
7(1)
5.2.8 Quarter car model
8(1)
5.2.9 Modelling the vehicle--bridge interaction
9(2)
5.2.10 Post-processing for extracting roughness profile
11(1)
5.2.11 Numerical examples
12(1)
5.2.12 Quarter car model
12(4)
5.3 Part II: The damage identification problem
16(19)
5.3.1 Vehicle dynamics
17(1)
5.3.2 Mathematical modelling of the bridge
18(1)
5.3.3 Part (a): Tyre model calibration
19(1)
5.3.4 Relationship between VBI force and tyre pressure
19(1)
5.3.5 Measurements used for tyre model calibration
19(1)
5.3.6 Bayesian parameter estimation
20(1)
5.3.7 Stein variational gradient descent (SVGD)
20(1)
5.3.8 Simulation results
21(1)
5.3.9 Part (b): Damage identification
22(2)
5.3.10 Damage model
24(2)
5.3.11 Damage scenarios considered in the study
26(1)
5.3.12 Damage identification by contour plots
26(1)
5.3.13 Damage indicators
27(2)
5.3.14 Performance of the contour plots in assessing damage
29(1)
5.3.15 Damage identification by optimization
30(5)
5.4 Summary
35
Acknowledgements
36(1)
References
36
6 Hybrid computational methods in vibration problems
1(1)
Fiorenzo A. Fazzolari
Puxue Tan
6.1 Hybrid FE-SEA model
3(1)
6.2 Benchmark models
4(4)
6.2.1 Translational-spring-plate system
5(2)
6.2.2 Torsional-spring-plate system
7(1)
6.3 Linearisation techniques
8(1)
6.3.1 Method of harmonic balance
8(1)
6.3.2 Statistical linearisation
9(1)
6.4 Numerical results
9(11)
6.4.1 Gaussian orthogonal ensemble statistics
10(2)
6.4.2 Built-up plate systems
12(8)
6.5 Hybrid meshless-SEA formulation
20(4)
6.5.1 Moving least square (MLS)
21(2)
6.5.2 Element free MLS Ritz method
23(1)
6.5.3 Hybrid element free MLS Ritz-SEA model
23(1)
6.5.4 Numerical results for structure with continuous junction
24(1)
6.6 Summary
24
References
25
7 Vibration analysis of structures with uncertain parameters:---an interval finite element approach
1(1)
M. V. Rama Rao
Andrew Pownuk
Introduction
1(1)
7.1 Free vibration analysis of structures with uncertain structural parameters
2(8)
7.2 Transient response of structures with uncertain structural parameters
10
References
26
8 Vibrations of functionally graded structure with material uncertainties
1(1)
Subrat Kumar Jena
8.1 Introduction
1(2)
8.2 Preliminaries
3(1)
8.3 Mathematical modelling
3(4)
8.4 Application of Hermite--Ritz method
7(1)
8.5 Numerical results and discussions
7(8)
8.5.1 Validation
8(1)
8.5.2 Propagation of uncertainties
9(6)
8.6 Conclusion
15
References
15
9 A new triple parametric approach to solve type-2 fuzzy structural problems with uncertain parameters in terms of interval type-2 trapezoidal fuzzy numbers
1(1)
S. Rout
9.1 Introduction
1(2)
9.2 Preliminaries
3(5)
9.2.1 Interval type-2 fuzzy set
4(1)
9.2.2 Interval type-2 fuzzy number
4(1)
9.2.3 Interval type-2 trapezoidal fuzzy number
4(1)
9.2.4 Interval type-2 trapezoidal fuzzy arithmetic
5(1)
9.2.5 Perfectly normal interval type-2 trapezoidal fuzzy number
6(1)
9.2.6 Type-2 fuzzy generalized eigenvalue problem
7(1)
9.3 Triple parametric form of IT2TrFN
8(1)
9.4 Proposed methodology
9(3)
9.5 Numerical examples
12(11)
9.6 Conclusion
23
References
23
10 Non-linear dynamic problems with uncertainty in type-2 fuzzy environment
1(1)
D. Mohapatra
S. Chakraverty
10.1 Introduction
1(1)
10.2 Preliminaries
2(3)
10.2.1 Type-1 fuzzy numbers
2(1)
10.2.2 Parametric form of fuzzy number
3(1)
10.2.3 Type-2 fuzzy set
3(1)
10.2.4 Vertical slice of type-2 fuzzy set
3(1)
10.2.5 r1-plane of type-2 fuzzy set
3(1)
10.2.6 Footprint of uncertainty
3(1)
10.2.7 Lower membership function (LMF) and upper membership function (LMF) of a type-2 fuzzy set
3(1)
10.2.8 Principle set of A
4(1)
10.2.9 r2-cut of r1-plane
4(1)
10.2.10 Triangular perfect quasi type-2 fuzzy numbers
4(1)
10.3 Crisp non-linear eigenvalue problem
5(1)
10.4 Type-2 fuzzy non-linear eigenvalue problem and proposed methodology
6(1)
10.5 Numerical examples
7(13)
10.6 Conclusion
20
Acknowledgment
20(1)
References
21
11 Semi-analytical methods for solving Ito stochastic models on the notion of Karhunen--Loeve Brownian motion transform
1(1)
Opeyemi Ogundile
Sunday Edeki
11.1 Introduction
1(1)
11.2 Ito stochastic model
2(1)
11.3 Stochastic/random vibration differential equations
3(1)
11.4 Approximate-analytical methods of solution
4(1)
11.5 Daftar--Gejii--Jafaris method
4(1)
11.6 Picard iterative method
5(1)
11.7 Karhunen--Loeve expansion (K--L E) of Brownian motion
6(1)
11.8 Application of K--L expansion to Ito SDEs
7(11)
11.9 Comparison of the DJM solution and the PIM solution
18(5)
11.10 Results, discussion and conclusion
23
References
25
12 Arbitrary order vibration equation of large membranes with uncertainty
1(1)
Rajarama Mohan Jena
12.1 Introduction
1(1)
12.2 Preliminaries
2(2)
12.3 Definitions and properties of Aboodh transform
4(1)
12.4 DPF of fuzzy fractional vibration equation
5(1)
12.5 Basic idea of q-HAATM
6(2)
12.6 Implementation of q-HAATM for solving fractional fuzzy VE
8(2)
12.7 Result and discussion
10
References
12
13 Fractional derivatives: a numerical insight into flow problems involving second grade fluid under fuzzy environment
1(1)
Gourangajit Borah
Palash Dutta
G. C. Hazarika
13.1 Introduction
1(1)
13.2 Formulation of the problem
2(2)
13.3 Fuzzification of the problem
4(3)
13.4 Method of solution
7(1)
13.4.1 Solution with AB fractional derivatives
7(1)
13.4.2 Solution with CF fractional derivatives
7(1)
13.5 Results and discussion
8(6)
13.5.1 Comparison of AB and CF fractional derivative methods in tabular form
9(3)
13.5.2 Validation of our present scheme
12(2)
13.6 Conclusion
14
Declarations
16(1)
References
16
14 Successive approximation method based on uncertain dynamic responses of a fractionally damped beam
1(1)
Smita Tapaswini
Diptiranjan Behera
14.1 Introduction
1(1)
14.2 Basic idea of successive approximation method
2(1)
14.3 Implementation of SAM for the double parametric based solution of uncertain fractionally damped beam
3(3)
14.4 Uncertain responses subjected to various forces
6(3)
14.4.1 Unit step function response
6(2)
14.4.2 Unit impulse function response
8(1)
14.5 Conclusions
9
References
9
15 Vibration of a cantilever beam immersed in a fluid with uncertain parameters
1(8)
U. Biswal
S. Chakraverty
B. K. Ojha
15.1 Introduction
1(1)
15.2 Preliminaries
2(1)
15.2.1 Interval
2(1)
15.2.2 Parametric concept
3(1)
15.3 Dynamics of a cantilever beam
3(4)
15.3.1 Construction of γ(jc, t) for a bending cantilever beam
3(3)
15.3.2 Least square method (LSM)
6(1)
15.4 Results and discussion
7(2)
15.5 Conclusion
9(1)
Acknowledgments 9(1)
References 9