This work explores probabilistic deep learning models as offshore farm-wide virtual load sensors, including Bayesian neural networks, Monte Carlo dropout, and deep neural network ensembles. The aim is to develop models offering uncertainty-aware predictions of damage equivalent loads using SCADA and accelerometer data. This study uses the data from a Belgian offshore wind farm’s five fleet-leader turbines. After training the neural networks with one year of collected data, these models are deployed to another year, facing out-of-distribution data due to changes in operational conditions. The analysis assesses generalization and uncertainty quantification abilities, providing insights into their strengths and weaknesses. Ultimately, our work supports the industrial adoption of probabilistic deep learning virtual monitoring models, enabling informed asset management decisions with predictions and uncertainty measures. ... Read more

Building energy prediction models expedite performance assessment and assist in decision making, from early-stage design to retrofit planning at single- or multi-building scales. However, the number of parameters involved in the energy performance evaluation often impede the prediction process requiring the assimilation of high-dimensional, uncertain input. This is compounded further at multi-building scale e.g. urban energy modelling, due to the increased complexity of evaluating diverse building geometries. While single-building sensitivity and uncertainty analysis is well-established for identifying the most influential input parameters and evaluate the uncertainty effects on energy demand, these are hard to generalize at multi-building scale which remains relatively unexplored. The present study advances existing research by applying a variance-based sensitivity analysis to assess the impact of varying (i) building façade layout, (ii) envelope thermal properties, (iii) envelope air tightness and (iv) building occupancy. The analysis is conducted for multiple buildings under two future climate variations, while also considering the degradation of material thermal properties. The latter is derived from known deterioration models for single-building uncertainty propagation, relying on experimental and simulated data. The approach is applied to a temperate oceanic climate with particular focus on the Dutch building stock, including a sample of buildings with diverse geometric characteristics in Rotterdam. First-order Sobol indices are computed to evaluate the impact with respect to the heating, cooling and total energy demand. Our findings indicate that infiltration is the most influential factor for heating energy demand, whereas cooling is mostly affected by the envelope thermal properties and, particularly, window solar heat gain coefficient. Common patterns regarding the impact of insulation across different envelope components can be identified among buildings with similar orientation and compactness ratio indicating the importance of considering these geometric properties in retrofit decision-making workflows. ... Read more

This study investigates the behavior of interacting surface cracks at the circumferential weld toe of monopile-supported offshore wind turbines. Relying on a numerical model that explicitly considers weld profiles, we explore the impact of crack interaction and loading scenarios on crack propagation. Our findings reveal that, initially, surface cracks grow independently, resembling single crack behavior. However, a pronounced interaction effect accelerates their growth as cracks propagate further, potentially leading to crack coalescence, high stress intensity factors, and reduced fatigue life. Consequently, this work highlights the need for integrating specific weld geometry representation in numerical models, as neglecting this can lead to significantly inaccurate fatigue life estimates in typical practical applications. Moreover, this study points out the challenge in accessing representative crack growth material parameters, vital for accurately evaluating the fatigue life of structural connections in offshore wind turbines. ... Read more

Aging wind energy assets demand the development of methods able to effectively support informed decision-making. These needs have inspired the use of data-driven methodologies, which offer valuable insights to wind turbine owners and/or operators. Many approaches can be found in the literature for extrapolating fatigue damage measurements to estimate the lifetime of wind turbines. In some cases, resampling approaches are proposed to compute the confidence levels associated with the generated projections, yet a standardized framework has not been adopted. Most reported studies identify the relationship between short-term damage and long-term Environmental and Operational Conditions (EOCs) by mainly rendering mean lifetime predictions and their associated confidence levels, whereas additional predicted lifetime statistical information is usually overlooked. In this work, we showcase the importance of properly accounting for the variability in lifetime predictions, describe how to summarize binned damages using statistical estimators and investigate bootstrapping variants for computing the confidence levels in the generated damage estimators. ... Read more

The application of Deep Reinforcement Learning (DRL) for the management of engineering systems has shown very promising results in terms of optimality and scalability. The interpretability of these policies by decision-makers who are so far mostly familiar with traditional approaches is also needed for implementation. In this work, we address this topic by providing a comprehensive overview of POMDP- and DRL-based management policies, along with simulation-based implementation details, for facilitating their interpretation. By mapping a sufficient statistic, namely a belief state, to the current optimal action, POMDP-DRL strategies are able to automatically adapt in time considering long-term sought objectives and the prior history. Through simulated policy realizations, POMDP-DRL-based strategies identified for representative inspection and maintenance planning settings are thoroughly analyzed. The results reveal that if the decision-maker opts for an alternative, even suboptimal, action other than the one suggested by the DRL-based policy, the belief state will be accordingly updated and can still be used as input for the remainder of the planning horizon, without any requirements for model retraining. ... Read more