Finite element model updating using objective-consistent sensitivity-based parameter clustering and Bayesian regularization

Finite element model updating seeks to modify a structural model to reduce discrepancies between predicted and measured data, often from vibration studies. An updated model provides more accurate prediction of structural behavior in future analyses. Sensitivity-based parameter clustering and regularization are two techniques used to improve model updating solutions, particularly for high-dimensional parameter spaces and ill-posed updating problems. In this paper, a novel parameter clustering scheme is proposed which considers the structure of the objective function to facilitate simultaneous updating of disparate data, such as natural frequencies and mode shapes. In a small-scale updating example with simulated data, the proposed clustering scheme is shown to provide moderate to excellent improvement over existing parameter clustering methods, depending on the accuracy of initial model. A full-scale updating example on a large suspension bridge shows similar improvement using the proposed parametrization scheme. Levenberg-Marquardt minimization with Bayesian regularization is also implemented, providing an optimal regularized solution and insight into parametrization efficiency

Sensitivity‐based singular value decomposition parametrization and optimal regularization in finite element model updating

Model updating is used to reduce error between measured structural responses and corresponding finite element (FE) model outputs, which allows accurate prediction of structural behavior in future analyses. In this work, reduced‐order parametrizations of an underlying FE model are developed from singular value decomposition (SVD) of the sensitivity matrix, thereby improving efficiency and posedness in model updating. A deterministic error minimization scheme is combined with asymptotic Bayesian inference to provide optimal regularization with estimates for model evidence and parameter efficiency. Natural frequencies and mode shapes are targeted for updating in a small‐scale example with simulated data and a full‐scale example with real data. In both cases, SVD‐based parametrization is shown to have good or better results than subset selection with very strong results on the full‐scale model, as assessed by Bayes factor

Symmetry properties of natural frequency and mode shape sensitivities in symmetric structures

When updating a finite element (FE) model to match the measured properties of its corresponding structure, the sensitivities of FE model outputs to parameter changes are of significant interest. These sensitivities form the core of sensitivity-based model updating algorithms, but they are also used for developing reduced parametrizations, such as in subset selection and clustering. In this work, the sensitivities of natural frequencies and mode shapes are studied for structures having at least one plane of reflectional symmetry. It is first shown that the mode shapes of these structures are either symmetric and anti-symmetric, which is used to prove that natural frequency sensitivities are equal for symmetric parameters. Conversely, mode shape sensitivities are shown to be unequal for symmetric parameters, as measured by cosine distance. These topics are explored with a small numerical example, where it is noted that mode shape sensitivities for symmetric parameters exhibit similar properties to asymmetric parameters

Rate of improvement of CF life expectancy exceeds that of general population: observational death registration study

Background: It is unclear why cystic fibrosis (CF) survival has improved. We wished to quantify increases in CF median age of death in the context of general population survival improvement. Method: Death registration data analysis (US, England & Wales (E&W)—1972–2009). Results: CF median age of death is higher in US than E&W and greater for males, opposite to that of death from all causes. CF median age of death has increased by 0.543 life years per year (E&W, US combined (95% confidence interval 0.506, 0.582)). The difference in median age at death between those dying from all causes and CF decreased in both territories. CF median age of death for males is greater than for females in both territories. This gap has not narrowed. Conclusion: The median age of death of people with CF is improving more rapidly than that of the general population in US and E&W

Cluster Analysis of Typhoon Tracks. Part II: Large-Scale Circulation and ENSO

A new probabilistic clustering method, based on a regression mixture model, is used to describe tropical cyclone (TC) propagation in the western North Pacific (WNP). Seven clusters were obtained and described in Part I of this two-part study. In Part II, the present paper, the large-scale patterns of atmospheric circulation and sea surface temperature associated with each of the clusters are investigated, as well as associations with the phase of the El Niño–Southern Oscillation (ENSO). Composite wind field maps over the WNP provide a physically consistent picture of each TC type, and of its seasonality. Anomalous vorticity and outgoing longwave radiation indicate changes in the monsoon trough associated with different types of TC genesis and trajectory. The steering winds at 500 hPa are more zonal in the straight-moving clusters, with larger meridional components in the recurving ones. Higher values of vertical wind shear in the midlatitudes also accompany the straight-moving tracks, compared to the recurving ones. The influence of ENSO on TC activity over the WNP is clearly discerned in specific clusters. Two of the seven clusters are typical of El Niño events; their genesis locations are shifted southeastward and they are more intense. The largest cluster is recurving, located northwestward, and occurs more often during La Niña events. Two types of recurving and one of straight-moving tracks occur preferentially when the Madden–Julian oscillation is active over the WNP region

Subseasonal-to-interdecadal variability of the Australian monsoon over North Queensland

Daily rainfall occurrence and amount at 11 stations over North Queensland are examined for summers 1958–1998, using a Hidden Markov Model (HMM). Daily rainfall variability is described in terms of the occurrence of five discrete ‘weather states’, identified by the HMM. Three states are characterized respectively by very wet, moderately wet, and dry conditions at most stations; two states have enhanced rainfall along the coast and dry conditions inland. Each HMM rainfall state is associated with a distinct atmospheric circulation regime. The two wet states are accompanied by monsoonal circulation patterns with large-scale ascent, low-level inflow from the north-west, and a phase reversal with height; the dry state is characterized by circulation anomalies of the opposite sense. Two of the states show significant associations with midlatitude synoptic waves. Variability of the monsoon on time-scales from subseasonal to interdecadal is interpreted in terms of changes in the frequency of occurrence of the five HMM rainfall states. Large subseasonal variability is identified in terms of active and break phases, and a highly variable monsoon onset date. The occurrence of the very wet and dry states is somewhat modulated by the Madden–Julian oscillation. On interannual time-scales, there are clear relationships with the El Niño–Southern Oscillation and Indian Ocean sea surface temperatures (SSTs). Interdecadal monsoonal variability is characterized by stronger monsoons during the 1970s, and weaker monsoons plus an increased prevalence of drier states in the later part of the record. Stochastic simulations of daily rainfall occurrence and amount at the 11 stations are generated by introducing predictors based on large-scale precipitation from (a) reanalysis data, (b) an atmospheric general circulation model (GCM) run with observed SST forcing and (c) antecedent June–August Pacific SST anomalies. The reanalysis large-scale precipitation yields relatively accurate station-level simulations of the interannual variability of daily rainfall amount and occurrence, with rainfall intensity less well simulated. At some stations, interannual variations in 10-day dry-spell frequency are also simulated reasonably well. The interannual quality of the simulations is markedly degraded when the GCM simulations are used as inputs, while antecedent Pacific SST inputs yield an anomaly correlation skill comparable to that of the GCM

Association between socioeconomic status, sex, and age at death from cystic fibrosis in England and Wales (1959 to 2008): cross sectional study

Objective To determine the trend in the association between socioeconomic status and sex and median age at death from cystic fibrosis in England and Wales, over the past 50 years

Subseasonal-to-interdecadal variability of the Australian monsoon over North Queensland

Daily rainfall occurrence and amount at 11 stations over North Queensland are examined for summers 1958–1998, using a Hidden Markov Model (HMM). Daily rainfall variability is described in terms of the occurrence of five discrete ‘weather states’, identified by the HMM. Three states are characterized respectively by very wet, moderately wet, and dry conditions at most stations; two states have enhanced rainfall along the coast and dry conditions inland. Each HMM rainfall state is associated with a distinct atmospheric circulation regime. The two wet states are accompanied by monsoonal circulation patterns with large-scale ascent, low-level inflow from the north-west, and a phase reversal with height; the dry state is characterized by circulation anomalies of the opposite sense. Two of the states show significant associations with midlatitude synoptic waves. Variability of the monsoon on time-scales from subseasonal to interdecadal is interpreted in terms of changes in the frequency of occurrence of the five HMM rainfall states. Large subseasonal variability is identified in terms of active and break phases, and a highly variable monsoon onset date. The occurrence of the very wet and dry states is somewhat modulated by the Madden–Julian oscillation. On interannual time-scales, there are clear relationships with the El Niño–Southern Oscillation and Indian Ocean sea surface temperatures (SSTs). Interdecadal monsoonal variability is characterized by stronger monsoons during the 1970s, and weaker monsoons plus an increased prevalence of drier states in the later part of the record. Stochastic simulations of daily rainfall occurrence and amount at the 11 stations are generated by introducing predictors based on large-scale precipitation from (a) reanalysis data, (b) an atmospheric general circulation model (GCM) run with observed SST forcing and (c) antecedent June–August Pacific SST anomalies. The reanalysis large-scale precipitation yields relatively accurate station-level simulations of the interannual variability of daily rainfall amount and occurrence, with rainfall intensity less well simulated. At some stations, interannual variations in 10-day dry-spell frequency are also simulated reasonably well. The interannual quality of the simulations is markedly degraded when the GCM simulations are used as inputs, while antecedent Pacific SST inputs yield an anomaly correlation skill comparable to that of the GCM

Feasibility and pilot study of the effects of microfinance on mortality and nutrition in children under five amongst the very poor in India: study protocol for a cluster randomized controlled trial

Background: The United Nations Millennium Development Goals include targets for the health of children under five years old. Poor health is linked to poverty and microfinance initiatives are economic interventions that may improve health by breaking the cycle of poverty. However, there is a lack of reliable evidence to support this. In addition, microfinance schemes may have adverse effects on health, for example due to increased indebtedness. Rojiroti UK and the Centre for Promoting Sustainable Livelihood run an innovative microfinance scheme that provides microcredit via women’s self-help groups (SHGs). This pilot study, conducted in rural Bihar (India), will establish whether it is feasible to collect anthropometric and mortality data on children under five years old and to conduct a limited cluster randomized trial of the Rojiroti intervention. Methods/Design: We have designed a cluster randomized trial in which participating tolas (small communities within villages) will be randomized to either receive early (SHGs and microfinance at baseline) or late intervention (SHGs and microfinance after 18 months). Using predesigned questionnaires, demographic, and mortality data for the last year and information about participating mothers and their children will be collected and the weight, height, and mid upper arm circumference (MUAC) of children will be measured at baseline and at 18 months. The late intervention group will establish SHGs and microfinance support at this point and data collection will be repeated at 36 months. The primary outcome measure will be the mean weight for height z-score of children under five years old in the early and late intervention tolas at 18 months. Secondary outcome measures will be the mortality rate, mean weight for age, height for age, prevalence of underweight, stunting, and wasting among children under five years of age. Discussion: Despite economic progress, marked inequalities in child health persist in India and Bihar is one of the worst affected states. There is a need to evaluate programs that may alleviate poverty and improve health. This study will help to inform the design of a definitive trial to determine if the Rojiroti scheme can improve the nutrition and survival of children under five years of age in deprived rural communities