17,041 research outputs found
Structural health monitoring and bridge condition assessment
Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) methodâs application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work
Seismic response trends evaluation via long term monitoring and finite element model updating of an RC building including soil-structure interaction
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Long term seismic response monitoring and finite element modeling of a concrete building considering soil flexibility and non-structural components
Peer reviewedPostprin
Finite element model updating of a RC building considering seismic response trends
ACKNOWLEDGEMENTS The authors would like to thank their supporters. GeoNet staff, particularly Dr Jim Cousins, Dr S.R. Uma and Dr Ken Gledhill, helped with access to seismic data and building information. Faheem Buttâs PhD study was funded by Higher Education Commission (HEC) Pakistan. Piotr Omenzetterâs work within The LRF Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by The Lloyd's Register Foundation (The LRF). The LRF supports the advancement of engineering-related education, and funds research and development that enhances safety of life at sea, on land and in the air.Peer reviewedPostprin
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A sub-Nyquist co-prime sampling music spectral approach for natural frequency identification of white-noise excited structures
Motivated by practical needs to reduce data transmission payloads in wireless sensors for vibration-based monitoring of civil engineering structures, this paper proposes a novel approach for identifying resonant frequencies of white-noise excited structures using acceleration measurements acquired at rates significantly below the Nyquist rate. The approach adopts the deterministic co-prime sub-Nyquist sampling scheme, originally developed to facilitate telecommunication applications, to estimate the autocorrelation function of response acceleration time-histories of low-amplitude white-noise excited structures treated as realizations of a stationary stochastic process. This is achieved without posing any sparsity conditions to the signals. Next, the standard MUSIC algorithm is applied to the estimated autocorrelation function to derive a denoised super-resolution pseudo-spectrum in which natural frequencies are marked by prominent spikes. The accuracy and applicability of the proposed approach is numerically assessed using computer-generated noise-corrupted acceleration time-history data obtained by a simulation-based framework pertaining to a white-noise excited structural system with two closely-spaced modes of vibration carrying the same amount of energy, and a third isolated weakly excited vibrating mode. All three natural frequencies are accurately identified by sampling at as low as 78% below Nyquist rate for signal to noise ratio as low as 0dB (i.e., energy of additive white noise equal to the signal energy), suggesting that the proposed approach is robust and noise-immune while it can reduce data transmission requirements in acceleration wireless sensors for natural frequency identification of engineering structures
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
Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges
Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief networkâbased structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted
Uncertainty Updating in the Description of Coupled Heat and Moisture Transport in Heterogeneous Materials
To assess the durability of structures, heat and moisture transport need to
be analyzed. To provide a reliable estimation of heat and moisture distribution
in a certain structure, one needs to include all available information about
the loading conditions and material parameters. Moreover, the information
should be accompanied by a corresponding evaluation of its credibility. Here,
the Bayesian inference is applied to combine different sources of information,
so as to provide a more accurate estimation of heat and moisture fields [1].
The procedure is demonstrated on the probabilistic description of heterogeneous
material where the uncertainties consist of a particular value of individual
material characteristic and spatial fluctuations. As for the heat and moisture
transfer, it is modelled in coupled setting [2]
Dynamic investigation on the Mirandola bell tower in post-earthquake scenarios
After the seismic events of the 20th and 29th of May 2012 in Emilia (Italy), most of the monumental and historic buildings of the area were severely damaged. In a few structures, partial collapse mechanisms were observed (e.g. façade tilting, out-of-plane overturning of panelsâŠ). This paper presents the case-study of the bell tower of the Santa Maria Maggiore cathedral, located in Mirandola (Italy). The dynamic response of the structure was evaluated through operational modal analysis using ambient vibrations, a consolidated non-destructive procedure that estimates the dynamic parameters of the bell-tower. The dynamic tests were carried out in pre-intervention and post-intervention conditions in order to understand the sensitivity of dynamic measurements to safety interventions. Furthermore, a comparative study is made with similar cases of undamaged masonry towers up to the 6th mode. Finally, an investigation on the state of connections and of the building itself is carried out via FE model updating
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