Performance indicator of a bridge expansion joint

In general the condition of a structure can be assessed in terms of a performance indicator. For example, this can be the strength of a structure. An asset manager is concerned with ensuring that the performance of a structure does not fall below a given minimum level. This can be achieved by inspecting or monitoring the structure. As the performance indicator decreases with time, the asset manager can decide to take pre-emptive measures to restore the condition to its initial level, thus avoiding getting too close to the minimum required level. In order to work this way, it is important to define a reliable performance indicator. Following an inventory of structures which are prone to some form of degradation over time, a modular bridge expansion joint was selected as a case to be considered in this investigation. In order to determine the observability of known failure mechanisms in terms of modal and spectral data an experiment is set up which can simulate the construction under these circumstances. Further, a Finite Element Model of the construction is made, which is tuned to the experimental setup. This model is used to validate the applied inverse modeling technique to identify the failure parameters. The inverse modeling is performed using a genetic algorithm. Through inverse modeling of the monitor data, changes over time of the identified failure parameters are obtained. In order to predict the development of the observed failure parameters in the future, these changes over time are incorporated in a prediction model. Taking uncertainties into account, stochastic processes are used to describe the degradation process. Thus, different types of processes can be used, e.g. Markov chains for discrete state changes in time or Gamma processes for continuous quantities in time. By continuously updating the prediction model with the monitor data, a risk based maintenance management tool is obtained by which pro-active and well-planned maintenance actions can be decided on. The developed methodology is applied to a full scale monitoring system of a real bridge in the Netherlands

Data for: Update (1.1) to ANDURIL — A MATLAB toolbox for ANalysis and Decisions with UnceRtaInty: Learning from expert judgments

Expert Judgment data which are used in several publications and on which the software validate

Data for: Update (1.1) to ANDURIL — A MATLAB toolbox for ANalysis and Decisions with UnceRtaInty: Learning from expert judgments

Expert Judgment data which are used in several publications and on which the software validatedTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

ANDURIL - A MATLAB toolbox for ANalysis and Decisions with UnceRtaInty: Learning from expert judgments

The Classical model (or Cooke’s model) for elicitation and combination of expert judgments has been used in science and engineering since at least the early 1990’s. The most widely used program for applications of this model is EXCALIBUR. However, its code is not available for practitioners, which limits the accessibility and potential of the method. In this paper, we discuss a MATLAB toolbox (ANDURIL) intended to fill in this gap. The software has been tested in a recent real-life application reproducing the results of EXCALIBUR. We discuss different advantages for the users from having the developed source code available for practice.Integral Design and ManagementHydraulic Structures and Flood Ris

Trends in flood exposure and vulnerability: Europe 1870–2016

ObjectivesSince the beginning of the second industrial revolution in the second half of the 19th century, Europe’s society and economy has been profoundly transformed. The population doubled in the last 150 years, together with more than fourfold increase in number of dwellings and 30-fold increase in production value in real terms. At the same time, rural population dropped, and share of agriculture in production declined from 30% to a mere 2%. Cities that once have been small and very densely populated evolved into less cramped, but quickly sprawling metropolitan areas. All those trends were not without effect on flood exposure and vulnerability, two crucial components of flood risk. The study aims to reevalute reported flood losses (population killed or affected, monetary value of losses, inundated area) so that for each flood event that occurred since 1870, flood losses relative to potential damage given the size of the flood event could be calculated.MethodsIn order to be able to calculate potential losses during any flood event within the study’s timeframe, a set of high-resolution maps of land use, population, production and assets distribution is needed. Firstly, such detailed maps of population and land use at 100 m resolution was compiled for year 2011/2012. From this ‘baseline’ other maps for other time points (decenially 1870–1970 and five-yearly 1975–2020) could be calculated. However, for those other time points we only know the total population and land use at regional level. Hence, for each time step, the population and the different land use classes had to be redistributed inside each region in order to match the regional totals. Several methodologies were used in order to provide the best approximation for each land use class and population. Most effort was put to estimate past and future residential urban areas (where most population lives) and lands used by agriculture and infrastructure. A database of population, land use and economy at NUTS 3 regions was compiled for this study. Estimates of production and assets were disaggregated from regional or national level to a 100 m grid based on population and land use. Information on flood events, each with a flood extent defined using NUTS 3 regions, was also collected. Finally, the exposure maps were intersected with flood zones taken from pan-European flood hazard models.Result

Applying a Bayesian network based on Gaussian copulas to model the hydraulic boundary conditions for hurricane flood risk analysis in a coastal watershed

In recent years significant emphasis has been placed on quantifying coastal flood hazards in the U.S. using high resolution 2-D hydrodynamic and nearshore wave models. However, these studies are computationally expensive and often neglect to consider the flooding that arises from the combined hazards of precipitation and storm surge in coastal watersheds. This paper describes a method to stochastically simulate a large number of combinations of peak storm surge and cumulative precipitation to determine the hydraulic boundary conditions for a low-lying coastal watershed draining into a semi-enclosed tidal bay. The method is computationally efficient and takes into consideration five tropical cyclone characteristics at landfall: windspeed, angle of approach, landfall location, radius of maximum winds, and forward speed. A precipitation gage network and tidal gage data were used, along with observations from over 300 tropical cyclones in the Gulf of Mexico. A Non-parametric Bayesian Network was built to generate 100,000 synthetic storm events and used as input to an empirical wind set-up model to simulate storm surge within a tidal bay and at the downstream boundary of the watershed. Based on the results, probable combinations of cumulative precipitation and peak storm surge for the watershed during hurricane conditions are determined. These boundary conditions can be easily incorporated into a coastal riverine model to determine flood risk in the watershed.<br/

A Vine-Copula Model for Time Series of Significant Wave Heights and Mean Zero-Crossing Periods in the North Sea

Stochastic descriptions and simulations of oceanographic variables are essential for coastal and marine engineering applications. In the past decade, copula-based approaches have become increasingly popular for estimating the multivariate distribution of some variables at the peak of a storm along with its duration. The modeling of the storm shape, which contributes to its impact, is often simplified. This article proposes a vine-copula approach to characterize hourly significant wave heights and corresponding mean zero-crossing periods as a random process in time. The model is applied to a data set in the North Sea, and time series with the duration of an oceanographic winter are simulated. The synthetic wave scenarios emulate storms as well as daily conditions. The results are useful, for example, as input for coastal risk analyses and for planning offshore operations. Nonetheless, selecting a vine structure, finding appropriate copula families, and estimating parameters is not straightforward. The validity of the model, as well as the conclusions that can be drawn from it, are sensitive to these choices. A valuable byproduct of the proposed vine-copula approach is the bivariate distribution of significant wave heights and corresponding mean zero-crossing periods at the given location. Its dependence structure is approximated by the flexible skew-t copula family and preserves the limiting wave steepness condition.Hydraulic Structures and Flood RiskCoastal Engineerin

Estimating extreme river discharges in Europe through a Bayesian network

Large-scale hydrological modelling of flood hazards requires adequate extreme discharge data. In practise, models based on physics are applied alongside those utilizing only statistical analysis. The former require enormous computational power, while the latter are mostly limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian networks (BNs), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables representing the geographical characteristics of their catchments. Annual maxima of daily discharges from more than 1800 river gauges (stations with catchment areas ranging from 1.4 to 807 000 km2) were collected, together with information on terrain, land use and local climate. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe and better than a comparable global statistical model. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and remains the same in a split-sample validation. Though discharge prediction under climate change is not the main scope of this paper, the BN was applied to a large domain covering all sizes of rivers in the continent both for present and future climate, as an example. Results show substantial variation in the influence of climate change on river discharges. The model can be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.Hydraulic Structures and Flood Ris

Vehicular loads hazard mapping through a Bayesian Network in the State of Mexico

Traffic counts collect information that is valuable, for example, in bridge and road design or maintenance processes. The average daily traffic volume is often the most collected measure of vehicular traffic, which is used in the design or assessment of major highways. Permanent control stations, situated in key locations of the highway network, gather data the entire year. However, one of the disadvantages of traffic count data is that most counters used, do not measure total vehicle weight and axle load data. Traffic counts display only the classification of vehicles, traffic volume, average daily traffic, and annual average daily traffic. Axle loads on the other hand are required, for example, as input in the design of pavement and new bridges, and the reliability assessment of existing ones. Weigh-in-motion (WIM) systems are usually used to collect vehicle load data. The State of Mexico (in central Mexico) has 115 permanent vehicle counting stations with 745 traffic counting points in its federally administered road network. However, due to the lack of WIM stations, it is not possible to obtain axle load data. In this paper, a Bayesian Network (BN) quantified with data from WIM stations in the Netherlands is used to describe the weight and length distribution of heavy vehicles registered in the permanent vehicle counting stations of the State of Mexico federal highways. The Dutch and Mexican vehicle types are matched according to similar characteristics. Later, synthetic WIM observations from the BN model are analysed through extreme value theory and vehicle loads with selected return periods are computed for all study counting points. The outcome is a mapping methodology with a linked database. The traffic volumes and extreme loads can then be easily found and compared with other highways in the network. This work shows that hazard maps can be implemented to provide importantly and summarized information to understand the risks of extreme traffic loads and to help in the reliability assessment and maintenance strategies of pavements and bridges.Accepted Author ManuscriptHydraulic Structures and Flood Ris