Artificial Neural Network (ANN)-Bayesian Probability Framework (BPF) based method of dynamic force reconstruction under multi-source uncertainties
In view of the universal existence of multi-source uncertainty factors in engineering
structures, a novel method of dynamic force reconstruction is investigated based on Artificial …
structures, a novel method of dynamic force reconstruction is investigated based on Artificial …
Discussing the spectrum of physics-enhanced machine learning: a survey on structural mechanics applications
The intersection of physics and machine learning has given rise to the physics-enhanced
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …
Extraction of contact-point response in indirect bridge health monitoring using an input estimation approach
Identification of bridge dynamic properties from moving vehicle responses presents several
practical benefits. However, a problem that arises when working with vehicle responses for …
practical benefits. However, a problem that arises when working with vehicle responses for …
Information theoretic-based optimal sensor placement for virtual sensing using augmented Kalman filtering
An optimal sensor placement (OSP) framework for virtual sensing using the augmented
Kalman Filter (AKF) technique is presented based on information and utility theory. The …
Kalman Filter (AKF) technique is presented based on information and utility theory. The …
[HTML][HTML] A hierarchical output-only Bayesian approach for online vibration-based crack detection using parametric reduced-order models
This contribution presents a hierarchical Bayesian filter for recursive input, state and
parameter estimation using spatially incomplete and noisy output-only vibration …
parameter estimation using spatially incomplete and noisy output-only vibration …