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Transparent Digital Twin for Output Control Using Belief Rule Base.

Leilei Chang, Limao Zhang, Chao Fu

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    A transparent digital twin (DT) using belief rule base (BRB) models complex systems and controls outputs. This DT-BRB approach effectively reduces building tilt rates in tunnel construction by identifying key parameters.

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    Area of Science:

    • Engineering
    • Computer Science
    • Control Systems

    Background:

    • Complex systems require advanced modeling techniques.
    • Digital twins (DT) offer a virtual representation for analysis and control.
    • Belief Rule Base (BRB) systems provide a framework for handling uncertainty and complex relationships.

    Purpose of the Study:

    • To develop a transparent digital twin (DT-BRB) for output control.
    • To model intricate input-output relationships within a physical system.
    • To identify and optimize critical parameters for enhanced system performance.

    Main Methods:

    • Utilizing BRB to model system input-output dynamics.
    • Implementing an analytical procedure to identify key input parameters based on contribution.
    • Performing data-driven optimization for system output control.

    Main Results:

    • The DT-BRB approach successfully models complex system relationships.
    • An 80% contribution standard identified key parameters, reducing building tilt rate (BTR) by 73.73% in a tunnel construction case study.
    • The method demonstrated effectiveness, potentially negating the need for iterative optimizations.

    Conclusions:

    • The transparent DT-BRB is a viable tool for complex system modeling and output control.
    • Parameter identification and optimization are crucial for improving system performance.
    • The proposed approach offers an efficient solution for real-world engineering challenges like BTR reduction.