Conference Item records
https://feeds.library.caltech.edu/people/Taflanidis-Alexandros-Angelos/conference_item.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 14:22:50 +0000Robust Mass Damper Design using Stochastic Simulation
https://resolver.caltech.edu/CaltechAUTHORS:20120905-122344321
Authors: {'items': [{'id': 'Taflanidis-Alexandros-Angelos', 'name': {'family': 'Taflanidis', 'given': 'Alexandros'}, 'orcid': '0000-0002-9784-7480'}, {'id': 'Beck-J-L', 'name': {'family': 'Beck', 'given': 'James'}}, {'id': 'Angelides-D-C', 'name': {'family': 'Angelides', 'given': 'Demos'}}]}
Year: 2006
Mass dampers (for example, TMDs or TLCDs) are widely used for
suppression of structural vibrations. Their design is based on the adjustment
of their parameters, referred to herein as design variables, to
the dynamic characteristics of the coupled damper-structure system.
Uncertainty in the parameters of the model considered for the system
significantly influences the effectiveness of this design. Prior knowledge
about the system is quantified in this study by specifying probability
distributions for the uncertain model parameters. The objective
function for optimal design is chosen to be maximization of the systems
reliability against failure, an appropriate concept for applications
that involve uncertainty. Failure is defined to be exceedance of limit
states for some of the systems response quantities that are important.
Stochastic simulation is used to evaluate the systems response to efficiently
address the high complexity that exists in many applications
of mass dampers and to overcome the limitations related to analytical
approximations when there are non-linearities (e.g associated with
TLCDs or base-isolated civil structures) or when there is a need to
consider transient response. The use of stochastic simulation for the
calculation of the objective function, however, significantly increases
the computational cost for the required optimization with respect to the
design variables. An efficient approach is proposed for this task. It
combines two algorithms suggested for simulation-based optimization:
the Stochastic Subset Optimization, for identification of a set that
icludes the design variables, and the Simultaneous Perturbation Stochastic
Averaging with common random numbers, for pinpointing the
optimal solution inside the identified set. Examples are presented that
show the efficiency of the proposed design for cases with model uncertainty.
The suggested techniques have applications to a large variety
of excitations and simple or complex systems.https://authors.library.caltech.edu/records/8cc9v-hfc95Reliability-based Performance Objectives and Probabilistic Model Uncertainty in Optimal Structural Control Applications
https://resolver.caltech.edu/CaltechAUTHORS:20120905-120329310
Authors: {'items': [{'id': 'Taflanidis-Alexandros-Angelos', 'name': {'family': 'Taflanidis', 'given': 'Alexandros'}, 'orcid': '0000-0002-9784-7480'}, {'id': 'Scruggs-J-T', 'name': {'family': 'Scruggs', 'given': 'Jeffrey'}}, {'id': 'Beck-J-L', 'name': {'family': 'Beck', 'given': 'James'}}]}
Year: 2006
A reliability-based structural control design approach is presented,
which optimizes a control system explicitly to minimize the probability of structural failure. Here, failure is interpreted as the probability that
the system state trajectory will exit a safe region, inside a given time
duration. This safe region is bounded by hyperplanes in the system
state space, each of which corresponds to an important dynamic
response variable. The failure threshold for each of these response
variables is designated as a bound on acceptable performance. Thus
defined, an accurate analytical approximation for the probability of
failure, and for its optimization through feedback control, are
discussed. Versions of the approach are described for the case with no
model uncertainty as well as for the case with uncertain model parameters.
For the case with uncertain parameters it is reasonable to
assume that, in most engineering applications, there will be a considerable
knowledge about the relative likelihood of the different possible
values of these parameters. This information is quantified through the
use of probability distributions on the uncertain parameter space. The
standard tools for design of feedback controllers which are robust to
model uncertainty, such as Hoo and miou-synthesis, do not account
for such probabilistic information.Examples are presented which
apply the above ideas to a simple active structural control system. The
influence of model uncertainty in the optimization process and the
advantages of adopting a probabilistic uncertainty approach are
discussed.https://authors.library.caltech.edu/records/n03mf-rqz17Smart Base Isolation Design including Model Uncertainty in Ground Motion Characterization
https://resolver.caltech.edu/CaltechAUTHORS:20120904-164556868
Authors: {'items': [{'id': 'Taflanidis-Alexandros-Angelos', 'name': {'family': 'Taflanidis', 'given': 'A. A.'}, 'orcid': '0000-0002-9784-7480'}, {'id': 'Scruggs-J-T', 'name': {'family': 'Scruggs', 'given': 'J. T.'}}, {'id': 'Beck-J-L', 'name': {'family': 'Beck', 'given': 'J. L.'}}]}
Year: 2012
N/Ahttps://authors.library.caltech.edu/records/7pxkr-7q302