Virtual structural health monitoring and remaining life prediction of steel bridges

In this study a Structural Health Monitoring (SHM) system is combined with Bridge Weigh-in-Motion (B-WIM) measurements of the actual traffic loading on a bridge to carry out a fatigue damage calculation. The SHM system uses the 'Virtual Monitoring' concept, where all parts of the bridge that are not monitored directly using sensors, are 'virtually' monitored using the load information and a calibrated Finite Element (FE) model of the bridge. Besides providing the actual traffic loading on the bridge, the measurements are used to calibrate the SHM system and to update the FE model of the bridge. The newly developed Virtual Monitoring concept then uses the calibrated FE model of the bridge to calculate stress ranges and hence to monitor fatigue at locations on the bridge not directly monitored. The combination of a validated numerical model of the bridge with the actual site-specific traffic loading allows a more accurate prediction of the cumulative fatigue damage at the time of measurement and facilitates studies on the implications of traffic growth. In order to test the accuracy of the Virtual Monitoring system, a steel bridge with a cable-stayed span in the Netherlands was used for testing

Nonlinear Analysis of Isotropic Slab Bridges under Extreme Traffic Loading

Probabilistic analysis of traffic loading on a bridge traditionally involves an extrapolation from measured or simulated load effects to a characteristic maximum value. In recent years, Long Run Simulation, whereby thousands of years of traffic are simulated, has allowed researchers to gain new insights into the nature of the traffic scenarios that govern at the limit state. For example, mobile cranes and low-loaders, sometimes accompanied by a common articulated truck, have been shown to govern in most cases. In this paper, the extreme loading scenarios identified in the Long Run Simulation are applied to a non-linear, two-dimensional (2D) plate finite element model. For the first time, the loading scenarios that govern in 2D nonlinear analyses are found and compared to those that govern for 2D linear and 1D linear/nonlinear analyses. Results show that, for an isotropic slab, the governing loading scenarios are similar to those that govern in simple one-dimensional (beam) models. Furthermore, there are only slight differences in the critical positions of the vehicles. It is also evident that the load effects causing failure in the 2D linear elastic plate models are significantly lower, i.e. 2D linear elastic analysis is more conservative than both 2D nonlinear and 1D linear/nonlinear

A Review of Probabilistic Methods of Assessment of Load Effects in Bridges

This paper reviews a range of statistical approaches to illustrate the influence of data quality and quantity on the probabilistic modelling of traffic load effects. It also aims to demonstrate the importance of long-run simulations in calculating characteristic traffic load effects. The popular methods of Peaks Over Threshold and Generalized Extreme Value are considered but also other methods including the Box-Cox approach, fitting to a Normal distribution and the Rice formula. For these five methods, curves are fitted to the tails of the daily maximum data. Bayesian Updating and Predictive Likelihood are also assessed, which require the entire data for fittings. The accuracy of each method in calculating 75-year characteristic values and probability of failure, using different quantities of data, is assessed. The nature of the problem is first introduced by a simple numerical example with a known theoretical answer. It is then extended to more realistic problems, where long-run simulations are used to provide benchmark results, against which each method is compared. Increasing the number of data in the sample results in higher accuracy of approximations but it is not able to completely eliminate the uncertainty associated with the extrapolation. Results also show that the accuracy of estimations of characteristic value and probabilities of failure are more a function of data quality than extrapolation technique. This highlights the importance of long-run simulations as a means of reducing the errors associated with the extrapolation process

Activators of SIRT1 in the kidney and protective effects of SIRT1 during acute kidney injury (AKI) (effect of SIRT1 activators on acute kidney injury)

Acute kidney injury (AKI) is a complex disorder and a clinical condition characterized by acute reduction in renal function. If AKI is not treated, it can lead to chronic kidney disease, which is associated with a high risk of death. SIRT1 (silent information regulator 1) is an NAD-dependent deacetylase. This enzyme is responsible for the processes of DNA repair or recombination, chromosomal stability, and gene transcription. This enzyme also plays a protective role in many diseases, including AKI. In this study, we review the mechanisms that mediate the protective effects of SIRT1 on AKI, including SIRT1 activators. © 2021, Japanese Society of Nephrology

Nonlinear Analysis of Isotropic Slab Bridges under Extreme Traffic Loading

Probabilistic analysis of traffic loading on a bridge traditionally involves an extrapolation from measured or simulated load effects to a characteristic maximum value. In recent years, Long Run Simulation, whereby thousands of years of traffic are simulated, has allowed researchers to gain new insights into the nature of the traffic scenarios that govern at the limit state. For example, mobile cranes and low-loaders, sometimes accompanied by a common articulated truck, have been shown to govern in most cases. In this paper, the extreme loading scenarios identified in the Long Run Simulation are applied to a non-linear, two-dimensional (2D) plate finite element model. For the first time, the loading scenarios that govern in 2D nonlinear analyses are found and compared to those that govern for 2D linear and 1D linear/nonlinear analyses. Results show that, for an isotropic slab, the governing loading scenarios are similar to those that govern in simple one-dimensional (beam) models. Furthermore, there are only slight differences in the critical positions of the vehicles. It is also evident that the load effects causing failure in the 2D linear elastic plate models are significantly lower, i.e. 2D linear elastic analysis is more conservative than both 2D nonlinear and 1D linear/nonlinear

A review of probabilistic methods of assessment of load effects in bridges

This paper reviews a range of statistical approaches to illustrate the influence of data quality and quantity on the probabilistic modelling of traffic load effects. It also aims to demonstrate the importance of long-run simulations in calculating characteristic traffic load effects. The popular methods of Peaks Over Threshold and Generalised Extreme Value are considered but also other methods including the Box-Cox approach, fitting to a Normal distribution and the Rice formula. For these five methods, curves are fitted to the tails of the daily maximum data. Bayesian Updating and Predictive Likelihood are also assessed, which require the entire data for fittings. The accuracy of each method in calculating 75-year characteristic values and probability of failure, using different quantities of data, is assessed. The nature of the problem is first introduced by a simple numerical example with a known theoretical answer. It is then extended to more realistic problems, where long-run simulations are used to provide benchmark results, against which each method is compared. Increasing the number of data in the sample results in higher accuracy of approximations but it is not able to completely eliminate the uncertainty associated with the extrapolation. Results also show that the accuracy of estimations of characteristic value and probabilities of failure are more a function of data quality than extrapolation technique. This highlights the importance of long-run simulations as a means of reducing the errors associated with the extrapolation process. © 2015 Elsevier Ltd

A Review of Probabilistic Methods of Assessment of Load Effects in Bridges

This paper reviews a range of statistical approaches to illustrate the influence of data quality and quantity on the probabilistic modelling of traffic load effects. It also aims to demonstrate the importance of long-run simulations in calculating characteristic traffic load effects. The popular methods of Peaks Over Threshold and Generalized Extreme Value are considered but also other methods including the Box-Cox approach, fitting to a Normal distribution and the Rice formula. For these five methods, curves are fitted to the tails of the daily maximum data. Bayesian Updating and Predictive Likelihood are also assessed, which require the entire data for fittings. The accuracy of each method in calculating 75-year characteristic values and probability of failure, using different quantities of data, is assessed. The nature of the problem is first introduced by a simple numerical example with a known theoretical answer. It is then extended to more realistic problems, where long-run simulations are used to provide benchmark results, against which each method is compared. Increasing the number of data in the sample results in higher accuracy of approximations but it is not able to completely eliminate the uncertainty associated with the extrapolation. Results also show that the accuracy of estimations of characteristic value and probabilities of failure are more a function of data quality than extrapolation technique. This highlights the importance of long-run simulations as a means of reducing the errors associated with the extrapolation process