Multiobjective forensic-based investigation algorithm for solving structural design problems

Abstract Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, it is difficult to concurrently optimize all design variables of portions within the hierarchical structure. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations for manipulating the hierarchical genotype representations are also developed. The proposed method is applied to a machine-tool structural design to demonstrate its effectiveness.

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Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems

Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-0301-0.ch002 ◽

2020 ◽

pp. 13-38

Author(s):

Melda Yucel ◽

Sinan Melih Nigdeli ◽

Gebrail Bekdaş

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Structural Properties ◽

Structural Design ◽

Civil Engineering ◽

Back Propagation ◽

Training Algorithm ◽

Design Problems ◽

Engineering Problems ◽

Artificial Neural

This chapter reveals the advantages of artificial neural networks (ANNs) by means of prediction success and effects on solutions for various problems. With this aim, initially, multilayer ANNs and their structural properties are explained. Then, feed-forward ANNs and a type of training algorithm called back-propagation, which was benefited for these type networks, are presented. Different structural design problems from civil engineering are optimized, and handled intended for obtaining prediction results thanks to usage of ANNs.

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Indexing and retrieval of cases in a case-based design system

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060400001281 ◽

1996 ◽

Vol 10 (1) ◽

pp. 47-63 ◽

Cited By ~ 7

Author(s):

B. Raphael ◽

B. Kumar

Keyword(s):

Structural Design ◽

Design Methods ◽

Design System ◽

Prototype System ◽

Design Problems ◽

New Approach ◽

Indexing And Retrieval ◽

Control Knowledge ◽

Case Based

AbstractThis paper discusses the issues related to retrieval of cases in a case-based design system. The limitations of the conventional approaches are brought out, and this is followed by a description of a new approach to indexing and retrieval. This approach aims at capturing control knowledge required for retrieval from individual retrieval examples. Since this approach is based on past retrieval examples, we call it RBEX (Retrieval Based on EXamples). The proposed approach is implemented in a prototype system called CADREM. CADREM uses design methods used in solving past design problems to solve new problems. This is closer to the derivational analogy approach rather than the more common transformational analogy. CADREM has been tried in the domain of conceptual structural design of buildings. Sample outputs from CADREM are also included.

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