The group's research vision is to:
The research programme is structured in four research themes:
This research theme focuses on the development of advanced linear and non-linear models (and their validation via experiments) for assessing the dynamic performance of structural materials and complex built-up structures .
Ongoing research topics include: Friction; Constrained Layer Damping; jointed beam elements with different material proprieties and cross section; damaged beams.
1) Marino, Cicirello, Hills, Displacement transmissibility of a Coulomb friction oscillator subject to joined base-wall motion, Nonlinear Dynamics, 2019. Full paper available here
2) Cicirello, On the response bounds of damaged Euler-Bernoulli beams with switching cracks under moving masses, International Journal of Solids and Structures, 2019. Full paper available here
This research theme focuses on the development of advanced experimental and modelling strategies to efficiently characterising manufacturing variability and to account for uncertainty in the input parameter under limited information.
Ongoing research topics include: Vibro-acoustic non-destructive testing; imprecise probability models; using high-speed video recordings.
1) Cicirello, Langley, Probabilistic assessment of performance under uncertain information using a generalized maximum entropy principle, Probabilistic Engineering Mechanics, 2018. Full paper available here
2) Igea, Cicirello, A vibro-acoustic quality control approach for the elastic properties characterisation of thin orthotropic plates, Journal of Physics: Conference Series, 2018, Full paper available here
This theme focuses on the development of efficient uncertainty propagation strategies (forward problem) in order to quantify their effects on the reliability and performance of a structure at the design stage.
Ongoing research topics include: incorporation of uncertainty and variability within wave-based approaches such as the Wave Finite Element Method; propagation of mixed types of uncertainty descriptions, Statistical Energy Analysis, Hybrid Finite Element/Statistical Energy Analysis.
1) Cicirello, Mace, Sensitivity analysis of generalised eigenproblems and application to wave and finite element models, RASD2019, 2019.
2) Langley, Cicirello, Deckers, The effect of generalised force correlations on the response statistics of a harmonically driven random system, Journal of Sound and Vibration, 2018. Full paper available here
This theme is focusing on the combination of physical-based models and data-based models (Machine Learning and Natural Language Processing) for failure prevention.
Ongoing research topics include: Predicting mechanical failure using non-operational data (non-destructive testing, physical models and machine learning); Predicting failures of the monitoring systems using Machine Learning and Natural Language Processing techniques; Improving predictive maintenance; Structural Health Monitoring.
1) Oncescu, Cicirello, Failure detection of low-cost wearable devices using recorded data and reports, UNCECOMP, 2019.
2) Kwok, Cicirello, An approach for detecting failures in real time monitoring systems for automotive applications, UNCECOMP, 2019.
Moreover, we enjoy working in cross-disciplinary environments investigating broader applications.
See for example the paper written in collaboration with the Oxford University Zoology Department on the investigation of the vibration generation mechanism in planthoppers (paper available here).
Vibration performance of built structures (August 2017 - August 2019)
Seed funding to setup the DVU lab
Noise and vibration performance of complex built-up structures with uncertain properties (March 2018 - March 2020)
Collaboration with: Professor Brian Mace (University of Auckland)
Joint EPSRC and Rolls-Royce funded research studentship (January 2018 - December 2021).
Joint EPSRC and Schlumberger funded research studentship (October 2019 - September 2022)
Topic: Mechanical Failure Prediction using non-Real Time Condition Monitoring