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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]

• 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]