Author

# Singiresu S. Rao

Other affiliations: United States Environmental Protection Agency, Indian Institute of Technology Kanpur, Indian Institutes of Technology ...read more

Bio: Singiresu S. Rao is an academic researcher from University of Miami. The author has contributed to research in topics: Fuzzy logic & Optimization problem. The author has an hindex of 44, co-authored 222 publications receiving 8204 citations. Previous affiliations of Singiresu S. Rao include United States Environmental Protection Agency & Indian Institute of Technology Kanpur.

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13 Dec 2006

TL;DR: The author explains how basic Equations of Elasticity, Laplace and Fourier Transforms, and Approximate Analytical Methods transformed into Eigenvalue and Modal Analysis Approach, which led to the modern Elastic Wave Propagation.

Abstract: Preface. Symbols. Chapter 1. Introduction: Basic Concepts and Terminology. Chapter 2. Vibration of Discrete Systems: Brief Review. Chapter 3. Derivation of Equations: Equilibrium Approach. Chapter 4. Derivation of Equations: Variation Approach. Chapter 5. Derivation of Equations: Integral Equation Approach. Chapter 6. Solution Procedure: Eigenvalue and Modal Analysis Approach. Chapter 7. Solution Procedure: Integral Transform Methods. Chapter 8. Transverse Vibration of Strings. Chapter 9. Longitudinal Vibration of Bars. Chapter 10. Torsional Vibration of Shafts. Chapter 11. Transverse Vibration of Beams. Chapter 12. Vibration of Circular Rings and Curved Beams. Chapter 13.Vibration of Membranes. Chapter 14. Transverse Vibration of Plates. Chapter 15. Vibration of Shells. Chapter 16. Elastic Wave Propagation. Chapter 17. Approximate Analytical Methods. A. Basic Equations of Elasticity. B. Laplace and Fourier Transforms. Index.

1,396 citations

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TL;DR: The ASME Digital Collection as mentioned in this paper is a collection of papers from the American Society of Mechanical Engineers (ASME) Conference Proceedings (Conference Publications Toolbox) and the ASME Journal.

Abstract: Search Applied Mechanics Reviews Purchase this Content $25.00 Purchase Learn about subscription and purchase options Topics: Piezoelectricity , Flexible structures , Piezoelectric materials , Vibration measurement Your Session has timed out. Please sign back in to continue. About ASME Digital Collection Email Alerts Library Service Center ASME Membership Contact Us Publications Permissions /Reprints Privacy Policy Terms of Use © 2018 ASME The American Society of Mechanical Engineers Journals Submit a Paper Announcements Call for Papers Title History Conference Proceedings About ASME Conference Publications Conference Proceedings Author Guidelines Conference Publications Toolbox This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy. | Accept Loading [MathJax]/jax/output/HTML-CSS/fonts/TeX/fontdata.js

487 citations

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TL;DR: In this article, an interval truncation approach is proposed to limit the growth of intervals of response parameters so that realistic and accurate solutions can be obtained in the presence of large amounts of uncertainty.

Abstract: The imprecision or uncertainty present in many engineering analysis/design problems can be modeled using probabilistic, fuzzy, or interval methods. This work considers the modeling of uncertain structural systems using interval analysis. By representing each uncertain input parameter as an interval number, a static structural analysis problem can be expressed in the form of a system of linear interval equations. In addition to the direct and Gaussian elimination-based solution approaches, a combinatorial approach (based on an exhaustive combination of the extreme values of the interval numbers) and an inequality-hased method are presented for finding the solution of interval equations. The range or interval of the solution vector (response parameters) is found to increase with increasing size of the problem in all of the methods. An interval-truncation approach is proposed to limit the growth of intervals of response parameters so that realistic and accurate solutions can be obtained in the presence of large amounts of uncertainty. Numerical examples are presented to illustrate the computational aspects of the methods and also to indicate the importance of the truncation approach in practical problems. The utility of interval methods in predicting the extreme values of the response parameters of structures is discussed.

400 citations

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306 citations

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TL;DR: In this article, a survey of the recent research studies in this important field is presented, which contains 336 references which are classified according to their applications and a brief theory and history of piezoelectricity.

Abstract: Due to their special characteristics, piezoelectric materials can be used in distributed behavior sensing and control of flexible structures. These materials are usually incorporated with the precision sensing and control of highly adaptive intelligent structures. Many theoretical, numerical, and experimental research activities treating piezoelectricity in sensing and control of various flexible structures have been carried out over the last decade. This survey article aims at collecting the recent research studies in this important field. It contains 336 references which are classified according to their applications. A brief theory and history of piezoelectricity is also presented.

225 citations

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30 Jun 2002

TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.

Abstract: List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

5,994 citations

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TL;DR: A survey of current continuous nonlinear multi-objective optimization concepts and methods finds that no single approach is superior and depends on the type of information provided in the problem, the user's preferences, the solution requirements, and the availability of software.

Abstract: A survey of current continuous nonlinear multi-objective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation of preferences, and methods with no articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of MOO as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software.

4,263 citations

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01 Jan 2011

TL;DR: This chapter discusses Optimization Techniques, which are used in Linear Programming I and II, and Nonlinear Programming II, which is concerned with One-Dimensional Minimization.

Abstract: Preface. 1 Introduction to Optimization. 1.1 Introduction. 1.2 Historical Development. 1.3 Engineering Applications of Optimization. 1.4 Statement of an Optimization Problem. 1.5 Classification of Optimization Problems. 1.6 Optimization Techniques. 1.7 Engineering Optimization Literature. 1.8 Solution of Optimization Problems Using MATLAB. References and Bibliography. Review Questions. Problems. 2 Classical Optimization Techniques. 2.1 Introduction. 2.2 Single-Variable Optimization. 2.3 Multivariable Optimization with No Constraints. 2.4 Multivariable Optimization with Equality Constraints. 2.5 Multivariable Optimization with Inequality Constraints. 2.6 Convex Programming Problem. References and Bibliography. Review Questions. Problems. 3 Linear Programming I: Simplex Method. 3.1 Introduction. 3.2 Applications of Linear Programming. 3.3 Standard Form of a Linear Programming Problem. 3.4 Geometry of Linear Programming Problems. 3.5 Definitions and Theorems. 3.6 Solution of a System of Linear Simultaneous Equations. 3.7 Pivotal Reduction of a General System of Equations. 3.8 Motivation of the Simplex Method. 3.9 Simplex Algorithm. 3.10 Two Phases of the Simplex Method. 3.11 MATLAB Solution of LP Problems. References and Bibliography. Review Questions. Problems. 4 Linear Programming II: Additional Topics and Extensions. 4.1 Introduction. 4.2 Revised Simplex Method. 4.3 Duality in Linear Programming. 4.4 Decomposition Principle. 4.5 Sensitivity or Postoptimality Analysis. 4.6 Transportation Problem. 4.7 Karmarkar's Interior Method. 4.8 Quadratic Programming. 4.9 MATLAB Solutions. References and Bibliography. Review Questions. Problems. 5 Nonlinear Programming I: One-Dimensional Minimization Methods. 5.1 Introduction. 5.2 Unimodal Function. ELIMINATION METHODS. 5.3 Unrestricted Search. 5.4 Exhaustive Search. 5.5 Dichotomous Search. 5.6 Interval Halving Method. 5.7 Fibonacci Method. 5.8 Golden Section Method. 5.9 Comparison of Elimination Methods. INTERPOLATION METHODS. 5.10 Quadratic Interpolation Method. 5.11 Cubic Interpolation Method. 5.12 Direct Root Methods. 5.13 Practical Considerations. 5.14 MATLAB Solution of One-Dimensional Minimization Problems. References and Bibliography. Review Questions. Problems. 6 Nonlinear Programming II: Unconstrained Optimization Techniques. 6.1 Introduction. DIRECT SEARCH METHODS. 6.2 Random Search Methods. 6.3 Grid Search Method. 6.4 Univariate Method. 6.5 Pattern Directions. 6.6 Powell's Method. 6.7 Simplex Method. INDIRECT SEARCH (DESCENT) METHODS. 6.8 Gradient of a Function. 6.9 Steepest Descent (Cauchy) Method. 6.10 Conjugate Gradient (Fletcher-Reeves) Method. 6.11 Newton's Method. 6.12 Marquardt Method. 6.13 Quasi-Newton Methods. 6.14 Davidon-Fletcher-Powell Method. 6.15 Broyden-Fletcher-Goldfarb-Shanno Method. 6.16 Test Functions. 6.17 MATLAB Solution of Unconstrained Optimization Problems. References and Bibliography. Review Questions. Problems. 7 Nonlinear Programming III: Constrained Optimization Techniques. 7.1 Introduction. 7.2 Characteristics of a Constrained Problem. DIRECT METHODS. 7.3 Random Search Methods. 7.4 Complex Method. 7.5 Sequential Linear Programming. 7.6 Basic Approach in the Methods of Feasible Directions. 7.7 Zoutendijk's Method of Feasible Directions. 7.8 Rosen's Gradient Projection Method. 7.9 Generalized Reduced Gradient Method. 7.10 Sequential Quadratic Programming. INDIRECT METHODS. 7.11 Transformation Techniques. 7.12 Basic Approach of the Penalty Function Method. 7.13 Interior Penalty Function Method. 7.14 Convex Programming Problem. 7.15 Exterior Penalty Function Method. 7.16 Extrapolation Techniques in the Interior Penalty Function Method. 7.17 Extended Interior Penalty Function Methods. 7.18 Penalty Function Method for Problems with Mixed Equality and Inequality Constraints. 7.19 Penalty Function Method for Parametric Constraints. 7.20 Augmented Lagrange Multiplier Method. 7.21 Checking the Convergence of Constrained Optimization Problems. 7.22 Test Problems. 7.23 MATLAB Solution of Constrained Optimization Problems. References and Bibliography. Review Questions. Problems. 8 Geometric Programming. 8.1 Introduction. 8.2 Posynomial. 8.3 Unconstrained Minimization Problem. 8.4 Solution of an Unconstrained Geometric Programming Program Using Differential Calculus. 8.5 Solution of an Unconstrained Geometric Programming Problem Using Arithmetic-Geometric Inequality. 8.6 Primal-Dual Relationship and Sufficiency Conditions in the Unconstrained Case. 8.7 Constrained Minimization. 8.8 Solution of a Constrained Geometric Programming Problem. 8.9 Primal and Dual Programs in the Case of Less-Than Inequalities. 8.10 Geometric Programming with Mixed Inequality Constraints. 8.11 Complementary Geometric Programming. 8.12 Applications of Geometric Programming. References and Bibliography. Review Questions. Problems. 9 Dynamic Programming. 9.1 Introduction. 9.2 Multistage Decision Processes. 9.3 Concept of Suboptimization and Principle of Optimality. 9.4 Computational Procedure in Dynamic Programming. 9.5 Example Illustrating the Calculus Method of Solution. 9.6 Example Illustrating the Tabular Method of Solution. 9.7 Conversion of a Final Value Problem into an Initial Value Problem. 9.8 Linear Programming as a Case of Dynamic Programming. 9.9 Continuous Dynamic Programming. 9.10 Additional Applications. References and Bibliography. Review Questions. Problems. 10 Integer Programming. 10.1 Introduction 588. INTEGER LINEAR PROGRAMMING. 10.2 Graphical Representation. 10.3 Gomory's Cutting Plane Method. 10.4 Balas' Algorithm for Zero-One Programming Problems. INTEGER NONLINEAR PROGRAMMING. 10.5 Integer Polynomial Programming. 10.6 Branch-and-Bound Method. 10.7 Sequential Linear Discrete Programming. 10.8 Generalized Penalty Function Method. 10.9 Solution of Binary Programming Problems Using MATLAB. References and Bibliography. Review Questions. Problems. 11 Stochastic Programming. 11.1 Introduction. 11.2 Basic Concepts of Probability Theory. 11.3 Stochastic Linear Programming. 11.4 Stochastic Nonlinear Programming. 11.5 Stochastic Geometric Programming. References and Bibliography. Review Questions. Problems. 12 Optimal Control and Optimality Criteria Methods. 12.1 Introduction. 12.2 Calculus of Variations. 12.3 Optimal Control Theory. 12.4 Optimality Criteria Methods. References and Bibliography. Review Questions. Problems. 13 Modern Methods of Optimization. 13.1 Introduction. 13.2 Genetic Algorithms. 13.3 Simulated Annealing. 13.4 Particle Swarm Optimization. 13.5 Ant Colony Optimization. 13.6 Optimization of Fuzzy Systems. 13.7 Neural-Network-Based Optimization. References and Bibliography. Review Questions. Problems. 14 Practical Aspects of Optimization. 14.1 Introduction. 14.2 Reduction of Size of an Optimization Problem. 14.3 Fast Reanalysis Techniques. 14.4 Derivatives of Static Displacements and Stresses. 14.5 Derivatives of Eigenvalues and Eigenvectors. 14.6 Derivatives of Transient Response. 14.7 Sensitivity of Optimum Solution to Problem Parameters. 14.8 Multilevel Optimization. 14.9 Parallel Processing. 14.10 Multiobjective Optimization. 14.11 Solution of Multiobjective Problems Using MATLAB. References and Bibliography. Review Questions. Problems. A Convex and Concave Functions. B Some Computational Aspects of Optimization. B.1 Choice of Method. B.2 Comparison of Unconstrained Methods. B.3 Comparison of Constrained Methods. B.4 Availability of Computer Programs. B.5 Scaling of Design Variables and Constraints. B.6 Computer Programs for Modern Methods of Optimization. References and Bibliography. C Introduction to MATLAB(R) . C.1 Features and Special Characters. C.2 Defining Matrices in MATLAB. C.3 CREATING m-FILES. C.4 Optimization Toolbox. Answers to Selected Problems. Index .

3,283 citations

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TL;DR: The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area and the optimal solutions obtained are mostly far better than the best solutions obtained by the existing methods.

Abstract: In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Levy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.

1,701 citations

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TL;DR: In this article, the state of the art in vibration-based condition monitoring with particular emphasis on structural engineering applications is reviewed, focusing on the use of in situ non-destructive sensing and analysis of system characteristics for detecting changes, which may indicate damage or degradation.

Abstract: Vibration based condition monitoring refers to the use of in situ non-destructive sensing and analysis of system characteristics –in the time, frequency or modal domains –for the purpose of detecting changes, which may indicate damage or degradation. In the field of civil engineering, monitoring systems have the potential to facilitate the more economical management and maintenance of modern infrastructure. This paper reviews the state of the art in vibration based condition monitoring with particular emphasis on structural engineering applications.

1,394 citations