Search Results (1,210)

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35–1.91 m, cross-city MAE 2.05–3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen’s kappa 0.493 across regulatory zones, and Spearman ρ 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3–8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys. Full article

Accurate forecasting of sediment accumulation under extreme hydrodynamic forcing is essential for coastal engineering design and harbor management. This study evaluates the performance of Dynamic Mode Decomposition (DMD), optimized DMD (optDMD), and optimized DMD with stability constraints (optDMDs) for reconstructing and forecasting sediment accumulation height fields at the Dilderesi River mouth under a 50-year return period flood scenario. Sediment height fields generated using Delft3D are represented through reduced-order modal decompositions and the truncation rank is determined based on reconstruction-error analysis. Although all formulations reproduce the training data with negligible error, their predictive behavior differs during temporal extrapolation. Standard DMD exhibits rapid error growth at longer lead times. The optDMD formulation improves short- and intermediate-horizon performance but shows gradual degradation at extended lead times. Optimized DMD with stability constraints provides the most consistent long-horizon forecasts, maintaining high Nash–Sutcliffe efficiency and low RMSE across the full 9 h prediction interval. Examination of the continuous-time eigenvalue distributions and modal dynamics indicates that spectral characteristics of the reduced-order representation govern forecast robustness. The results demonstrate that enforcing spectral stability within reduced-order frameworks substantially enhances morphodynamic forecasting reliability under extreme flood conditions. The proposed approach provides a computationally efficient and physically consistent tool for sediment dynamics prediction in coastal engineering applications. Full article

Geosynthetics-reinforced soil technology represents an innovative reinforcement method for calcareous sand foundations and revetment engineering in coral reef areas. The interaction response at the reinforced soil interface directly influences the safety and stability of reinforced soil structures. However, research on the interaction mechanisms between geosynthetics and calcareous sand interfaces remains insufficient. Therefore, this paper investigates the effects of different normal stresses and various interface types on the shear characteristics of the geosynthetics–calcareous sand interface through a series of large-scale monotonic direct shear tests. By integrating statistical damage theory and accounting for the influence of residual strength, we establish the constitutive relation for interface damage. The results indicate that the shear stress–displacement curves for both the geosynthetics–calcareous sand interface and the unreinforced calcareous sand exhibit softening behavior. Furthermore, the relationship between the interface shear modulus and horizontal displacement for the geogrid–calcareous sand and unreinforced calcareous sand adheres to a power function model, while the relationship for the geotextile–calcareous sand follows a logarithmic function model. In the structural design of geosynthetics-reinforced calcareous sand, it is crucial to consider the influence of residual shear strength on structural stability. This study proposes a statistical damage constitutive model that accounts for the strain-softening characteristics of the geosynthetics–calcareous sand interface, while also considering the impact of residual strength. The findings provide a theoretical basis for the stability analysis of geosynthetics-reinforced calcareous sand structures in coral reefs with significant engineering implications for island reef construction, coastal development, and bank slope protection projects. Full article

Ras El-Bar, a premier historic coastal resort on Egypt’s Nile Delta, has experienced a marked increase in drowning incidents in recent years, despite the presence of extensive coastal protection structures. While these measures, particularly detached breakwaters (DBWs), groins, and port jetties, were originally implemented to mitigate shoreline erosion, their influence on nearshore hydrodynamics and swimmer safety remains insufficiently understood. In this context, the present study integrates high-resolution bathymetric data, remote sensing observations, and coupled numerical modeling (CMS-Wave and CMS-Flow) to examine how these interventions have altered wave–current interactions. The results indicate that the modified coastal setting produces distinct flow regimes, ranging from weak offshore currents (<0.1 m/s) to moderate rip currents (≈0.25 m/s) within DBW shadow zones, and locally intensified flows exceeding 0.7 m/s in shallow nearshore areas. These conditions facilitate the development of vortices and persistent rip currents, particularly within inter-DBW embayments. A simulation-based swimming risk map was developed by integrating water depth and simulated current characteristics, classifying the coastline into safe, moderate-risk, and high-risk zones. High-risk zones, concentrated within inter-DBW embayments at depths exceeding 2 m, show broad spatial agreement with available drowning and rescue incident records, subject to the limitations of the informal dataset, while the shallow accretional shadow zones landward of the DBWs exhibit comparatively lower hydrodynamic energy and safer conditions. Overall, the study demonstrates that coastal protection structures, although effective in controlling erosion, may unintentionally increase human risk when safety considerations are not incorporated into their design and management. Accordingly, a set of integrated, sustainability-oriented measures is proposed, including enhanced real-time monitoring, regulated beach access, adaptive sand nourishment, and targeted public awareness, with the aim of achieving a more balanced and resilient approach to coastal zone management. Full article

Silica fume (SF) is a valuable industrial by-product for low-carbon cementitious systems, but it weakens early-age strength due to slow pozzolanic activation. To overcome this limitation and, crucially, to elucidate the influence of pore system geometry on macroscopic performance, graphene oxide (GO) was introduced as a modifying agent. Concurrently, the fractal dimension (D) of the pore network was adopted as a pivotal descriptor linking microstructure to macroscopic strength. Results show that GO compensates for the early strength loss caused by SF and further amplifies long-term gains by accelerating hydration and promoting gel continuity. SF reduces total porosity through filler and pozzolanic reactions, while GO dramatically increases geometric complexity of pores, producing the highest fractal dimension and the most refined pore structure in the matrix. Critically, the proposed log–log interaction model demonstrates that compressive strength is jointly controlled by porosity and fractal dimension, rather than porosity alone. Higher fractal dimension intensifies strength gains in low-porosity matrices by reflecting improved pore connectivity control and energy-dissipation pathways. This establishes fractal dimension as a powerful, mechanistically interpretable index for predicting performance and guiding structural design in SF–GO modified cementitious composites. Full article

Croatia hosts one of the most intensive recreational boating activities in the Mediterranean, with over 134,600 registered vessels along 5835 km of Adriatic coastline. This paper presents an AI-driven simulation framework for evaluating electrification pathways for the Croatian recreational vessel fleet. A key contribution is the explicit treatment of the AIS data gap: recreational vessels in Croatia are not required to carry AIS transponders, so synthetic operational profiles calibrated from manufacturer specifications and verified economic data are used instead. Six machine learning architectures are compared for vessel energy demand forecasting, with a proposed Transformer-based model achieving the best simulated performance. Fleet-weighted Monte Carlo simulation across three electrification scenarios suggests that an AI-optimised hybrid configuration can, subject to use intensity, reduce per-vessel CO₂ emissions by up to 56.8% relative to conventional engines. Techno-economic analysis shows payback periods ranging from over 15 years for low-use private owners to 7–9 years for charter operators, supporting targeted incentive design. The framework is intended to be transferable to other Mediterranean coastal regions facing comparable data and operational constraints. Full article

The Zhuanghe coast in the northern part of the Yellow Sea is one of China’s important fishing and ocean engineering areas. Frequent storm surge events pose a significant threat to residents’ safety and properties. This study used the coupled Finite Volume Coastal Ocean Model (FVCOM) and the Surface Wave Model (FVCOM-SWAVE) to investigate storm surges and wave heights during Typhoons Muifa (1109) and Lekima (1909) in the northern parts of the Yellow Sea and analyze the impact of the typhoon parameters on flood inundation on the Zhuanghe coast. The wind stress comparison in the coupled wave–current model uses synthetic wind field data formed by superimposing ERA5 wind fields with a parameterized typhoon model. The results showed that the simulated and measured tide levels, wave heights, and storm surges were in good agreement, indicating that the coupled model accurately reproduced the dynamics of the storm surges and wave heights during the two typhoons. The maximum significant wave height (H_s) exhibited a right-skewed distribution in the two typhoons’ paths, with extreme values consistently located to the right of the typhoon’s center. The decrease in atmospheric pressure at the center of Typhoon Muifa was significantly, nonlinearly, and positively correlated with the severity of storm surge disasters. A significant correlation was observed between the path of Typhoon Muifa and the disaster intensity. Full article

The viae romanae (Roman roads) were constructed according to precise designs and exceptional engineering techniques, ensuring their strength and durability. They represent an immeasurably important factor in human history. Their impact has been universal, facilitating the movement of people, goods, ideas, beliefs and religions over the centuries. The Via Appia Regina Viarum, built between the end of 4th and 1st centuries BCE, connected Rome to Brundisium, spanning the region of Latium and Apulia. The road initially crossed the coastal plains of the Tyrrhenian Sea (in Latium) before cutting through the reliefs and river valleys of the southern Apennines (in Campania) and finally crossing the regio Apulia et Calabria via Tarentum, to the harbor of Brundisium, along the Adriatic coast. In 2024, the Italian Ministry of Culture proposed the ‘Via Appia Regina Viarum’ for inscription on the Unesco World Heritage List, recognizing its unique and exceptional testimony to Roman civilization. Later that same year, the nomination was accepted, and today, the Via Appia is part of the UNESCO World Heritage List. A significant contribution to this nomination came from the multidisciplinary studies and research conducted along the Via Appia between the ancient cities of Beneventum and Aeclanum in the Campanian Apennine, including: (1) geoarcheological investigation aimed at identifying the ancient path of the road, which was not well documented in the area between Beneventum and Aeclanum; (2) studies focused on cultural and geological heritage along the road and its surrounding landscapes, enhancing the value of the nomination; and (3) the organization of social and cultural events designed to disseminate scientific findings and raise awareness among scientists, students, local and national administrators, local food and wine producers, and the general public. This paper highlights the pivotal role of geoscience at all stages of the project: from preliminary field surveys and mapping of landforms and lithofacies, to targeted field and geophysical surveys, to archaeological excavation and geoarchaeological consideration, and to the dissemination of new data through cultural events. Full article

Given the continuous expansion of global trade, coastal and estuarine environments have been increasingly modified by anthropogenic pressures associated with port development, particularly through inlet stabilization by jetties, which often causes unintended environmental changes. This study evaluates alterations in estuarine and coastal hydro-sedimentological dynamics resulting from the construction of jetties (1911–1915) in the Patos Lagoon estuary, Brazil. A calibrated and validated numerical model (TELEMAC-3D) was used to compare pre-jetties and present conditions. Results showed that the morphological changes induced by the jetties altered estuarine circulation and sediment retention mechanisms. The reduction in current velocities within the channel increased sediment trapping, decreasing sediment transport capacity towards the adjacent coast. In contrast, along the plume jet, flow acceleration enhanced offshore export of fine suspended sediments, shifting deposition from nearshore areas to deeper offshore zones. Under northeastern wind conditions, a higher potential for mud deposition near the western jetty was observed in the post-construction scenario, reflecting a change in local deposition trends. These human-induced modifications not only reorganize sediment pathways but also influence habitat distribution and deposition patterns, highlighting the importance of considering engineering structures in sustainable coastal and estuarine management strategies. Full article

Since 1978, Chinese–foreign cooperation in running schools (CFCRS) has evolved from fragmented pilot initiatives into a policy-coordinated system of higher education internationalization. This study employs an exploratory sequential mixed-methods design to examine how national policy shifts reshaped the structure of CFCRS collaboration networks between 1978 and 2025. Integrating longitudinal policy analysis with Social Network Analysis (SNA), the research identifies five policy-driven stages: exploratory opening, legal institutionalization, regulated development, quality enhancement, and strategic repositioning. Network analysis shows that increasing density, expanding degree centrality of leading institutions, and greater diversification of international partners reflect growing integration into global transnational higher education networks. At the same time, persistent structural concentration in key institutional hubs and regulated entry into partnerships indicate strong path dependence shaped by state-steered governance. The network also exhibits a disciplinary shift toward engineering and STEM collaborations aligned with national innovation strategies, alongside gradual spatial diffusion from coastal regions toward central and western provinces. Conceptually, the findings demonstrate that state-coordinated internationalization can generate dense and diversified collaboration networks without fully liberalizing governance structures. The CFCRS case thus illustrates a model of hybrid governance, where centralized policy coordination coexists with expanding network-based international partnerships. Full article

Coral reefs are among the most biodiverse marine ecosystems, playing irreplaceable roles in maintaining marine ecological balance and coastal services. Under dual pressures of global climate change and human activities, coral bleaching and degradation have become increasingly frequent, creating an urgent need for large-scale, long-term, and highly automated monitoring technologies. In recent years, advances in underwater imaging and deep learning have made visual recognition a core approach for coral classification and health assessment. However, most studies only focus on isolated model accuracy optimization, lacking systematic full-chain analysis integrating datasets, model evolution, cross-domain generalization, engineering constraints, and ecological adaptation, which severely hinders large-scale cross-regional and long-term application. This paper systematically reviews coral visual recognition technologies. It summarizes underwater image acquisition, public dataset characteristics, and annotation system evolution, then compares traditional feature engineering and deep learning in key tasks, highlighting their differences in feature representation and generalization. Four core challenges are identified: class imbalance, poor underwater image quality, weak cross-device/region generalization, and mismatched algorithm metrics with ecological needs. Finally, feasible solutions based on self-supervised pre-training, domain adaptation, and multimodal fusion are discussed to enhance model robustness and ecological interpretability, providing methodological support for intelligent coral reef monitoring systems. Full article

Environmental concerns associated with the construction industry have drawn increasing attention worldwide. This study addresses the dual challenges of carbon emissions from cement production and construction waste disposal by developing and characterizing a fiber-modified geopolymer recycled aggregate porous concrete (GRAPC). An orthogonal experiment first optimized the GRAPC mix proportion (slag content = 40%, alkali modulus = 1.4, alkali content = 8%). Subsequently, the effects of coir, basalt, and steel fibers (0.25% and 0.5%) on its properties were investigated through laboratory experiments combined with scanning electron microscopy (SEM) analysis. The results show that steel fibers at 0.25% dosage enhanced compressive strength by approximately 25% due to their effective stress-bearing capacity. In contrast, 0.5% coir and basalt fibers reduced compressive strength by approximately 20.5% and 22.2%, respectively, due to low intrinsic strength and agglomeration. In addition, 0.25% coir and steel fibers increased effective porosity by 18.4% and 17.4%, respectively, owing to their uniform dispersion. All fibers promoted a more ductile-like failure mode, with coir fibers providing the best toughness improvement. This study elucidates how fiber type and dosage regulate the macro-properties and micro-mechanisms of GRAPC, providing a basis for designing sustainable eco-friendly concrete with great potential for non-primary load-bearing engineering fields. Full article

Using waste materials in the manufacture of concrete has many environmental advantages. However, it can be difficult to estimate structural performance, especially when beams are reinforced with fiber-reinforced polymers (FRP). In order to provide a data-driven approach to sustainable structural design, this work explores the use of machine learning (ML) approaches to forecast the flexural strength of FRP-strengthened waste aggregate concrete beams. A total number of 92 experimental datasets were used to develop and assess four ML algorithms: Random Forest (RF), Decision Tree (DT), Neural Network (NN), and Extreme Gradient Boosting (XGBoost). Regression plots, Taylor diagrams, statistical measures (R2R^2R2, RMSE, MAE, MSE), and explainable AI (XAI) tools, including SHAP, LIME, and partial dependence plots (PDPs), were used to evaluate the model’s performance. RF outperformed NN in terms of predictive accuracy, while XGBoost exhibited similar performance to RF. The most significant predictors, according to a SHAP analysis, were beam length and fiber length, with the lower followed by steel tensile strength, fiber width, and concrete compressive strength. LIME offered local interpretability for individual predictions, but PDPs demonstrated optimal parameter ranges and a nonlinear feature strength relationship. The findings provide engineers with a strong decision-support tool for designing green infrastructure, since they show that ensemble-based models can accurately represent the intricate, nonlinear dynamics controlling flexural behavior in sustainable FRP-strengthened waste aggregate concrete beams. Full article

Shoreline change detection from remote sensing imagery remains challenging in environments subject to water level fluctuations, as remotely sensed shoreline positions reflect instantaneous hydrodynamic states rather than true geomorphic change. In the Great Lakes, seasonal and short-term water level variations can produce apparent shoreline shifts unrelated to sediment dynamics. Reliable calibration with bathymetry and water level data can mitigate this effect, but such data are often unavailable or difficult to obtain for many coastal and lacustrine systems worldwide. To address this limitation, we proposed a morphology-based framework that quantifies geometric change between successive shoreline curves using a discrete Fréchet distance, a modified Euclidean distance and a Union distance metric. Rather than relying solely on cross-shore displacements, the approach leverages shape similarity to differentiate water-level-driven shifts from true morphological change. We evaluated the framework across three spatial scales (100 m, 500 m, and 1000 m) along 125 km of southwestern Lake Michigan coastline using 2010 and 2020 aerial imagery, benchmarking against water-level-calibrated DSAS erosion hotspots. The Fréchet distance improved monotonically with scale, achieving strong agreement at 1000 m (F1 = 0.84, Spearman $\rho$ = 0.79) but limited reliability at 100 m. While individual morphology-based metrics appeared competitive with or inferior to uncalibrated DSAS at each scale, the union of both distances substantially outperformed uncalibrated DSAS at management-relevant scales (F1 of 0.64 vs. 0.50 at 500 m and 0.79 vs. 0.42 at 1000 m), reflecting the complementary nature of shape-based and displacement-based detection. The Patient Rule Induction Method (PRIM) further identified gentle nearshore slopes and moderate separation from engineered structures as the geomorphic conditions under which the morphology-based and calibrated erosion indicators converged most closely (in-box F1 = 0.92 at 1000 m and 0.72 at 500 m). These results suggest that the proposed framework, particularly the complementary union of both metrics, provides a practical, calibration-free alternative for multiscale shoreline change screening in lacustrine and microtidal, data-limited environments, while local-scale applications still benefit from explicit water-level correction. Full article

Masonry buildings have shaped construction history since about 6500 BCE. They offer durability, strength, and cost effectiveness, especially in developing countries. Yet assessing compressive strength during construction remains challenging due to the constituent materials soil, cement, and stone, complicating standardization worldwide. In the present study, an innovative model based on a machine learning algorithm is put forth to predict the compressive strengths of prisms. Some important factors considered as input to the algorithm based on traditional methods are the brick and mortar strengths, prism geometry, mortar bed thickness, and empirically derived height-to-thickness (t) (h/t) ratios. Three different ANN algorithms are coded and trained on the input data, and they are based on the Levenberg–Marquardt algorithm, the resilient backpropagation algorithm, and the conjugate gradient algorithm. The optimal ANN model trained using the conjugate gradient Polak–Ribière algorithm (traincgp) achieves superior performance, with R² = 0.9881, R² = 0.9927, RMSE = 0.9914 MPa, MAE = 0.6039 MPa, MAPE = 20.9141%, VAF = 0.9881, and WI = 0.9970. Sensitivity analysis shows the height-to-thickness (h/t) ratio is the dominant influence on compressive strength, consistent with structural mechanics. The primary contributions are the systematically curated, richly parameterized dataset and its use to produce robust, physically interpretable predictions with established ANN methods. Full article