This book is an up-to-date source for computation applications of optimization, prediction via artificial intelligence methods, and evaluation of metaheuristic algorithm with different structural applications. As the current interest of researcher, metaheuristic algorithms are a high interest topic area since advance and non-optimized problems via mathematical methods are challenged by the development of advance and modified algorithms. The artificial intelligence (AI) area is also important in predicting optimum results by skipping long iterative optimization processes. The machine learning used in generation of AI models also needs optimum results of metaheuristic-based approaches. This book is a great source to researcher, graduate students, and bachelor students who gain project about structural optimization. Differently from the academic use, the chapter that emphasizes different scopes and methods can take the interest and help engineer working in design and production of structural engineering projects.
Author(s): Sinan Melih Nigdeli, Gebrail Bekdaş, Aylin Ece Kayabekir, Melda Yucel
Series: Studies in Systems, Decision and Control, 326
Publisher: Springer
Year: 2021
Language: English
Pages: 340
City: Cham
Preface
Contents
Developments on Metaheuristic-Based Optimization in Structural Engineering
1 Introduction
2 General Definition of Optimization and Metaheuristic Algorithms
2.1 Genetic Algorithm
2.2 Simulated Annealing
2.3 Particle Swarm Optimization
2.4 Harmony Search
2.5 Firefly Algorithm
2.6 Cuckoo Search
2.7 Bat Algorithm
2.8 Teaching Learning Based Optimization
2.9 Flower Pollination Algorithm
2.10 Other Metaheuristic Algorithms Used in Civil Engineering
3 Applications and Optimization in Structural Engineering
3.1 Truss Structures
3.2 Reinforced Concrete (RC) Members
3.3 Frame Structures
3.4 Bridges
3.5 Structural Control
4 Conclusions
References
Artificial Intelligence and Machine Learning with Reflection for Structural Engineering: A Review
1 Introduction
2 Evolution of Artificial Intelligence and Transition Process to Machine Learning
3 Review for Machine Learning Techniques
3.1 Frequently-Used Machine Learning Techniques
4 Artificial Intelligence and Machine Learning Applications
4.1 Prediction Applications on Structural Engineering Via Machine Learning Techniques
5 An Overview for Structural Engineering Applications Via Machine Learning
5.1 Prediction of Optimum Tuned Mass Damper (TMD) Parameters
5.2 Tubular Column Section Sizes Prediction (Case 1 and 2)
5.3 Optimum Design and Estimation of Parameters for I-Beam (Case 1 and 2)
5.4 Prediction Application for Linear Base Isolation Systems
5.5 Estimation of Optimal Section Areas and Minimum Volume of 3-Bar Truss
5.6 Determination of Optimum Section Parameters Regard to Fiber Reinforced Polymer Design
References
Design Optimization of Multi-objective Structural Engineering Problems Via Artificial Bee Colony Algorithm
1 Introduction
2 General Construction for Pareto Optimal Approach Based Multi-objective Optimization
2.1 Metrics for Algorithmic Performance Measurement
3 Artificial Bee Colony Algorithm
3.1 Bees in Habitat
3.2 Concept of Artificial Bee Colony Algorithm
4 Pareto-Based Multi-objective ABC (Mo-ABC) Algorithm
5 Design Examples
5.1 Two-Bar Truss Design Problem
5.2 I-Beam Design Problem
5.3 Steel Welded Beam Design Problem
6 Conclusions
References
Optimal Parameter Identification of Fuzzy Controllers in Nonlinear Buildings Based on Seismic Hazard Analysis Using Tribe-Charged System Search
1 Introduction
2 Seismic Hazard Analysis
3 Fuzzy Logic Controller
4 Optimization Algorithms
4.1 Standard CSS
4.2 Presentation of the Tribe-CSS
5 Design Example
5.1 Structural Details
5.2 Nonlinear Model
5.3 FLC Implementation
5.4 Performance Criteria
6 Statement of the Optimization Problem
7 Numerical Results
7.1 Optimized FLC by Tribe-CSS and CSS
7.2 Variation of Design Variables
7.3 Comparing to Other Metaheuristics
8 Conclusions
References
Current Trends in the Optimization Approaches for Optimal Structural Control
1 Introduction
2 Passive Control
2.1 Tuned Mass Damper
2.2 Fluid Viscous Damper
2.3 Viscoelastic Damper
2.4 Base Isolation
3 Active Control
3.1 Active Tuned Mass Damper
3.2 Active Tendon
4 Semi-active Control
4.1 Magnetorheological Fluid Damper
4.2 Semi-active TMD
4.3 Semi-active Base Isolation
4.4 Other Semi-active Dampers
5 Current Trends in Optimization of Structural Control Devices
5.1 Particle Swarm Optimization and Active Control of Building Structures Using ATMD
5.2 Harmony Search Algorithm (HSA) and Active Control of Jacket Platform Using ATMD
5.3 Soil-Structure Interaction (SSI) and TMD Optimization Using Metaheuristic Methods
5.4 Modified Harmony Search Algorithm (MHSA) for Optimization of ATMD
5.5 H2 and H∞ Optimization Algorithms for Robust Optimization of TMDs Under Near-Fault and Far-Fault Records
5.6 Robust Optimal Control of Tuned Mass Damper Inerter (TMDI)
References
The Effect of SSI and Impulsive Motions on Optimum Active Controlled MDOF Structure
1 Introduction
2 Methodology
3 Numerical Example
4 Conclusion
References
Metaheuristic Algorithms for Optimal Design of Truss Structures
1 Introduction
2 Optimization Algorithms
2.1 Firefly Algorithm (FA)
2.2 Teaching and Learning Based Optimization (TLBO) Algorithm
2.3 Drosophila Food-Search Optimization (DSO)
2.4 Ions Motion Optimization (IMO)
2.5 Interactive Search Algorithm (ISA)
3 Structural Optimization
4 Numerical Examples
4.1 A 160-Bar Pyramid Truss
4.2 A 120-Bar Dome Structure
4.3 A 582-Bar Spatial Truss Tower
4.4 A 18-Bar Planar Truss
References
Total Potential Optimization Using Hybrid Metaheuristics: A Tunnel Problem Solved via Plane Stress Members
1 Introduction
2 The Total Potential Energy of Plane Stress Members
3 The Analysis Methodology
3.1 Hybrid Methods
4 Numerical Examples
4.1 Cantilever Beam Under a Concentered Load
4.2 Tunnel System
5 Conclusion and Future Studies
References
Buckling Analysis and Stacking Sequence Optimization of Symmetric Laminated Composite Plates
1 Introduction
2 Analytical Techniques for the Computation of the Buckling Load of Orthotropic Symmetric Plates
3 Methods and Results
3.1 Stacking Sequence Optimization
4 Conclusion
References
Sustainable Optimum Design of RC Retaining Walls: The Influence of Structural Material and Surrounding Soil Properties
1 Introduction
2 Materials and Methods
3 Parametric Analysis
4 Results and Discussion
4.1 The Minimization of Total Cost
4.2 The Minimization of CO2 Emission
5 Conclusions
References
Statistical Evaluation of Metaheuristic Algorithm: An Optimum Reinforced Concrete T-beam Problem
1 Introduction
2 The Optimization Process
3 The Numerical Example
4 Statistical Evaluation
4.1 Friedman Ranking as Non-parametric Test
4.2 Parametric Tests: One-Way ANOVA, Post hoc Bonferroni Test, and Independent Samples T-test
5 Conclusions
References
