[
    {
        "id": "authors:49ec2-8nc41",
        "collection": "authors",
        "collection_id": "49ec2-8nc41",
        "cite_using_url": "https://resolver.caltech.edu/CaltechEERL:1998.EERL-97-03",
        "type": "monograph",
        "title": "A performance-based optimal structural design methodology",
        "author": [
            {
                "family_name": "Beck",
                "given_name": "James L.",
                "clpid": "Beck-J-L"
            },
            {
                "family_name": "Papadimitriou",
                "given_name": "Costas",
                "orcid": "0000-0002-9792-0481",
                "clpid": "Papadimitriou-Costas"
            },
            {
                "family_name": "Chan",
                "given_name": "Eduardo",
                "clpid": "Chan-Eduardo"
            },
            {
                "family_name": "Irfanoglu",
                "given_name": "Ayhan",
                "orcid": "0000-0001-8334-6717",
                "clpid": "Irfanoglu-Ayhan"
            }
        ],
        "abstract": "A general framework for multi-criteria optimal design is presented which is well-suited for performance-based design of structural systems operating in an uncertain dynamic environment. A decision theoretic approach is used which is based on aggregation of preference functions for the multiple, possibly conflicting, design criteria. This allows the designer to trade off these criteria in a controlled manner during the optimization. Reliability-based design criteria are used to maintain user-specified levels of structural safety by properly taking into account the uncertainties in the modeling and seismic loads that a structure may experience during its lifetime. Code-based requirements are also easily incorporated into this optimal design process. The methodology is demonstrated with two simple examples involving the design of a three-story steel-frame building for which the ground motion uncertainty is characterized by a probabilistic response spectrum which is developed from available attenuation formulas and seismic hazard models.",
        "publisher": "California Institute of Technology",
        "publication_date": "1998-01-01"
    },
    {
        "id": "authors:xdqf3-f0569",
        "collection": "authors",
        "collection_id": "xdqf3-f0569",
        "cite_using_url": "https://resolver.caltech.edu/CaltechEERL:1997.EERL-97-06",
        "type": "monograph",
        "title": "Optimal design of building structures using genetic algorithms",
        "author": [
            {
                "family_name": "Chan",
                "given_name": "Eduardo",
                "clpid": "Chan-Eduardo"
            }
        ],
        "abstract": "A general framework for multi-criteria optimal design is presented which is well-suited for automated design of structural systems. A systematic computer-aided optimal design decision process is developed which allows the designer to rapidly evaluate and improve a proposed design by taking into account the major factors of interest related to different aspects such as design, construction, and operation.\n\nThe proposed optimal design process requires the selection of the most promising choice of design parameters taken from a large design space, based on an evaluation using specified criteria. The design parameters specify a particular design, and so they relate to member sizes, structural configuration, etc. The evaluation of the design uses performance parameters which may include structural response parameters, risks due to uncertain loads and modeling errors, construction and operating costs, etc. Preference functions are used to implement the design criteria in a \"soft\" form. These preference functions give a measure of the degree of satisfaction of each design criterion. The overall evaluation measure for a design is built up from the individual measures for each criterion through a preference combination rule. The goal of the optimal design process is to obtain a design that has the highest overall evaluation measure - an optimization problem.\n\nGenetic algorithms are stochastic optimization methods that are based on evolutionary theory. They provide the exploration power necessary to explore high-dimensional search spaces to seek these optimal solutions. Two special genetic algorithms, hGA and vGA, are presented here for continuous and discrete optimization problems, respectively.\n\nThe methodology is demonstrated with several examples involving the design of truss and frame systems. These examples are solved by using the proposed hGA and vGA.",
        "publisher": "California Institute of Technology",
        "publication_date": "1997-06-10"
    }
]