11,792 research outputs found
Real-time Loss Estimation for Instrumented Buildings
Motivation. A growing number of buildings have been instrumented to measure and record
earthquake motions and to transmit these records to seismic-network data centers to be archived and
disseminated for research purposes. At the same time, sensors are growing smaller, less expensive to
install, and capable of sensing and transmitting other environmental parameters in addition to
acceleration. Finally, recently developed performance-based earthquake engineering methodologies
employ structural-response information to estimate probabilistic repair costs, repair durations, and
other metrics of seismic performance. The opportunity presents itself therefore to combine these
developments into the capability to estimate automatically in near-real-time the probabilistic seismic
performance of an instrumented building, shortly after the cessation of strong motion. We refer to
this opportunity as (near-) real-time loss estimation (RTLE).
Methodology. This report presents a methodology for RTLE for instrumented buildings. Seismic
performance is to be measured in terms of probabilistic repair cost, precise location of likely physical
damage, operability, and life-safety. The methodology uses the instrument recordings and a Bayesian
state-estimation algorithm called a particle filter to estimate the probabilistic structural response of
the system, in terms of member forces and deformations. The structural response estimate is then
used as input to component fragility functions to estimate the probabilistic damage state of structural
and nonstructural components. The probabilistic damage state can be used to direct structural
engineers to likely locations of physical damage, even if they are concealed behind architectural
finishes. The damage state is used with construction cost-estimation principles to estimate
probabilistic repair cost. It is also used as input to a quantified, fuzzy-set version of the FEMA-356
performance-level descriptions to estimate probabilistic safety and operability levels.
CUREE demonstration building. The procedure for estimating damage locations, repair costs, and
post-earthquake safety and operability is illustrated in parallel demonstrations by CUREE and
Kajima research teams. The CUREE demonstration is performed using a real 1960s-era, 7-story, nonductile
reinforced-concrete moment-frame building located in Van Nuys, California. The building is
instrumented with 16 channels at five levels: ground level, floors 2, 3, 6, and the roof. We used the
records obtained after the 1994 Northridge earthquake to hindcast performance in that earthquake.
The building is analyzed in its condition prior to the 1994 Northridge Earthquake. It is found that,
while hindcasting of the overall system performance level was excellent, prediction of detailed damage
locations was poor, implying that either actual conditions differed substantially from those shown on
the structural drawings, or inappropriate fragility functions were employed, or both. We also found
that Bayesian updating of the structural model using observed structural response above the base of
the building adds little information to the performance prediction. The reason is probably that
Real-Time Loss Estimation for Instrumented Buildings
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structural uncertainties have only secondary effect on performance uncertainty, compared with the
uncertainty in assembly damageability as quantified by their fragility functions. The implication is
that real-time loss estimation is not sensitive to structural uncertainties (saving costly multiple
simulations of structural response), and that real-time loss estimation does not benefit significantly
from installing measuring instruments other than those at the base of the building.
Kajima demonstration building. The Kajima demonstration is performed using a real 1960s-era
office building in Kobe, Japan. The building, a 7-story reinforced-concrete shearwall building, was not
instrumented in the 1995 Kobe earthquake, so instrument recordings are simulated. The building is
analyzed in its condition prior to the earthquake. It is found that, while hindcasting of the overall
repair cost was excellent, prediction of detailed damage locations was poor, again implying either that
as-built conditions differ substantially from those shown on structural drawings, or that
inappropriate fragility functions were used, or both. We find that the parameters of the detailed
particle filter needed significant tuning, which would be impractical in actual application. Work is
needed to prescribe values of these parameters in general.
Opportunities for implementation and further research. Because much of the cost of applying
this RTLE algorithm results from the cost of instrumentation and the effort of setting up a structural
model, the readiest application would be to instrumented buildings whose structural models are
already available, and to apply the methodology to important facilities. It would be useful to study
under what conditions RTLE would be economically justified. Two other interesting possibilities for
further study are (1) to update performance using readily observable damage; and (2) to quantify the
value of information for expensive inspections, e.g., if one inspects a connection with a modeled 50%
failure probability and finds that the connect is undamaged, is it necessary to examine one with 10%
failure probability
Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey
Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks
The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
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