Towards Agent-based Test Support Systems: An Unsupervised Environment Design Approach
This addresses the need for more accurate and adaptable modal testing in engineering industries, though it appears incremental as it builds on existing reinforcement learning methods for a specific domain.
The paper tackles the problem of designing effective modal test campaigns in structural analysis by introducing an agent-based decision support framework for adaptive sensor placement in dynamically changing environments, demonstrating its efficacy in optimizing sensor locations across frequency segments on a steel cantilever structure.
Modal testing plays a critical role in structural analysis by providing essential insights into dynamic behaviour across a wide range of engineering industries. In practice, designing an effective modal test campaign involves complex experimental planning, comprising a series of interdependent decisions that significantly influence the final test outcome. Traditional approaches to test design are typically static-focusing only on global tests without accounting for evolving test campaign parameters or the impact of such changes on previously established decisions, such as sensor configurations, which have been found to significantly influence test outcomes. These rigid methodologies often compromise test accuracy and adaptability. To address these limitations, this study introduces an agent-based decision support framework for adaptive sensor placement across dynamically changing modal test environments. The framework formulates the problem using an underspecified partially observable Markov decision process, enabling the training of a generalist reinforcement learning agent through a dual-curriculum learning strategy. A detailed case study on a steel cantilever structure demonstrates the efficacy of the proposed method in optimising sensor locations across frequency segments, validating its robustness and real-world applicability in experimental settings.