Computational Methods for Identification of Vibrating Structures

System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system" [1]. In the context of civil engineering, the system refers to a large scale structure such as a building, bridge, or an offshore structure, and identification mostly involves the determination of modal parameters (the natural frequencies, damping ratios, and mode shapes). This paper presents some modal identification results obtained using a state-of-the-art time domain system identification method (data-driven stochastic subspace algorithms [2]) applied to the output-only data measured in a steel arch bridge. First, a three dimensional finite element model was developed for the numerical analysis of the structure using ANSYS. Modal analysis was carried out and modal parameters were extracted in the frequency range of interest, 0-10 Hz. The results obtained from the finite element modal analysis were used to determine the location of the sensors. After that, ambient vibration tests were conducted during April 23-24, 2009. The response of the structure was measured using eight accelerometers. Two stations of three sensors were formed (triaxial stations). These sensors were held stationary for reference during the test. The two remaining sensors were placed at the different measurement points along the bridge deck, in which only vertical and transversal measurements were conducted (biaxial stations). Point estimate and interval estimate have been carried out in the state space model using these ambient vibration measurements. In the case of parametric models (like state space), the dynamic behaviour of a system is described using mathematical models. Then, mathematical relationships can be established between modal parameters and estimated point parameters (thus, it is common to use experimental modal analysis as a synonym for system identification). Stable modal parameters are found using a stabilization diagram. Furthermore, this paper proposes a method for assessing the precision of estimates of the parameters of state-space models (confidence interval). This approach employs the nonparametric bootstrap procedure [3] and is applied to subspace parameter estimation algorithm. Using bootstrap results, a plot similar to a stabilization diagram is developed. These graphics differentiate system modes from spurious noise modes for a given order system. Additionally, using the modal assurance criterion, the experimental modes obtained have been compared with those evaluated from a finite element analysis. A quite good agreement between numerical and experimental results is observed.

Damage identification from power spectrum density transmissibility

Damage identification under real operating conditions of the structure during its daily use would be suitable and attractive to civil engineers due to the difficulty and problems of carrying out controlled forced excitation tests on this kind of structures. In this case, output-only response measurements would be available, and an output-only damage identification procedure should be implemented. Transmissibility, defined on an output-to-output relationship, is getting increased attention in damage detection applications because of its dependence with output-only data and its sensitivity to local structural changes. In this paper, a method based on the power spectrum density transmissibility (PSDT) is proposed to detect structural damage.

Análisis dinámico experimental y numérico de una presa bóveda : ajuste del modelo

El objetivo de este trabajo es analizar las propiedades dinámicas de una presa bóveda de doble curvatura (presa de La Tajera, Guadalajara) para ajustar un modelo de elementos finitos. Para ello se han utilizado acelerómetros de alta sensibilidad sincronizados inalámbricamente. Se han obtenido las frecuencias, amortiguamientos y formas modales frente a los efectos de las acciones de tipo ambiental (viento, paso de vehículos). Se ha modelado mediante elementos finitos la presa y su cimiento incorporando el efecto del nivel del embalse. Con las propiedades dinámicas de la estructura halladas numéricamente se ha realizado un plan de medidas en los puntos que se consideraban más significativos. Tras realizar las medidas, se ha procedido al análisis de resultados mediante un Análisis Modal Operacional. Ello permite estimar los parámetros modales (frecuencias, amortiguamientos y formas modales) experimentalmente y se ha valorado el alcance de los mismos. Posteriormente viene la parte fundamental de este trabajo, que es el ajuste del modelo de elementos finitos inicial considerando el comportamiento dinámico obtenido experimentalmente. El modelo actualizado puede utilizarse dentro de un sistema de detección de daños, o por ejemplo, para el estudio del comportamiento ante un sismo considerando la interacción presa-embalse-cimiento. The purpose of this paper is to study the dynamic characteristics of a double curvature arch dam (La Tajera arch dam) for a Finite Element Model Updating. To achieve it, high sensitivity accelerometers synchronized wirelessly have been used. The system modal dampings, natural frequencies mode shapes are identified using output only identification techniques under environmental loads (wind, vehicles). Firstly, a finite element model of the dam-reservoir-foundation system was created. Once the dynamic properties of the structure were numerically obtained, a testing plan was then carried out identifying the most significant test points. After the measurements were carried out, an Operational Modal Analysis was performed to obtain experimentally the structure dynamic properties: natural frequencies, modal dampings and mode shapes. experimentally and to assess its reach. Then, the finite element model updating of the initial model was carried out to match the recorded dynamic behavior. The updated model may be used within a structural health monitoring and damage detection system or, as it is proposed on this thesis, for the analysis of the seismic response of arch dam-reservoir-foundation coupled systems

A procedure to track vibration modes under changing external factors: application to a pedestrian bridge

A low-cost vibration monitoring system has been developed and installed on an urban steel- plated stress-ribbon footbridge. The system continuously measures: the acceleration (using 18 triaxial MEMS accelerometers distributed along the structure), the ambient temperature and the wind velocity and direction. Automated output-only modal parameter estimation based on the Stochastic Subspace Identification (SSI) is carried out in order to extract the modal parameters, i.e., the natural frequencies, damping ratios and modal shapes. Thus, this paper analyzes the time evolution of the modal parameters over a whole-year data monitoring. Firstly, for similar environmental/operational factors, the uncertainties associated to the time window size used are studied and quantified. Secondly, a methodology to track the vibration modes has been established since several of them with closely-spaced natural frequencies are identified. Thirdly, the modal parameters have been correlated against external factors. It has been shown that this stress-ribbon structure is highly sensitive to temperature variation (frequency changes of more than 20%) with strongly seasonal and daily trends

State space models for multi-setup operational modal analysis

Operational Modal Analysis consists on estimate the modal parameters of a structure (natural frequencies, damping ratios and modal vectors) from output-only vibration measurements. The modal vectors can be only estimated where a sensor is placed, so when the number of available sensors is lower than the number of tested points, it is usual to perform several tests changing the position of the sensors from one test to the following (multiple setups of sensors): some sensors stay at the same position from setup to setup, and the other sensors change the position until all the tested points are covered. The permanent sensors are then used to merge the mode shape estimated at each setup (or partial modal vectors) into global modal vectors. Traditionally, the partial modal vectors are estimated independently setup by setup, and the global modal vectors are obtained in a postprocess phase. In this work we present two state space models that can be used to process all the recorded setups at the same time, and we also present how these models can be estimated using the maximum likelihood method. The result is that the global mode shape of each mode is obtained automatically, and subsequently, a single value for the natural frequency and damping ratio of the mode is computed. Finally, both models are compared using real measured data.

Hippocampal acetylcholine release during memory testing in rats: augmentation by glucose.

Several lines of evidence indicate that a modest increase in circulating glucose levels enhances memory. One mechanism underlying glucose effects on memory may be an increase in acetylcholine (ACh) release. The present experiment determined whether enhancement of spontaneous alternation performance by systemic glucose treatment is related to an increase in hippocampal ACh output. Samples of extracellular ACh were assessed at 12-min intervals using in vivo microdialysis with HPLC-EC. Twenty-four minutes after an intraperitoneal injection of saline or glucose (100, 250, or 1000 mg/kg), rats were tested in a four-arm cross maze for spontaneous alternation behavior combined with microdialysis collection. Glucose at 250 mg/kg, but not 100 or 1000 mg/kg, produced an increase in spontaneous alternation scores (69.5%) and ACh output (121.5% versus baseline) compared to alternation scores (44.7%) and ACh output (58.9% versus baseline) of saline controls. The glucose-induced increase in alternation scores and ACh output was not secondary to changes in locomotor activity. Saline and glucose (100-1000 mg/kg) treatment had no effect on hippocampal ACh output when rats remained in the holding chamber. These findings suggest that glucose may enhance memory by directly or indirectly increasing the release of ACh. The results also indicate that hippocampal ACh release is increased in rats performing a spatial task. Moreover, because glucose enhanced ACh output only during behavioral testing, circulating glucose may modulate ACh release only under conditions in which cholinergic cells are activated.

The Hurst exponent as an indicator of the behaviour of a model monopile in an ocean wave testing basin

With the importance of renewable energy well-established worldwide, and targets of such energy quantified in many cases, there exists a considerable interest in the assessment of wind and wave devices. While the individual components of these devices are often relatively well understood and the aspects of energy generation well researched, there seems to be a gap in the understanding of these devices as a whole and especially in the field of their dynamic responses under operational conditions. The mathematical modelling and estimation of their dynamic responses are more evolved but research directed towards testing of these devices still requires significant attention. Model-free indicators of the dynamic responses of these devices are important since it reflects the as-deployed behaviour of the devices when the exposure conditions are scaled reasonably correctly, along with the structural dimensions. This paper demonstrates how the Hurst exponent of the dynamic responses of a monopile exposed to different exposure conditions in an ocean wave basin can be used as a model-free indicator of various responses. The scaled model is exposed to Froude scaled waves and tested under different exposure conditions. The analysis and interpretation is carried out in a model-free and output-only environment, with only some preliminary ideas regarding the input of the system. The analysis indicates how the Hurst exponent can be an interesting descriptor to compare and contrast various scenarios of dynamic response conditions.

Footbridge system identification using wireless inertial measurement units for force and response measurements

With the main focus on safety, design of structures for vibration serviceability is often overlooked or mismanaged, resulting in some high profile structures failing publicly to perform adequately under human dynamic loading due to walking, running or jumping. A standard tool to inform better design, prove fitness for purpose before entering service and design retrofits is modal testing, a procedure that typically involves acceleration measurements using an array of wired sensors and force generation using a mechanical shaker. A critical but often overlooked aspect is using input (force) to output (response) relationships to enable estimation of modal mass, which is a key parameter directly controlling vibration levels in service.

This paper describes the use of wireless inertial measurement units (IMUs), designed for biomechanics motion capture applications, for the modal testing of a 109 m footbridge. IMUs were first used for an output-only vibration survey to identify mode frequencies, shapes and damping ratios, then for simultaneous measurement of body accelerations of a human subject jumping to excite specific vibrations modes and build up bridge deck accelerations at the jumping location. Using the mode shapes and the vertical acceleration data from a suitable body landmark scaled by body mass, thus providing jumping force data, it was possible to create frequency response functions and estimate modal masses.

The modal mass estimates for this bridge were checked against estimates obtained using an instrumented hammer and known mass distributions, showing consistency among the experimental estimates. Finally, the method was used in an applied research application on a short span footbridge where the benefits of logistical and operational simplicity afforded by the highly portable and easy to use IMUs proved extremely useful for an efficient evaluation of vibration serviceability, including estimation of modal masses.

Robust model predictive controller with output feedback and target tracking

A model predictive controller (MPC) is proposed, which is robustly stable for some classes of model uncertainty and to unknown disturbances. It is considered as the case of open-loop stable systems, where only the inputs and controlled outputs are measured. It is assumed that the controller will work in a scenario where target tracking is also required. Here, it is extended to the nominal infinite horizon MPC with output feedback. The method considers an extended cost function that can be made globally convergent for any finite input horizon considered for the uncertain system. The method is based on the explicit inclusion of cost contracting constraints in the control problem. The controller considers the output feedback case through a non-minimal state-space model that is built using past output measurements and past input increments. The application of the robust output feedback MPC is illustrated through the simulation of a low-order multivariable system.

Acute high-intensity interval training improves T-vent and peak power output in highly trained males.

This study examined the effects of four high-intensity interval-training (HIT) sessions performed over 2 weeks on peak volume of oxygen uptake (VO2peak), the first and second ventilatory thresholds (UT VT2) and peak power output (PPO) in highly trained cyclists. Fourteen highly trained male cyclists (VO2peak = 67.5 +/- 3.7 ml . kg(-1) . min(-1)) performed a ramped cycle test to determine VO2peak VT1 VT2, and PPO. Subjects were divided equally into a HIT group and a control group. The HIT group performed four HIT sessions (20 x 60 s at PPO, 120 s recovery); the V-02peak test was repeated <I wk after the HIT program. Control subjects maintained their regular training program and were reassessed under the same timeline. There was no change in V0(2peak) for either group; however, the HIT group showed a significantly greater increase in VT1, (+22% vs. -3%), VT2 (+15% vs. -1%), and PPO (+4.3 vs. -.4%) compared to controls (all P <.05). This study has demonstrated that HIT can improve VT1, VT2,, and PPO, following only four HIT sessions in already highly trained cyclists.

Research output of Australian general practice: A comparison with medicine, surgery and public health

Objectives: To compare the productivity of Australian general practice in terms of research publications with the productivity of other medical disciplines. Design: A survey of Australian general practice, medicine, surgery and public health publications carried out by manual searching of specific journals and an electronic search of the US National Library of Medicine's PubMed database. Main outcome measures: The number of original research publications by Australian general practitioners, physicians, surgeons and public health physicians during 1999; the relative publication rate of Australian general practice, medicine, surgery and public health over the period 1990-1999. Results: Of original research articles published in 1999, GPs authored 65% (17/26) in Australian Family Physician and 3% (3/90) in the Medical Journal of Australia; physicians published 4% and 37%, respectively. The electronic search identified 54 research articles relating to Australian general practice published in 1999 in 21 different journals, only two of which were primary care journals. Over the period 1990-1999, there was a publication rate of one general practice [discipline] article per 1000 GPs in practice per year. Corresponding rates for medicine, surgery and public health were 105/1000, 61/1000 and 148/1000, respectively. Conclusions: There is considerable disparity between the level of research output of general practice and that of the disciplines of medicine, surgery and public health. If we are to have effective general practice research, we urgently need to develop research skills, a supportive infrastructure and a culture that nurtures research.

Macroeconomic policy in an output-expenditure model

This paper proposes an alternative framework for examining the international macroeconomic impact of domestic monetary and fiscal policies and focuses on the distinction between national spending and national production and the reactive behavior of foreign investors to changing external account balances. It demonstrates that under a floating exchange rate regime, monetary and fiscal policies can affect aggregate expenditure and output quite differently, with important implications for the behavior of the exchange rate, the current account balance, and national income in the short run, as well as the economy's price level in the long run. In particular, this paper predicts that expansionary monetary and fiscal policies tend to depreciate the currency and only temporarily raise gross domestic product and the current account surplus, although permanently raise the domestic price level. This is a revised version of a paper presented at the Forty-Ninth International Atlantic Economic Conference, March 14–21, 2000, Munich, Germany.

Structural decomposition analysis and input-output subsystems: An application to Spanish CO2 emissions

Analysis of gas emissions by the input-output subsystem approach provides detailed insight into pollution generation in an economy. Structural decomposition analysis, on the other hand, identifies the factors behind the changes in key variables over time. Extending the input-output subsystem model to account for the changes in these variables reveals the channels by which environmental burdens are caused and transmitted throughout the production system. In this paper we propose a decomposition of the changes in the components of CO2 emissions captured by an input-output subsystems representation. The empirical application is for the Spanish service sector, and the economic and environmental data are for years 1990 and 2000. Our results show that services increased their CO2 emissions mainly because of a rise in emissions generated by non-services to cover the final demand for services. In all service activities, the decomposed effects show an increase in CO2 emissions due to a decrease in emission coefficients (i.e., emissions per unit of output) compensated by an increase in emissions caused both by the input-output coefficients and the rise in demand for services. Finally, large asymmetries exist not only in the quantitative changes in the CO2 emissions of the various services but also in the decomposed effects of these changes. Keywords: structural decomposition analysis, input-output subsystems, CO2 emissions, service sector.

Water Reallocation in the Input-Output Model

Water reallocation between economic agents has been –and continues to be- the subject of a considerable amount of research. This paper proposes a method for evaluating how water is reallocated within the economy in response to changes in final demand and changes in the technical water needs of activities and consumers. The empirical application, which is for the Catalan economy, shows important asymmetries in the effects that exogenous inflows and changes in water technical requirements cause on water reallocation. In the process of water distribution, exogenous inflows mostly benefit agriculture and damage private consumers. On the other hand, increases in technical water requirements have negative effects on agriculture and positive effects on the other production activities. The results of the study suggest that agriculture is an important activity not only in terms of water distribution but also in terms of water reallocation due to changes in final demand and technical water needs. Keywords: Water reallocation, water distribution, exogenous shock, technical water needs.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.