Training of a Classifier for Structural Component Failure Based on Hybrid Simulation and Kriging

Abbiati, Giuseppe; Marelli, Stefano; Ligeikis, Connor; Christenson, Richard; Stojadinović, Božidar

doi:10.1061/(asce)em.1943-7889.0002048

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…Ritto and Rochinha [352] constructed a hybrid model calibrated with measured and simulated data to identify damage severity and location in a bar structure. Abbiati et al [124] implemented a hybrid model to detect Euler buckling failure in a beam using GPR and AL to assess structural reliability. Zhang and Sun [125] trained an NN guided by FEM results to improve generality and physical consistency in damage detection.…”

Section: Hybrid Modelsmentioning

confidence: 99%

A review of machine learning methods applied to structural dynamics and vibroacoustic

Cunha

Droz

Zine

et al. 2023

Mechanical Systems and Signal Processing

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Section: Hybrid Modelsmentioning

confidence: 99%

A review of machine learning methods applied to structural dynamics and vibroacoustic

Cunha

Droz

Zine

et al. 2023

Mechanical Systems and Signal Processing

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“…Hybrid models integrating physics-based and MLbased models are also found in the literature for fault detection and diagnosis. Abbiati et al [84] implemented a Hybrid Model to detect Euler buckling failure in a beam using Kriging meta-models and active learning to assess structural reliability. The Hybrid model proposed by Ritto and Rochinha [85] configures a Digital Twin due to its bi-directional connection, which allows the model to be calibrated with data from the physical twin (real asset) and the digital twin predictions can be used to update the physical twin operation parameters and control strategy.…”

Section: Failure Detection and Diagnosismentioning

confidence: 99%

“…Abbiati et al [152] show a framework to do global sensitivity analysis in hybrid surrogates, which merge physical and numerical substructures, showing an application in a structural dynamic problem modeled by polynomial chaos expansion surrogates. With a similar method, Abbiati et al [84] creates a hybrid model for buckling failure reliability analysis using a GP classifier, obtaining a failure surface prediction with good accuracy against experimental and analytical references.…”

Section: Uncertainty Quantification With Surrogatesmentioning

confidence: 99%

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

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

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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.

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Reliability analysis for data-driven noisy models using active learning

Pires,

Moustapha,

Marelli

et al. 2025

Structural Safety

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Training of a Classifier for Structural Component Failure Based on Hybrid Simulation and Kriging

Cited by 4 publications

References 25 publications

A review of machine learning methods applied to structural dynamics and vibroacoustic

A review of machine learning methods applied to structural dynamics and vibroacoustic

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

Reliability analysis for data-driven noisy models using active learning

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