Current wind design codes incorporate turbulence through gust factors and rely on historical wind data, including tropical cyclones. While generally conservative, standard code wind profiles and spectra do not fully reproduce the vertical distribution and dynamic characteristics of hurricane winds, particularly in the supergradient region near the eyewall, and can sometimes underestimate tail risks, low-probability, high-impact events, as observed during Hurricane Otis in Acapulco (2023). This study probabilistically evaluates wind-induced vibrations in high-rise buildings with different lateral resisting systems equipped with fluid viscous dampers (FVDs), under non-tropical storm and tropical cyclone conditions. Along-wind loads were modeled in the time domain as stationary, multidimensional stochastic processes and analyzed using one million Monte Carlo simulations and Incremental Dynamic Analysis on the DelftBlue supercomputer. Statistical distributions of responses, bivariate dependence via copulas, and fragility curves were obtained. Results show that wind type, structural deformation mode, and damper properties significantly affect response distributions, correlation structures, and failure probabilities. FVDs effectively reduce structural dynamic response, improving serviceability, while increased shear stiffness further reduces fragility. Modeling hurricane winds as non-tropical storms can overestimate damper effectiveness. These findings provide insights for refining wind codes and designing high-rise buildings that remain safe and functional under extreme events.

Infrastructures are facing growing challenges due to their aging process while climate change and evolution of traffic and shipping fleets are increasing the uncertainty of loadings in the future. This study proposes a method to assess the survivability of structures with gradual deterioration under changing loading scenarios based on field data. The methodology is applied to the armor deterioration of a rock-armored groyne under ship-wave attack. First, we generate synthetic timeseries of damage by coupling a Poisson distribution to determine the number of passing ships per day, a vine-copula to quantify the multivariate joint distribution of the loading variables that define the primary wave height and a Bernoulli process and a bivariate copula to translate the primary wave height into the increment of damage. Afterwards, these damage curves are used to quantify a Gamma process. Thus, it is possible to conditionalize the joint distribution of the loading variables to generate the damage curves under different loading scenarios and evaluate the effects of these scenarios on the structure’s survivability. We exemplify the use of the methodology to assess the armor deterioration of a rock-armored groyne under ship-wave attack with and without a limitation in the speed velocity in the waterway.

Extreme storms over the North Sea drive coastal flood risk in the Netherlands, causing high waves and extreme sea levels. Designing flood defenses requires accurate statistical extrapolation of hydraulic load conditions with return periods of 1,000 years or more. This is a challenging task given limited observational data. This study uses a large, simulated dataset ( 9,000 years) to explore the statistical dependence between extreme wind speed u and surge height s. Storms were clustered using several techniques. Self-organizing maps (SOM) effectively captured physical relationships, such as the influence of wind direction and tidal offset on storm dynamics, however variability in statistical dependence between u and s for different clusters was better represented using manual clusters. Copula models were fitted to the cluster data, with the BB8 copula outperforming others. This study illustrates the potential of machine learning to identify patterns in large datasets while emphasizing the relevance of manual clustering approaches for revealing nuanced statistical dependencies critical to flood risk assessment.

Study Region: The study examines the Geul River basin in the southern Netherlands, a small (343 km2), hilly, rain-fed catchment characterised by a rapid rainfall-runoff response and exposure to both floods and droughts. Recent extremes, including the July 2021 flood and multiple dry summers, make the basin a relevant case study for understanding transitions between these opposite hydrological conditions. Study Focus: Transitions between drought-flood and flood-drought events were analysed using observations and hydrological simulations for 1982–2018. Floods and droughts were defined from meteorological thresholds and extended by an empirically derived catchment response time to capture hydrological effects. The role of antecedent precipitation, temperature, streamflow, soil moisture, and groundwater storage on subsequent event’s hydrological response was assessed using correlation and distribution (Kolmogorov–Smirnov) analyses. New Hydrological Insights for the Region: Antecedent droughts were associated with lower flood peaks, likely reflecting greater absorption of rainfall by dry soils prior to runoff generation. In contrast, antecedent floods were not associated with a consistent mitigation of streamflow response during subsequent droughts, consistent with continued streamflow recession from prior high flows. Groundwater showed a slower and more gradual adjustment than surface fluxes at the event scale, consistent with sub-seasonal storage buffering. Overall, opposite antecedent conditions in the Geul basin tend to moderate, rather than amplify, subsequent extremes. These findings emphasise the importance of considering antecedent catchment conditions and longer-term subsurface storage when evaluating flood and drought interactions in regional water management.

This study explores the statistical dependence between wind speed and surge height along the Dutch coast using a large synthetic dataset. Storms were clustered based on wind direction, tidal offset, wind rotation, tidal peak, surge and wind exceedance duration, resulting in 16 clusters per wind direction and per location. Apart from wind direction, comparing clusters revealed a limited impact of clustering based on these storm characteristics on the choice of the best-fitting copula model, suggesting sub-clustering may not be necessary for accurately representing the statistical dependence between extreme wind speeds and surge heights. The BB8 copula generally provided the best fit to the data. However, the observed upper tail dependence did not decrease to zero, particularly for western to northern wind directions, indicating non-negligible dependence in joint extremes of wind speed and surge height. Therefore, applying the BB8 copula (or any other copula model without upper tail dependence) may lead to underestimation of the flood risk, when applied in probabilistic analyses. The findings from this study provide valuable insights for refining hydraulic load models for reliability assessments and design of flood defenses.

Extreme sea level events pose significant risks to coastal regions, with non-tidal residuals (NTRs) being a primary driver in low-lying areas like the Netherlands, where shallow seas amplify their impact. This study investigates the spatial patterns of NTRs along the Dutch coast using time series clustering on historical NTR hydrographs. The design of hydraulic boundary conditions divides the Netherlands into three coastal regions. To evaluate whether this division sufficiently captures regional variability, three clustering scenarios (k = 3, k = 4, and k = 5) were explored. The analysis identified k = 5 as the optimal configuration based on the Davies-Bouldin index. This result emphasized the importance of fine-scale approaches to understanding regional spatial variations in NTR dynamics. Regional bathymetry and tide-surge interactions were explored as drivers of these spatial patterns. Southern stations near river systems and deeper waters displayed characteristics distinct from northern stations in the Wadden Sea, which are influenced by shallow tidal flats. Analysis of the M2 tidal constituent and the timing of NTR maxima relative to high tides underscored the role of tidal dynamics in shaping spatial clusters. Future research will focus on integrating spatio-temporal patterns and environmental drivers into clustering methodologies, providing deeper insights for coastal risk management and adaptation strategies.

Coastal regions in the Netherlands face persistent challenges from sea level extremes, prompting a comprehensive exploration of their spatial variability. Our study explores the nuances of extreme sea level events across the country, using the observed sea level data from the GESLA-3 (Global Extreme Sea Level Analysis) dataset. We analyse 16 stations with observational periods spanning from 38 to 68 years. The total observed sea level is detrended and split into two components: (i) the tidal component, derived using harmonic analysis, and (ii) the non-tidal residual, calculated by subtracting the obtained tidal signal from the observed sea-level records. Extremes of both total sea level and non-tidal residual are then identified using the Peak over Threshold method, opting for a 70th percentile threshold. This choice allows us to examine less severe scenarios, suitable for risk assessments or planning purposes. Our preliminary analysis of extreme event characteristics, such as the duration and intensity of an event, indicates significant spatial differences across stations. Correlation coefficients between stations, particularly for total extreme sea level characteristics and extreme non-tidal residual characteristics (duration and intensity), show a noticeable pattern that consistently reveals higher values between stations with similar latitudes across all variables. Moreover, the distributions of total extreme sea level characteristics exhibit noteworthy differences as well - for example, in southern regions, the distributions of intensity are more broadly dispersed and skewed to the right, signifying higher events than those in the northern counterparts. However, this distinction is less pronounced when focusing solely on the non-tidal residual, possibly since the total sea level is influenced by factors such as the river inflow, prevalent in the south, and tidal propagation behaviour in the North Sea. As we progress with our analysis, we plan to apply a supervised learning method for classifying extreme events based on storm characteristics, and conduct a clustering analysis to reveal hidden spatial patterns of extreme events, for both total sea level and non-tidal residual. Furthermore, we aim to explore the interactions between surges and tides across different classes of extreme events, unravelling the underlying driving mechanisms of enhanced compound events. In summary, our ongoing study of sea level extremes in the Netherlands, from spatial dynamics to event characteristics, will provide a solid foundation for understanding the driving mechanisms behind the extremes, gaining insights about their natural variability, and evaluating the impacts of changing climatic conditions.

In absence of sufficient data, structured expert judgment is a suitable method to estimate uncertain quantities. While such methods are well established for individual variables, eliciting their dependence in a structured manner is a less explored field of research. We tested the performance of experts in constructing and quantifying a nonparametric Bayesian network, describing the correlation between river tributary discharges. Specialized software was provided to assist the experts. Expert performance was investigated using the dependence calibration score (a correlation matrix distance metric) and the likelihood of the joint distribution. Desirable properties of the dependence calibration score were investigated theoretically. Individual expert judgments were combined based on performance into a group opinion aka decision maker. All experts were able to create and quantify a correlation matrix between 10 variables that resembled the correlations between observed discharges well. The decision makers performed similarly to the best expert. Based on the metrics investigated, it mattered little which expert opinions and with what weight were combined in a decision maker. This is partly because all experts performed well. Adding a bad performing expert increased the positive effect of performance-based weighting, underscoring the importance of developing scoring rules for dependence elicitation. The overall results are promising: Aided by specialized graphical software, the experts in this study were able to quickly create and quantify dependence structures.

This study evaluates five scoring rules, or measures of statistical accuracy, for assessing uncertainty estimates from expert judgment studies and model forecasts. These rules — the Continuously Ranked Probability Score ( CRPS ${CRPS}$ ), Kolmogorov-Smirnov ( KS ${KS}$ ), Cramer-von Mises ( CvM ${CvM}$ ), Anderson Darling ( AD ${AD}$ ), and chi-square test — were applied to 6864 expert uncertainty estimates from 49 Classical Model (CM) studies. We compared their sensitivity to various biases and their ability to serve as performance-based weight for expert estimates. Additionally, the piecewise uniform and Metalog distribution were evaluated for their representation of expert estimates because four of the five rules require interpolating the experts' estimates. Simulating biased estimates reveals varying sensitivity of the considered test statistics to these biases. Expert weights derived using one measure of statistical accuracy were evaluated with other measures to assess their performance. The main conclusions are (1) CRPS ${CRPS}$ overlooks important biases, while chi-square and AD ${AD}$ behave similarly, as do KS ${KS}$ and CvM ${CvM}$ . (2) All measures except CRPS ${CRPS}$ agree that performance weighting is superior to equal weighting with respect to statistical accuracy. (3) Neither distributions can effectively predict the position of a removed quantile estimate. These insights show the behavior of different scoring rules for combining uncertainty estimates from expert or models, and extent the knowledge for best-practices.

Human drivers significantly influence flood occurrence and impacts through multiple avenues. In this work, we explore how human drivers contributed to flood risk in 42 European countries between 1950 and 2020, with particular focus on 1504 historical floods that caused significant socioeconomic impacts. Our modelling chain covers both riverine and coastal floods and is able to reconstruct past extreme events including the influence of (1) human impact on catchment hydrology through changing land use, water demand and reservoir capacity, (2) increase in exposure related to land use change, demographic and economic growth, and evolving structure of the economy, and (3) changes in flood preparedness, exhibited by flood protection levels (primarily from structural defences) and flood vulnerability (relative loss at given intensity of hazard). The results indicate that although construction of large reservoirs (the number of which increased six-fold in the study area since 1950) has locally led to a pronounced decline in riverine flood risk, human alterations to catchments overall increased the flood risk in Europe due to land-use change, particularly through strong increase in soil sealing caused by urbanization. An even stronger relative effect on the increase in flood impacts is caused by exposure growth, consisting of population growth, particularly in cities, a rapid increase in gross domestic product per capita, and further compounded by growth in capital-to-income ratio. Exposure growth is more pronounced for coastal floods compared to riverine floods. On the other hand, historical flood impact data analysed in this study show evidence of improving preparedness over time. Flood defences currently protect against higher return periods of floods than before, particularly for coastal floods, though they are mostly much lower than assumed in previous pan-European studies. A decline in flood vulnerability (relative losses) over time is also observed, partially compensating for negative human influences on flood risk.

Groynes and revetments have suffered an increase in the severity of their damage in the German estuaries during the past years due to the increase in the ship-induced loads. Consequently, there is a pressing demand for further tools to model the structure deterioration over time with the aim to facilitate the planning of maintenance and repair measures. This damage evolution is directly caused by the ship-induced waves and comes with a high level of uncertainty due to the complexity of the involved phenomena (e.g.: ship-wave generation or wave-structure interaction). Therefore, a probability-based estimate of that damage evolution over time is essential for the optimization of the maintenance/repair decisions of these structures. The goal of this research is to quantify the survivability of a rock-armored groyne at Juelssand in the Lower Elbe Estuary based on field measurements. First, an algorithm to generate random damage curves (based on the field measurements) is developed to overcome the data scarcity from the field and improve the subsequent phases of the analysis. After that, a Gamma process is applied to model the structure survivability; it is fitted using the generated random damage curves and the survivability of the groyne is studied for different tolerable damage levels.

Effective resource planning in higher education requires anticipating student demand for courses, especially when dealing with elective programs. Monitoring student preference is a recurring topic in the literature; however, to the authors’ knowledge, no simple methods for estimating student preferences when choosing courses in higher education have been proposed. This study develops and explores the use of a simple questionnaire to capture patterns in student course preferences within a university context. The research is developed in the context of the nine Cross-Over modules offered as part of the curriculum of the master’s programs (MSc) of the Faculty of Civil Engineering and Geosciences of Delft University of Technology (The Netherlands). No prior registration is required far in advance for these courses, making an accurate estimation of student numbers critical for the planning and allocation of educational resources. The developed questionnaire is applied three times in two different academic years to the students’ choice of Cross-Over modules. The questionnaire was shared in 2021, with 225 responses out of 339 students, in 2022, with 159 responses out of 365 students, and in 2024, with 94 responses out of 272 students. Student enrollment in the academic year 2023/2024 is used to assess the performance of the questionnaire. The questionnaire is able to capture general preferences of the students, providing fair estimates of the number of students per course; larger differences are observed in courses with a lower number of students. In addition, some patterns were identified in student preferences: there is a relationship between the first and second choices, and students usually choose modules closer to their own disciplines. The developed questionnaire provides with a reasonable first estimation of the expected number of students in courses, allowing for better planning and allocation of educational resources beforehand.

Economic losses of bridge failures can mount to millions of dollars per day and spiral quickly. In particular, steel truss bridges are highly vulnerable to member failures, which, if propagated, can cause severe disruptions to the entire system. The vulnerability of these structures has been underscored in recent bridge collapses, which were initiated by the propagation of localised member failures (e.g., I-35W Mississippi Bridge). This paper proposes a methodology for the structural assessment of member failure scenarios in steel truss bridges. A quantitative index (SoD) is proposed to evaluate the consequences of member failures in all bridge elements. The methodology includes a Bayesian Network that captures the relationship between load models and structural responses. Additionally, the methodology integrates Extreme Value Analysis and computes the expected SoD for a 100-year return period. Two complementary approaches are suggested for the analysis of the member failure scenarios. The first approach focuses on the failure scenario itself, examining the post-failure effects in all bridge elements. The second approach evaluates the response of individual elements to various failure scenarios, allowing an in-depth understanding of how different member failures influence specific bridge elements. The methodology has been tested on a railway steel truss bridge in which eleven member failures were simulated. Results allowed to identify the level of significance for the scenarios, providing insights to guide SHM strategies, prioritise interventions and optimise maintenance efforts. This work aims to simplify engineering efforts and support bridge management entities in their crucial fight to improve the bridge’s structural safety.

Base isolation of high-rise buildings has growing popularity to limit peak floor accelerations under seismic loads; however, it may increase susceptibility to wind-induced vibrations due to the increase in fundamental vibration period. This study presents an equivalent coupled-two-beam (CTB) model incorporating base isolation (BI) and a tuned mass damper inerter (TMDI) to evaluate passive vibration control under lateral wind loads for various lateral resisting systems. A 144-meter-tall building was analyzed under along-wind and across-wind loads simulated as Gaussian processes, considering six isolator-damper configurations: (1) fixed-base (FB), (2) FB with a top TMDI (FB-TTMDI), (3) BI, (4) BI with a top TMDI (BI-TTMDI), (5) BI with a bottom TMDI (BI-BTMDI), and (6) BI with double TMDI (BI-DTMDI). TMDIs were compared to traditional tuned mass dampers (TMDs) to assess mass amplification under varying base isolator damping. Optimization strategies were explored to enhance vibration control: for FB-TTMDI, the TMDI placement minimized RMS accelerations, while for BI-TTMDI, it was optimized to reduce peak displacement. Finally, design guidelines are provided for ultimate and serviceability limit states. Results indicate hybrid control systems are most effective when lateral deformation resembles pure bending, making them suited for shear wall-frame and tubular systems.

Rock groins in the Elbe Estuary are constructed to maintain proper water levels for navigation and for embankment erosion protection. At certain localities, significant damages to rock groins have been observed due to the primary ship-generated waves. Primary waves are generated along the ship's hull and then propagate toward the river banks and groin fields, appearing in the interaction with the structures as a turbulent overflow phenomenon. Eventually, this overflowing may cause damages mainly to the crest and leeward side of the groins. Since this overflowing is the most pronounced with large primary waves at certain water levels, the estimation of the probabilities of extreme primary waves is a key element for a safe and reliable design of groins. For this goal, nonparametric Bayesian networks (NPBNs) are used here to infer the probability distribution function of the extreme primary wave heights at the tip of a groin in the Elbe Estuary. Results demonstrate the suitability of the NPBN in their prediction. The model framework allows the designer to predict the probabilities of primary ship-generated waves at groins when the information of ship dimensions, nautical parameters, and waterway geometry is available. These probabilities can later be used for design purposes for current and future conditions.

Due to changing climates and rising sea levels, low-lying coastal regions, such as the Netherlands, face increased risks of flooding driven by extreme sea levels. Thus, understanding extreme sea level events and their underlying dynamics is crucial for effective coastal management. This study developed and applied a novel classification framework to investigate historical storm surge events along the Dutch coast and improve the understanding of regional storm surge dynamics. Using 16 sea level records, storm surges were identified with the Peak Over Threshold (POT) method, using the 70th (POT70) and 99th (POT99) percentiles as thresholds. POT70 captured a more comprehensive storm surge activity, including multiple peaks and successive surges that are critical for coastal management. In contrast, POT99 captured surge peaks but missed significant pre- and post-storm surge activities. The POT70-derived surges were classified into 56 event types using clustering methods based on surge values across the whole event time series, and event duration. Event types were then characterised by temporal patterns, peak magnitude, duration, probability of occurrence, yearly frequency, and cumulative surge intensity. Key findings revealed frequent two-peak storm surges and significant variations in storm surge intensity along the coast, with stronger events occurring in northern regions. The results highlight the complexity of storm surge patterns, indicating that while simplified hydrograph models are useful, they may not always capture the full range of surge pattern variations. This novel classification framework offers a more detailed approach to evaluating surge patterns and can be applied to other coastal regions as well.

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.

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.

The Netherlands has traditionally focused on managing flood risk. However, the frequent occurrence of droughts in recent years has brought attention to managing both extremes. Transitions between these opposite extremes pose additional challenges to water management, requiring a trade-off between water storage during dry periods and flood control during wet periods. In this study, we develop a framework to define wet and dry meteorological events and study their transitions using timeseries of meteorological data namely, precipitation, temperature and potential evapotranspiration. The magnitudes of event characteristics are retained, which presents a different approach to the normalized climate indices (like the Standardized Precipitation Index) commonly used in literature. We apply this framework to the Dutch part of the Meuse River basin in northwestern Europe using climate observations between 1951 and 2022. Our analysis shows a statistically significant increase in the amount of water lost from potential evapotranspiration compared to water gained from precipitation between April and September of the water year and an increase in the length of this drying period over the past decades. Such trends in the drying period are related to variability in potential evapotranspiration caused by rising temperatures in the region, indicating the potential for increased water shortage in Spring and Summer due to future temperature increases. We also identify abrupt transitions between opposite extreme events where there is a lack of water at the end of the second event as meteorological situations that challenge water management due to overlapping impacts like flash flooding, less time for water storage, and reduced water availability. We see such conditions occur in 6% of the wet-dry transitions and 20% of the dry-wet transitions, highlighting meteorological scenarios to which the hydrological response of the catchment can be simulated to increase our understanding of the combined risk of floods and droughts.