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Author: search.filters.author.Dzeng, RJSubject: search.filters.subject.case-based reasoning

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Now showing 1 - 3 of 3
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    Product modelling to support case-based construction planning and scheduling
    (2004-05-01)
    Dzeng, RJ
    ;
    Tommelein, ID
    ;
    土木工程學系
    ;
    Department of Civil Engineering
    Many human schedulers create schedules by reusing past similar schedules. The retrieval and reuse of similar schedules are subjective and experience-based. This paper explores different notions of similarity required when performing different scheduling tasks. It describes the CasePlan system that helps schedulers retrieve and reuse parts of existing schedules based on a generic product model, and apply case-based reasoning to generate new schedules. The validation experiment demonstrated CasePlan's accuracy in determining individual subnetworks and activity durations, but weak performance in determining interlinks between subnetworks, which highly depends upon the availability of pertinent cases and the level of detail of project information. (C) 2004 Elsevier B.V. All rights reserved.
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    Critiquing contractors' scheduling by integrating rule-based and case-based reasoning
    (2004-09-01)
    Dzeng, RJ
    ;
    Lee, HY
    ;
    土木工程學系
    ;
    Department of Civil Engineering
    Nowadays, projects involve many activities. Hence, the review process requires the scheduler to be experienced both in field operations and in computer scheduling software. However, the dilemma faced by contractors is that many senior project managers are experienced in the field but not in computers, whereas many young engineers are skilled in computers but not in field operations. This work proposes a generalized framework to represent schedule knowledge, and a computer-based system that analyzes a project schedule and offers corrective advice on potential errors by integrating case-based and rule-based reasoning. (C) 2004 Elsevier B.V. All rights reserved.
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    Learning heuristics for determining slurry wall panel lengths
    (2006-05-01)
    Dzeng, RJ
    ;
    Pan, NF
    ;
    土木工程學系
    ;
    Department of Civil Engineering
    Determining panel lengths for slurry walls is an engineering issue that involves complex geotechnical, design, and site considerations. In practice, the decision is made through a trial-and-error process. Relevant principles extracted from experts are not sufficiently detailed to generate a solution. This research proposes an inductive learning model for solving this problem. Given a new project whose panel lengths need to be determined, the model chooses similar cases from existing cases, based on case-based reasoning, performs an inductive learning, and uses correctness and coverage rates, and then static rules to verify the induced results. (c) 2005 Elsevier B.V. All rights reserved.