Evolutionary Algorithms and Metaheuristics in Civil Engineering and Construction Management

Author:   Jorge Magalhães-Mendes ,  David Greiner
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2015
Volume:   39
ISBN:  

9783319204055


Pages:   127
Publication Date:   23 July 2015
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Evolutionary Algorithms and Metaheuristics in Civil Engineering and Construction Management


Overview

This book focuses on civil and structural engineering and construction management applications. The contributions constitute modified, extended and improved versions of research presented at the  minisymposium organized by the editors at the ECCOMAS conference on this topic in Barcelona 2014.

Full Product Details

Author:   Jorge Magalhães-Mendes ,  David Greiner
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2015
Volume:   39
Dimensions:   Width: 15.50cm , Height: 1.00cm , Length: 23.50cm
Weight:   3.376kg
ISBN:  

9783319204055


ISBN 10:   331920405
Pages:   127
Publication Date:   23 July 2015
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

On-line metamodel-assisted optimization with mixed variables.- Comparing the fully stressed design and the minimum constrained weight solutions in truss structures.- Multiobjective optimization of time-cost using a multi-mode genetic algorithm.- On efficiency increase of evoluationary algorithms for large non-linear constrained optimization problems with applications to mechanics.- Lightweight optimization for additive manufacturing parts based on genetic algorithms, metamodels and infinite element analysis.-Calibration of dynamic models of railway bridges based on genetic algorithms.- A procedure for improving the acoustic efficiency of top-edge devices on noise barriers: an application of genetic algorithms and boundary elements.

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