A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Abstract

The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD\&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD\&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD\&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD\&V and guides the reader to an advanced understanding of progress and prospects in the field.

References in corpus (24)

• Deep Learning in Neural Networks: An Overview
• Machine learning and deep learning
• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications
• Zero-Shot Text-to-Image Generation
• Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
• Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder
• Physics guided machine learning using simplified theories
• A Gaussian process latent force model for joint input-state estimation in linear structural systems
• Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling
• Machine Learning for Predicting Thermal Transport Properties of Solids
• A Living Review of Machine Learning for Particle Physics
• Digital Twin: Values, Challenges and Enablers
• Data-driven Experimental Modal Analysis by Dynamic Mode Decomposition
• DR-RNN: A deep residual recurrent neural network for model reduction
• Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks
• Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction
• Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces
• Blending Diverse Physical Priors with Neural Networks
• On the application of generative adversarial networks for nonlinear modal analysis
• Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids
• Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models
• RL-Controller: a reinforcement learning framework for active structural control
• Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias

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• Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems