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Immersed tunnels are positive buoyant structures during installation and negative buoyant after installation. A tunnel is composed of sequential immersed elements that are coupled to each other in joints. Tunnel elements consist of segments which are compressed to each other by longitudinal post-tensioning. After immersion the tunnel is supported by the seabed and the longitudinal post-tension is cut at the joints between segments. Therefore, the structure is a segmented lining which is sensitive for settlements due to non uniform circumstances over the length of the tunnel. An uneven response of the bedding underneath the tunnel introduce shear forces in joints of an immersed tunnel. Because immersed tunnels need to be buoyant during installation, they have limitations on weight and geometry, the size and therefore the capacity of these shear keys is limited because the height of the tunnel, as shear keys are applied in the walls of the tunnel. The foundation response is influenced by many factors related to subsoil but also to construction and dredging tolerances. The shear forces were derived as a function of different covariance lengths for subsoil stiffness and dredging tolerances for different tunnel layouts. In reliability analyses, using two different probabilistic methods, exceedance probabilities of maximum shear forces are derived for one lay out using Non Parametric Bayesian Networks and Vine Copulas. The analyses give more insight in to the magnitude of the shear forces in joints both in conditioned and unconditioned situations and this can be used for the design of immersed tunnels. • Incorporate subsoil uncertainty in structural behaviour of immersed tunnels. • Uncertainty of dredging tolerances described by Gaussian random fields. • Influence of bedding variation on shear key forces of immersed tunnels. • Probabilistic analysis based on non-parametric Bayesian networks and Vine Copulas. • Deriving exceedance probability of maximum shear key forces. [ABSTRACT FROM AUTHOR]

This study investigates the application of hazard maps in identifying critical locations associated with extreme vehicle load events. Examining extreme vehicle loads can offer valuable insights into the reliability of road infrastructure. Weigh-In-Motion (WIM) systems are commonly employed for this purpose; however, their limited availability often necessitates the use of less-sophisticated traffic counters (LSTCs). Unfortunately, LSTCs are unable to measure vehicular axle loads, posing a significant drawback. To overcome this limitation, we propose a methodology that utilizes data from LSTCs to estimate axle loads and map extreme gross vehicle weights. To demonstrate the feasibility of this approach, we present a case study concentrating on the major highway corridors in Mexico. While WIM stations are absent, data from a network of 1,777 counting stations and origin–destination surveys are available. By quantifying Gaussian copula-based Bayesian networks using the existing information, we generate synthetic site-specific axle loads. Subsequently, we calculate gross vehicle weights for selected return periods. These study findings facilitate the identification of hazardous locations. Additionally, we provide an interactive web map and a graphical user interface to generate synthetic axle loads. These tools, along with the proposed methodology, can serve as the foundation for maintenance strategies for existing roads and bridges. • Traffic counters data to pinpoint critical locations with extreme vehicle loads. • 1777 site-specific Bayesian Networks are used to compute synthetic axle loads. • 180 million trucks were simulated to assess extreme gross vehicle weights. • Spatial distribution of loads is revealed by mapping 50-year and 1000-year events. [ABSTRACT FROM AUTHOR]

The concept of intrinsic vulnerability of a traffic network is defined for the first time in this paper. Intrinsic vulnerability is the susceptibility to incidents characterised by a probability of occurrence in space and time of difficult estimation, which can result in considerable reduction or loss of the system functionality. Given the nature of this type of vulnerability, its assessment might arise as a major problem. Therefore, this paper investigates the assessment of the intrinsic vulnerability of a traffic network through a set of quantifiable indicators, i.e., accessibility and reliability. Moreover, it is of interest to determine whether the selected indicators are sufficient to assess the intrinsic vulnerability or if there is any significant missing aspect to be considered. A new methodology based on structured elicitation of multivariate uncertainty from experts is presented to address these issues, allowing the estimation of the intrinsic vulnerability and its probabilistic relationship with the indicators accessibility and reliability. Although applied to the case of the metric intrinsic vulnerability, the proposed methodology emerges as an effective tool to understand other traffic descriptors of difficult evaluation such as resilience. [ABSTRACT FROM AUTHOR]

Governmental climate change mitigation targets are typically developed with the aid of forecasts of greenhouse-gas (GHG) emissions. The robustness and credibility of such forecasts depends, among other issues, on the extent to which forecasting approaches can reflect prevailing uncertainties. We apply a transparent and replicable method to quantify the uncertainty associated with projections of gross domestic product growth rates for Mexico, a key driver of GHG emissions in the country. We use those projections to produce probabilistic forecasts of GHG emissions for Mexico. We contrast our probabilistic forecasts with Mexico’s governmental deterministic forecasts. We show that, because they fail to reflect such key uncertainty, deterministic forecasts are ill-suited for use in target-setting processes. We argue that (i) guidelines should be agreed upon, to ensure that governmental forecasts meet certain minimum transparency and quality standards, and (ii) governments should be held accountable for the appropriateness of the forecasting approach applied to prepare governmental forecasts, especially when those forecasts are used to derive climate change mitigation targets. POLICY INSIGHTS No minimum transparency and quality standards exist to guide the development of GHG emission scenario forecasts, not even when these forecasts are used to set national climate change mitigation targets. No accountability mechanisms appear to be in place at the national level to ensure that national governments rely on scientifically sound processes to develop GHG emission scenarios. Using probabilistic forecasts to underpin emission reduction targets represents a scientifically sound option for reflecting in the target the uncertainty to which those forecasts are subject, thus increasing the validity of the target. Setting up minimum transparency and quality standards, and holding governments accountable for their choice of forecasting methods could lead to more robust emission reduction targets nationally and, by extension, internationally. [ABSTRACT FROM AUTHOR]

The influence of social and economic change on the consequences of natural hazards has been a matter of much interest recently. However, there is a lack of comprehensive, high-resolution data on historical changes in land use, population or Historical Analysis of Natural Hazards in Europe, which contains two parts: (1) HANZE-Exposure with maps for 37 countries and territories from 1870 to 2020 in 100 m resolution and (2) HANZE-Events, a compilation of past disasters with information on dates, locations and losses, currently limited to floods only. The database was constructed using high-resolution maps of present land use and population, a large compilation of historical statistics, and relatively disaggregation techniques and rule-based land-use reallocation schemes. Data encompassed in HANZE allow to normalize information on losses due to natural hazards by taking into account inflation as well as changes in population, production and wealth. Database of past events currently contains 1564 records (1870-2016) of flash, river, coastal and compound floods. HANZE database is freely available at https://doi.org/10.4121/collection:HANZE. [ABSTRACT FROM AUTHOR]

There are numerous uncertainties that impact offshore operations. However, environmental uncertainties concerning variables such as wave height and wind speed are crucial because these may affect installation and maintenance operations with potential delays and financial consequences. In order to include these uncertainties into the duration estimation, adequate tools should be developed to simulate an installation scenario for a large number of historical environmental data. Data regarding environmental time series are usually scarce and limited, therefore they should be modelled. Since the environmental variables are in reality dependent, we propose a probabilistic method for their construction using copulas. To demonstrate the effectiveness of this method compared to the cases where observed or independently constructed environmental time series are used, a realistic cable installation scenario for an offshore wind farm was simulated. It was found that the proposed method should be followed to acquire more reliable and accurate estimation of the installation's duration. [ABSTRACT FROM AUTHOR]

Abstract: Weigh-in-Motion (WIM) systems are used, among other applications, in pavement and bridge reliability. The system measures quantities such as individual axle load, vehicular loads, vehicle speed, vehicle length and number of axles. Because of the nature of traffic configuration, the quantities measured are evidently regarded as random variables. The dependence structure of the data of such complex systems as the traffic systems is also very complex. It is desirable to be able to represent the complex multidimensional-distribution with models where the dependence may be explained in a clear way and different locations where the system operates may be treated simultaneously. Bayesian Networks (BNs) are models that comply with the characteristics listed above. In this paper we discuss BN models and results concerning their ability to adequately represent the data. The paper places attention on the construction and use of the models. We discuss applications of the proposed BNs in reliability analysis. In particular we show how the proposed BNs may be used for computing design values for individual axles, vehicle weight and maximum bending moments of bridges in certain time intervals. These estimates have been used to advise authorities with respect to bridge reliability. Directions as to how the model may be extended to include locations where the WIM system does not operate are given whenever possible. These ideas benefit from structured expert judgment techniques previously used to quantify Hybrid Bayesian Networks (HBNs) with success. [Copyright &y& Elsevier]

Dams' safety is highly important for authorities around the world. The impacts of a dam failure can be enormous. Models for investigating dam safety are required for helping decision-makers to mitigate the possible adverse consequences of flooding. A model for earth dam safety must specify clearly possible contributing factors, failure modes and potential consequences of dam failure. Probabilistic relations between variables should also be specified. Bayesian networks (BNs) have been identified as tools that would assist dam engineers on assessing risks. BNs are graphical models that facilitate the construction of a joint probability distribution. Most of the time, the variables included in a model for earth dam risk assessment involve continuous quantities. The presence of continuous random variables makes the implementation of discrete BNs difficult. An alternative to discrete BNs is the use of non-parametric continuous BNs, which will be briefly described in this article. As an example, a model for earth dams' safety in the State of Mexico will be discussed. Results regarding the quantification of conditional rank correlations through ratios of unconditional rank correlations have not been presented before and are introduced herein. While the complete application of the model for the State of Mexico is presented in an accompanying paper, here some results regarding model use are shown for demonstration purposes. The methods presented in this article can be applied for investigating risks of failure of civil infrastructures other than earth dams. [ABSTRACT FROM AUTHOR]

Dams are civil engineering structures to hinder water flows. Criteria such as purpose, size and construction material are useful to categorise them. The latter is used to classify ‘earth dams’, which tend to have higher risk levels than other types. The failure of a dam leads to significant economic loss and usually to catastrophic impacts. In an effort to comprehensively examine the variables that influence earth dam breaks and describe their interactions, a model has been developed in such a way that it allows to assess risks and to prioritise the allocation of resources for maintenance activities. The research was carried out by systematically reviewing the literature, which led to the choice of Bayesian Networks (BNs) as a tool for assessing risks. Using data from seven case studies in Mexico, a model was built, which helped to rank the dams under study, leading to results comparable with those reported in the literature. While the particular type of BN used and its quantification is presented more extensively in an accompanying paper, the model may be of interest for dam owners, managers, practitioners and academics on their efforts to manage earth dams’ risks. [ABSTRACT FROM AUTHOR]

Constructing Bayesian networks (BN) for practical applications presents significant challenges, especially in domains with limited empirical data available. In such situations, field experts are often consulted to estimate the model's parameters, for instance, rank correlations in Gaussian copula-based Bayesian networks (GCBN). Because there is no consensus on a 'best' approach for eliciting these correlations, this paper proposes a framework that uses probabilities of concordance for assessing dependence, and the dependence calibration score to aggregate experts' judgments. To demonstrate the relevance of our approach, the latter is implemented to populate a GCBN intended to estimate the condition of air handling units' components—a key challenge in building asset management. While the elicitation of concordance probabilities was well received by the questionnaire respondents, the analysis of the results reveals notable disparities in the experts' ability to quantify uncertainty. Moreover, the application of the dependence calibration aggregation method was hindered by the absence of relevant seed variables, thus failing to evaluate the participants' field expertise. All in all, while the authors do not recommend to use the current model in practice, this study suggests that concordance probabilities should be further explored as an alternative approach for the elicitation of dependence. [ABSTRACT FROM AUTHOR]

• Reliability analysis on flood defences designed without probabilistic criteria. • Characterizing precipitation and evaporation time series • Generation of water level data using copulas, Markov chains, and hydrological data. • The resulting probability of failure is consistent with historical sources. In some cases, hydraulic infrastructures of different sizes and importance are built without formal design criteria and it may be desired to infer the reliability level for which it was originally designed. In this paper, we present a method to perform the reliability analysis of a dike that was, in all likelihood, designed without probabilistic criteria. This is the Nezahualcoyotl dike, an ancient structure built by the Aztec empire around 1445 in modern-day Mexico City. The method consists of 1. Characterizing time-series of relevant environmental variables. We use precipitation and evaporation by combining a discrete time-state Markov chain with a copula-based stochastic process. 2. Simulating from the time series model a large number of observations to "load" the structure of interest and finally, 3. Compute the probability of failure of the structure with respect to a failure mechanism. In this case, we focus on overflow. The proposed model is able to reproduce to a good extent the hydrology of the system. The return period for overflow obtained in our research is consistent with historical accounts. This work can be used as a reference to assess the reliability of other (ancient or present-day) structures whose design is based on informal criteria. [ABSTRACT FROM AUTHOR]

Offshore asset construction is a complex and costly process that is subject to various uncertainties within the entire supply chain. Hence, both the construction management optimization and the reduction of deployment expenditures should be supported by automated decision support models which include proper representations of predominant uncertainties. One of these is the supply disruption risk that is often ignored in existing models. Therefore, this article proposes a methodology to properly take this construction risk into account. An algorithm to model this risk was developed and a study was conducted to obtain the required probability distributions of disruption delays using real data and expert judgments for an offshore wind farm construction application. The simulation of a realistic test case with an appropriately modified stochastic simulation tool showed that it is important to consider this risk in order to make optimal decisions for different offshore wind farm construction strategies. • A methodology to support optimal construction management of offshore assets • An algorithm to model supply disruptions risk • Real data and expert judgments to quantify supply disruptions risk • Evidence that disregarding supply disruptions may cause significant cost overruns • Obtained distributions can support future offshore wind farms construction [ABSTRACT FROM AUTHOR]

Rising sea levels caused by climate change are increasing the risk of overtopping on coastal structures. Moreover, there is a growing societal concern about the visual impact of these structures, which leads to the lowering of their crest freeboards. In previous studies, safety during overtopping events was assessed considering the overtopping layer thickness (h c), the overtopping flow velocity (u c) and the individual wave overtopping volume (V). Existing models in the literature to estimate h c , u c and V on mound breakwater crests are mainly deterministic, involve a chain of successive estimations leading to accumulated errors and/or do not account for the dependencies between h c , u c and V. This study proposes a model to describe the joint probability distribution of h c , u c and V based on bivariate copulas. Experimental data from small-scale 2D physical tests conducted on mound breakwaters with three armor layers (single-layer Cubipod®, and double-layer cubes and rocks) in depth-limited breaking wave conditions on two mild bottom slopes and dimensionless crest freeboards between 0.33 and 3.20 is used. Lognormal distribution functions are proposed for each variable and a multivariate dependence model is developed through a one-tree vine-copula. The parameters of this model are quantified directly using wave characteristics and the structure geometry minimizing the accumulated errors in the final predictions. The application of the model is illustrated by computing the probability of not fulfilling at least a tolerability limit for one of the studied variables (OR probability). The OR probability is computed both considering the dependence and assuming independence between the variables and a significant difference is obtained. It is concluded that by accounting for the multivariate dependence between the variables, it is possible to reduce the crest freeboard and, thus, achieve a more economic design within the required safety level. • Distribution functions are proposed to describe each overtopping variable. • A one-tree vine-copula is proposed to model the dependence of overtopping variables. • The model allows to compute the probability of not fulfilling at least one limit. • The model is quantified based on wave characteristics and structure geometry. [ABSTRACT FROM AUTHOR]

Around the world, an increasing amount of bridge infrastructure is ageing. The resources involved in the reassessment of existing assets often exceed available resources and many bridges lack a minimum structural assessment. Therefore, there is a need for comprehensive and quantitative approaches to assess all the assets in the bridge network to reduce the risk of collapsing, damage to infrastructure, and economic losses. This paper proposes a methodology to quantify the structural criticality of bridges at a network level. To accomplish this, long-run site-specific simulations are conducted using Bayesian Networks and bivariate copulas, utilizing recorded traffic data obtained from permanent counting stations. To enhance the dataset, information from Weigh-in-Motion systems from different regions was integrated through a matching process. Subsequently, the structural response resulting from the simulated traffic is assessed, and the extreme values of the traffic load effects are obtained for selected return periods. Site-specific bridge criticality as a performance indicator for traffic load effects is derived by comparing the extreme load effects with the design load effects. The outcomes are mapped to facilitate visualization employing an open-source geographic information system application. To illustrate the application of the methodology, a total of 576 bridges within a national highway network are investigated, and a comparison with a popular simplified method is shown. The methodology herein presented can be used to assist in assessing the condition of a bridge network and prioritizing maintenance and repair activities by identifying potential bridges subjected to major load stress. • Methodology for mapping extreme load effects due to heavy vehicles in bridge networks • Evaluation of bridge criticality as a performance indicator for traffic load effects • Simple yet robust method using Bayesian Networks and copulas for traffic simulation • Application of the methodology to Mexico's national bridge network as case study [ABSTRACT FROM AUTHOR]

Highlights • This research deals with reinforced concrete columns behavior and under live loads and seismic event. • The use of NPBN and MCS could lead to the development of a management decision tool. • The results may be used for ranking investments in maintenance actions. • This model is in agreement with the estimates given in reliability literature. Abstract In the bridge industry, current traffic trends have increased the likelihood of having the simultaneous presence of both extreme live loads and earthquake events. To date, their concurrent interaction has scarcely been systematically studied. Prevailing studies have investigated the isolated existence of either live loads or seismic actions. In an effort to fill this gap in the literature, a non-parametric Bayesian Network (BN) has been proposed. It is aimed at evaluating the conditional probability of failure for a reinforced concrete bridge column, subject simultaneously to the actions mentioned above. Based on actual data from a structure located in the State of Mexico, a Monte Carlo Simulation model was developed. This led to the construction of a BN with 17 variables. The set of variables included in the model can be categorized into three groups: acting loads, materials resistances and structure force-displacement behavior. Practitioners are then provided with a tool for unspecialized labor force to gather information in situ (e.g. Weight-In-Motion data and Schmidt hammer measurements), which can be included in the network, leading to an updated probability of failure. Moreover, this framework also serves as a quantitative tool for bridge column reliability assessments. Results from the theoretical model confirmed that the bridge column probability of failure was within the expected range reported in the literature. This reflects not only the appropriateness of its design but also the suitability of the proposed BN for reliability analysis. [ABSTRACT FROM AUTHOR]

Modeling the stochastic evolution of a large-scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke's method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information. [ABSTRACT FROM AUTHOR]

A probabilistic model is developed to investigate the crack growth development in welded details of orthotropic bridge decks. Bridge decks may contain many of these vulnerable details and bridge reliability cannot always be guaranteed upon the attainment of a critical crack. Therefore, insight into the crack growth development is crucial in guaranteeing bridge reliability and scheduling efficient maintenance schemes. The probabilistic nature of the crack growth development model and the dependence of this model on many interdependent random variables result in significant uncertainties regarding model outcome. To reduce some of these uncertainties, the probabilistic model is combined with a monitoring system installed on a part of the bridge. In addition, a Bayesian network is used to determine the dependence structure between the different details (monitored and non-monitored) of the bridge. This dependence structure enables us to make more accurate crack growth predictions for all details of the bridge while monitoring only a limited number of those details and updating the remaining uncertainties. [ABSTRACT FROM PUBLISHER]

Commercial assets comprise buildings, machinery and equipment, which are susceptible to floods. Existing damage models and exposure estimation methods for this sector have limited transferability between flood events and therefore limited potential for pan-European applications. In this study we introduce two methodologies aiming at improving commercial flood damage modelling: (1) disaggregation of economic statistics to obtain detailed building-level estimates of replacement costs of commercial assets; (2) a Bayesian Network (BN) damage model based primarily on post-disaster company surveys carried out in Germany. The BN model is probabilistic and provides probability distributions of estimated losses, and as such quantitative uncertainty information. The BN shows good accuracy of predictions of building losses, though overestimates machinery/equipment loss. To test its suitability for pan-European flood modelling, the BN was applied to three case studies, comprising a coastal flood in France (2010) and fluvial floods in Saxony (2013) and Italy (2014). Overall difference between modelled and reported average loss per company was only 2–19% depending on the case study. Additionally, the BN model achieved better results than six alternative damage models in those case studies (except for one model in the Italian case study). Further, our exposure estimates mostly resulted in better predictions of the damage models compared to previously published pan-European exposure data, which tend to overestimate exposure. All in all, the methods allow easy modelling of commercial flood losses in the whole of Europe, since they are applicable even if only publicly-available datasets are obtainable. The methods achieve a higher accuracy than alternative approaches, and inherently provide confidence intervals, which is particularly valuable for decision making under high uncertainty. Unlabelled Image • A commercial flood damage model based on Bayesian Networks is introduced. • This micro-scale model inherently provides uncertainty information. • An associated commercial asset exposure estimation method is based on open data. • The open-data concept makes the model easily applicable to European countries. • Loss predictions in three test cases are more accurate than those from other models. [ABSTRACT FROM AUTHOR]

Cliff coasts are dynamic environments that can retreat very quickly. However, the short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion. [ABSTRACT FROM AUTHOR]