Assessing risk and resilience in the built environment requires a comprehensive understanding of the dynamic interactions between physical spaces and their users across multiple scales. The study aims to develop a framework to support such assessments by identifying and structuring quantitative Key Performance Indicators (KPIs) for evaluating risk and resilience in the built environment. The study combines expert engagement and desk review to identify key factors influencing risk and resilience. It considers a wide range of hazards—both climate-related (e.g., floods, droughts, heat waves) and non-climate-related (e.g., earthquakes)—and examines their impacts on people, buildings, infrastructure, cultural heritage, and urban and territorial systems. Grounded in international guidelines and validated by experts, the proposed set of KPIs enables systematic assessment across scales, user groups, and systems. The KPIs cover risk components such as hazard, exposure, sensitivity, and adaptive capacity, as well as resilience qualities including robustness, rapidity, resourcefulness, and redundancy. Furthermore, the framework incorporates multiple resilience dimensions—environmental, economic, physical, digital, organisational, and human health and well-being—addressing critical gaps in existing assessment tools. By measuring both vulnerability characteristics and resilience qualities of built environment assets, the framework provides actionable insights to inform policies, planning strategies, and project design. This study contributes to advancing integrated and evidence-based approaches for disaster risk reduction and climate resilience, offering a tool to support decision-makers, designers, and practitioners in evaluating current conditions and shaping future development or regeneration pathways.

Extreme temperatures in urban environments exacerbate thermal discomfort and intensify the Urban Heat Island (UHI) effect, particularly during peak warm periods. Pavements, which constitute a significant portion of urban surfaces, contribute significantly to heat retention, whereas soil and vegetated areas aid in cooling through lower heat storage and higher moisture retention. Accurate forecasting of soil and pavement surface temperatures is critical for developing effective UHI mitigation strategies. This paper explores the application of Resistance-Capacitance (RC) models, a type of grey-box model, for soil surface temperature prediction. Unlike purely physics-based and data-driven models, RC models integrate physical principles with data-driven insights, balancing accuracy and interpretability. The proposed methodology is validated using real-world data from a dike in the Netherlands, where an optimal RC model is identified through an iterative process based on the Akaike Information Criterion (AIC). Results demonstrate that a two-node RC model provides a reliable balance between complexity and predictive accuracy, achieving an R² of 0.862 and a mean absolute error (MAE) of 0.675°C. These findings highlight the feasibility of applying RC models for soil temperature prediction while maintaining physical interpretability. Future research could extend this methodology to various soil types and urban surfaces, including pavements, to further enhance predictive capabilities and inform climate-responsive urban design.

Backward erosion piping is a failure mechanism of dikes. Numerical modelling is crucial for design and assessment against BEP. Over 30 models have been developed, each with a different purpose and approach. This paper provides a comprehensive overview of the available numerical BEP models, highlighting their limitations, capabilities, and associated challenges. It discusses the different assumptions and their implications on the representation of BEP. Key challenges in the numerical modelling of BEP are (1) the flow (regime) inside the pipe, which is often simplified, even though the impact of this is relatively unknown. (2) The type of erosion (primary or secondary) differs per model, and even within a given type of erosion, approaches vary. (3) Overcoming the difference in scale is a trade-off between the computational effort and simplification. (4) Furthermore, validation of the physics in BEP modelling is difficult due to a of lack micro-scale experimental data.

Sea level rise can compromise the safety of coastal flood defences, as wave overtopping events are becoming more frequent and severe. This increasing threat emphasizes the need for accurate assessment of wave overtopping hydrodynamics over dikes, which is essential for evaluating flood safety. The currently available methods do not combine computational efficiency, detailed results and general applicability, which limits their use in modelling wave overtopping and the resulting dike erosion. To address these limitations, this study introduces the Wave Overtopping Surrogate Model (WOSM), a novel method for rapidly generating high-quality two-dimensional simulations of wave overtopping over the dike crest and landward slope. The foundation of the WOSM is the Vision Transformer Image to Image (ViTI2I), a new deep learning model that combines an adapted Vision Transformer with a convolutional decoder for next-frame prediction. Trained on CFD wave overtopping simulations, the WOSM accurately reproduces the overtopping hydrodynamics such as flow velocities, water depths, overtopping duration and vertical velocity profiles, including both spatial and temporal variations. The scope of the training data limits the applicability of the WOSM and its ability to consistently capture complex phenomena such as flow separation and reattachment, both of which could be improved by enriching the dataset. Its low computational demand makes it suitable for exploring additional applications, such as probabilistic design or simulating wave overtopping with evolving dike profiles for erosion assessment. Additionally, this study serves as a proof of concept that the WOSM framework could benefit other fields encountering comparable modelling constraints.

Preferential flow induced by desiccation cracks (PF-DC) has been proven to be an important hydrological effect that could cause various geotechnical engineering and ecological environment problems. Investigation on the PF-DC remains a great challenge due to the soil shrinking–swelling behavior. This work presents an experimental and numerical study of the PF-DC considering the dynamic changes of desiccation cracks. A soil column test was conducted under wetting–drying cycles to investigate the dynamic changes of desiccation cracks and their hydrological response. The ratios between the crack area and soil matrix area (crack ratio), crack aperture and depth were measured. The soil water content, matrix suction and water drainage were monitored. A new dynamic dual-permeability preferential flow model (DPMDy) was developed, which includes physically consistent functions in describing the variation of both porosity and hydraulic conductivity in crack and matrix domains. Its performance was compared to the single-domain model (SDM) and rigid dual-permeability model (DPM) with fixed crack ratio and hydraulic conductivity. The experimental results showed that the maximum crack ratio and aperture decreased when the evaporation intensity was excessively raised. The self-closure phenomenon of cracks and increased surficial water content was observed during low-evaporation periods. The simulation results showed that the matrix evaporation modeled by the DPMDy is lower than that of the SDM and DPM, but its crack evaporation is the highest. Compared to the DPM, the DPMDy simulated a faster pressure head building-up process in the crack domain and higher water exchange rates from the crack to the matrix domain during rainfall. Using a fixed crack ratio in the DPM, whether it is the maximum or the average value from the experiment data, will overestimate the infiltration fluxes of PF-DC but underestimate its contribution to the matrix domain. In conclusion, the DPMDy better described the underlying physics involving crack evolution and hydrological response with respect to the SDM and DPM. Further improvement of the DPMDy should focus on the hysteresis effect of the soil water retention curve and soil deformation during wetting–drying cycles.

Soil-atmosphere interaction occurs frequently in most geotechnical situations. By including it in analyses, a high computational load can occur, and analyses are made more complex. This paper explores the key processes and demonstrates the impact and advantages of quantifying soil-atmosphere interactions. Possibilities of utilising the soil atmosphere interaction to accelerate soil processes and reduce environmental impact, and to generate energy are shown, highlighting benefits of consid- ering soil-atmosphere processes. It is also seen that it is not always necessary to directly include the processes in analyses, but that consideration of the processes can lead to approaches to directly monitor or guide monitoring. The soil-atmosphere interface is important, and whether it is necessary, useful or damaging to consider in analyses is situation dependent.

In recent years, environmental sensing of the temperature, strain and strain rate has been revolutionized by the development of fiber-optic based measurements. These tools now allow groundwater hydrologists to measure at very high spatial and temporal scales, the temperature of ground and surface water, the rock strain induced by groundwater pumping and land subsidence, and seismic signals for inversion of complex geologic structures.

This book summarizes the theory and examples of the use of Distributed Temperature Sensing (DTS), Distributed Strain Sensing and Distributed Acoustic Sensing (DAS) for subsurface characterization and analysis of groundwater/surface water exchange.

In this paper, we aim to identify factors affecting susceptibility to drought-induced cracking in levees and use them to build a machine learning model that can identify crack-prone levees on a regional scale. By considering the key relationship between the size of cracks and the moisture content, we observed that low moisture contents act as an important driver in the cracking mechanism. In addition, factors which control the deformation at low moisture content were seen to be important. Factors that affect susceptibility to cracking were proposed. These factors are precipitation, evapotranspiration, soil subsidence, grass color, soil type, peat layer thickness, soil stiffness and levee orientation. Statistics show that the cumulative precipitation deficit is best associated with the occurrence of the cracks (cracks are characterized by higher precipitation deficits). Model tree classification algorithms were used to predict whether a given input of the factors can lead to cracking. The performance of a model predicting long cracks was evaluated with a Matthews correlation coefficient (MCC) of 0.31, while a model predicting cracks in general was evaluated with an MCC of 0.51. Evaluation of the model trees indicated that the peat thickness, the soil stiffness and the orientation of the levee can be used to determine crack-proneness of the levees. To maintain validity and usefulness of the data-driven models, it is important that asset managers of levees also register locations on which no cracks are observed.

Backward erosion piping is an internal erosion process, which compromises the stability of water retaining structures such as dams and levees. In this paper, we propose a numerical solution that combines a 2D Darcy groundwater solution with Exner's 1D sediment transport mass conservation equation. As an estimate of sediment transport, we tested four different empirical transport equations for laminar flow. The model performance was evaluated based on the results of the real-scale IJkdijk experiment. Through this, we were able to demonstrate the applicability of existing sediment transport equations to the description of particle motion during piping erosion. The proposed transient piping model not only predicts the pipe progression in time, it also allows for an identification of pore pressure transitions due to the erosion process. A major conclusion of the study is that from the four different modeling approaches for laminar flow, it is recommended to follow the approach of Yalin et al. regarding the simulation of backward erosion piping for dike configurations similar to those of the IJkdijk experiment.

This paper describes the implementation of an FBG sensor to measure water levels in a dike. The sensor is based on a 3D-printed mechanical transducer through which the external pressure is converted into longitudinal strain exerted on the fiber. An additional FBG integrated within the sensor measures temperature and is used to compensate for the temperature effects on the first FBG. By employing an aluminum alloy case, the sensor is suitable for operations in harsh environments and rough installation procedures. Four sensors of this kind have been successfully tested on a real scale dike at the Water Proof Holland facility in The Netherlands.

Dual-permeability models assume that the complete porous media system can be represented by two different interacting subsystems: the matrix and the fracture pore domain. For some soils like fractured clays, the fracture domain may be empty, which makes its physical behavior differ significantly from capillary flow. Our main hypothesis is that this kind of preferential flow systems can be represented as a dual-permeability porous media by adapting the 2-D formulation and initial conditions of the fracture domain and the mass exchange function. The performance of the dual-permeability finite element solution was evaluated by comparing it to its equivalent 2-D explicit fracture single-permeability finite element model. The results of the numerical experiments show that the 2-D dual-permeability concept allows to simulate preferential flow in soils with fractures. This was achieved by improving the parameterization of the Mualem-van Genuchten soil water retention curve of the fractured domain and the hydraulic conductivity exchange function for the first-order mass exchange term for fractured soils. The exchange term hydraulic conductivity evaluated at the minimum value of the pressure heads of the two domains considerably improved the results as compared to using the well-established arithmetic average of the hydraulic conductivity values from both domains. These two improvements of the dual-permeability model approach are especially useful in cases where preferential flow systems consist mostly of relatively large, noncapillary fractures and macropores.

We present the design and field test of a rugged FBG sensor prototype for high-sensitivity measurement of underground water level. Pressure sensors have many fields of application, ranging from environmental monitoring to the oil and gas industry. In particular, pressure sensors can be used to monitor the stability of dikes and embankments by measuring the inner phreatic level at their foot to detect anomalous filtration and excess of pore pressures. For this application, rather high sensitivity at an affordable cost is required. Fiber optic pressure sensors have been explored with different solutions, but the technologies proposed so far have either small sensitivity, and hence are befitted for large pressure ranges, or are based on interferometry, and hence require rather expensive laser sources. The sensor described in this paper exploits a 3D-printed mechanical transducer to convert external pressure in longitudinal strain along the fiber. A second FBG, embedded in the sensor, is used to compensate for temperature cross-sensitivity. The structure is enclosed in an aluminum alloy case to withstand harsh environments and installation procedures. Pressure and temperature sensitivities of the sensor are about 20 pm/cm H2O and 17 pm/°C respectively. Three sensors of this kind have been successfully tested in a large scale dike at the Flood Proof Holland facility, in Delft, Netherlands.

The article was published Open Access under the Dutch Compact Agreement; however, due to an internal system error, previous HTML rendering of the article did not reflect this.

Hard structures, i.e., roads, are commonly found over flood defences, such as dikes, in order to ensure access and connectivity between flood protected areas. Several climate change future scenario studies have concluded that flood defences will be required to withstand more severe storms than the ones used for their original design. Therefore, this paper presents a probabilistic methodology to assess the effect of a road on top of a dike: it gives the failure probability of the grass cover due to wave overtopping over a wide range of design storms. The methodology was developed by building two different dike configurations in computational fluid dynamics Navier-Stokes solution software; one with a road on top and one without a road. Both models were validated with experimental data collected from field-scale experiments. Later, both models were used to produce data sets for training simpler and faster emulators. These emulators were coupled to a simplified erosion model which allowed testing storm scenarios which resulted in local scouring conditioned statistical failure probabilities. From these results it was estimated that the dike with a road has higher probabilities (5 × 10^-5 > P_f > 1 × 10^-4) of failure than a dike without a road (P_f < 1 × 10^-6) if realistic grass quality spatial distributions were assumed. The coupled emulator-erosion model was able to yield realistic probabilities, given all the uncertainties in the modelling process and it seems to be a promising tool for quantifying grass cover erosion failure.

Structures integrated in a grass-covered dike may increase erosion development. Currently, safety assessment methods for flood defences are only applicable for a conventional grass-covered dike and the effects of structures on dike cover erosion are poorly understood. Since many dikes have a road on top, it is important to study the effect of such a road structure on erosion onset during wave overtopping. To investigate this effect, a coupled hydrodynamic–eroison model was developed. The erosion onset caused by overtopping waves was predicted by combining the time-varying bed shear stresses from the hydrodynamic model with a depth-dependent erosion model. The results show that roads on top of a dike increase the erosion of the neighbouring grass cover. This increase in erosion may have a negative impact on dike stability. Therefore, we recommend considering effects of constructions on top of dike profiles during safety assessments. Explicitly, consideration of the roughness transitions in the safety assessments of dikes is recommended.

The present study aimed to include the turbulence effects derived from a road located over the crest of a dike, in its probabilistic safety assessment. This was done by building two different computation fluid dynamics models (RANS K-); one of a dike with a road on top and one without it. Both models were validated with experimental data collected from the Wave overtopping simulator experiments performed in the Netherlands. These models were used to produce training data sets which were later used for constructing emulators (computationally cheaper models) which allowed to reduce the computational burden from the required stochastic modelling. These new emulators allowed to calculate bottom shear stress time series in different locations along the dike profile. With these time series, it is possible to estimate the potential scouring depth per wave volume routed. These emulators allowed to model different probabilistic overtopping scenarios without running the CFD models again. The results showed that when assessed under extreme climate scenarios, the presence of a road may reduce the dike safety by almost 50% with respect to the case where no road is present. In addition, it is also concluded that the spatial grass quality distribution is a more important factor for determining dike safety than the spatial grass cover thickness.

In the case of wave overtopping, structures constructed above the flood defense will change the hydrodynamic behavior of the overtopped waves. This will change the scouring rates of the inner grass cover.

Flood defenses are exposed to deterioration processes that compromise their stability. These processes are called ‘failure mechanisms’ and they are mostly triggered and/or exacerbated by the hydraulic loads generated during extreme high water events, which are becoming more frequent due to climate change. Concepts such as the ‘Delta dike’ or the ‘Unbreachable dike’ have been developed in the Netherlands to deal with these failure mechanisms.

Fiber optic cables are commonly known for being robust and reliable mediums for transferring information at the speed of light in glass. Billions of kilometers of cable have been installed around the world for internet connection and real time information sharing. Yet, fiber optic cable is not only a mean for information transfer but also a way to sense and measure physical properties of the medium in which is installed. For dike monitoring, it has been used in the past for detecting inner core and foundation temperature changes which allow to estimate water infiltration during high water events. The DOMINO research project, aims to develop a fiber optic based dike monitoring system which allows to directly sense and measure any pore pressure change inside the dike
structure. For this purpose, questions like which location, how many sensors, which measuring frequency and which accuracy are required for the sensor development. All these questions may be initially answered with a
finite element model which allows to estimate the effects of pore pressure change in different locations along the cross section while having a time dependent estimation of a stability factor. The sensor aims to monitor
two main failure mechanisms at the same time; The piping erosion failure mechanism and the macro-stability failure mechanism. Both mechanisms are going to be modeled and assessed in detail with a finite element based
dual permeability Darcy-Richards numerical solution. In that manner, it is possible to assess different sensing configurations with different loading scenarios (e.g. High water levels, rainfall events and initial soil moisture
and permeability conditions). The results obtained for the different configurations are later evaluated based on an entropy based performance evaluation. The added value of this kind of modelling approach for the sensor
development is that it allows to simultaneously model the piping erosion and macro-stability failure mechanisms in a time dependent manner. In that way, the estimated pore pressures may be related to the monitored one and
to both failure mechanisms. Furthermore, the approach is intended to be used in a later stage for the real time monitoring of the failure.