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.

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.

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.

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.

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.

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.

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.