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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]

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]

• 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]

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]

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]