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

TL;DR

This survey reviews how machine learning enhances structural dynamics and vibroacoustic applications, including health monitoring, control, and surrogate modeling, highlighting current methods, challenges, and future opportunities.

Contribution

It provides a comprehensive overview of ML applications in SD&V, analyzing methodologies, advantages, limitations, and emerging trends like Digital Twins and Physics Guided ML.

Findings

ML improves structural health monitoring and maintenance.

ML-based surrogate models enable faster simulations.

Digital Twins and Physics Guided ML are promising future directions.

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.