Search Results (651)

Underground metro tunnel failures in recent years have caused significant economic losses and posed serious risks to surface structures, highlighting the importance of accurately predicting tunnelling-induced ground deformations. Surface settlements occurring during TBM excavation may adversely affect existing infrastructure, particularly in sensitive urban areas. This study evaluates surface deformations induced by a TBM-driven metro tunnel as a case study, explicitly considering tunnel–structure interaction at locations where piled bridge piers are present. Due to site sensitivity, topographic monitoring was conducted during TBM passage, and measured settlement data were used for assessment. Settlement analyses were performed using the Peck (1969) empirical method and finite element modelling in Plaxis. Two constitutive soil models, Mohr–Coulomb (MC) and Hardening Soil (HS), were adopted to compare their predictive performance. The results show that the MC model predicts the highest surface settlements, whereas the Peck (1969) method provides results close to those obtained with the HS model, despite not explicitly incorporating structural loads. From the finite element tunnel models, it was determined—particularly from the two coordinate routes—that the HS model achieved prediction accuracy of up to approximately 95% compared to the measured values. Overall, the Peck approach and the HS model yielded more consistent predictions than the MC model for the investigated conditions, emphasizing the importance of appropriate soil model selection in finite element analyses of tunnelling-induced settlements. Full article

In structural health monitoring, purely data-driven methods for deformation prediction are often susceptible to time-varying boundary conditions under complex operating scenarios, leading to insufficient physical interpretability and limited generalization across different conditions. To address these challenges, this study proposes a Physics-Informed Dual-branch Long Short-Term Memory framework (PINN-DualSHM). The framework employs dual-branch LSTMs to separately extract temporal features of structural mechanical responses and environmental thermal effects. Dynamic decoupling and fusion of these heterogeneous features are achieved through an adaptive cross-attention mechanism. Furthermore, physical priors, including the thermodynamic superposition principle and structural settlement monotonicity, are embedded into the loss function as regularization terms, complemented by a dual uncertainty quantification system based on heteroscedastic regression and MC Dropout. Experimental results based on long-term measured data from an industrial base project in Shenzhen demonstrate that PINN-DualSHM significantly outperforms baseline models such as LSTM, CNN-LSTM, and GAT-LSTM. Specifically, the Root Mean Square Error (RMSE) is reduced by 65.25%, and the coefficient of determination (R²) reaches 0.925. Physical consistency analysis confirms that the introduction of physical constraints effectively suppresses anomalous predictive fluctuations that violate mechanical laws. Uncertainty decomposition reveals that aleatoric uncertainty is dominant (93.7%), objectively indicating that the current system’s accuracy bottleneck lies in sensor noise rather than model capability. By enhancing prediction accuracy while providing credible quantitative assessments and physical interpretability, the proposed method provides a scientific basis for the operation, maintenance optimization, and upgrading decisions of SHM systems. Full article

Accurate prediction of embankment settlement and evaluation of its serviceability in permafrost regions are significantly challenged by scarce monitoring data and dynamic, non-stationary settlement processes. To address this, an integrated framework combining change-point detection with a novel dynamic prediction model is proposed. Analysis of long-term monitoring data from the Qinghai–Tibet Railway using the Pettitt test revealed a key change point around 2015, indicating a transition towards stabilization. Subsequently, an SAA-GRU-LSTM hybrid model, employing a dynamic compensation prediction strategy, was developed. The model successfully utilized only early-stage data to forecast future settlement trends, demonstrating robust performance in adapting to the identified abrupt change. Furthermore, by applying established engineering serviceability criteria to both historical and predicted data, the framework enables a dynamic and prospective serviceability assessment. This methodology provides a practical tool for the maintenance and risk management of infrastructure in permafrost environments under conditions of data scarcity and process uncertainty. Full article

This study presents a comprehensive wildfire risk assessment framework for Rhodes Island, Greece, aimed at quantifying the impacts of climate change on hazard levels and vulnerability in a typical Mediterranean environment. The approach integrates Fire Weather Index (FWI) data, detailed fuel-type mapping, and multiple vulnerability indicators covering ecological, socioeconomic, and population factors, enabling spatially explicit estimates of current and future wildfire risk. Historically, Rhodes mostly faces moderate wildfire risk, mainly in central and northeastern regions, with localised areas of higher risk near settlements and key economic sites. Climate forecasts for 2025–2049 predict a notable increase in hazard, with areas experiencing extreme fire weather (FWI > 50) increasing from 15.19% to 66–72%, across all emission scenarios. Ecological vulnerability is particularly alarming, as 93% of the island is already highly susceptible; fire-prone forest and agricultural zones are expected to move into the highest ecological risk categories, especially in the central mountain areas. The devastating 2023 wildfire, which burned over 17,600 hectares, caused more than €5.8 million in direct damages and led to the largest evacuation in the island’s history, closely aligning with high-risk zones modelled in the framework. An important insight is the limited spatial variation in near-future risk between RCP 4.5 and RCP 8.5, indicating that significant wildfire intensification is largely unavoidable by mid-century, emphasising the urgent need for quick adaptation and risk mitigation efforts for Mediterranean critical infrastructure and communities. Full article

This paper presents a design research study that develops a comprehensive conceptual framework for an integrated digital twin system for architectural heritage. The framework aims to explore mechanisms for real-time monitoring and the coupled regulation of structural health, microclimatic conditions, and energy performance. In the context of the ongoing global warming emergency, this framework supports climate adaptation strategies for heritage sites. It enables a fully coordinated operational process encompassing real-time sensing, predictive analysis, coupled control, and decision support. In the structural dimension, the framework is designed to utilise sensors to monitor and warn against cracks, settlement, and deformation, whilst integrating models to analyse stress conditions. In the microclimate dimension, the study envisages predicting and adjusting HVAC and lighting systems based on environmental parameters and footfall monitoring data via algorithms, with the aim of balancing occupant comfort with humidity control and mould prevention. Regarding energy, the framework optimises equipment operation through smart metering and algorithms and we propose a modelling tool for the quantitative assessment of energy-saving retrofit effects. Furthermore, the framework incorporates the establishment of an open-access dataset covering structural, microclimate, and energy use data, providing data standards and a foundation for subsequent empirical research. Full article

Climate hazards increasingly disrupt schooling, revealing the limits of preparedness models that treat teachers only as implementers. This study reframes teacher empowerment as a climate-resilience capability and examines how governance arrangements enable (or constrain) hazard-ready education systems. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), searches of Web of Science, Scopus, and Google Scholar (2000–2025) identified 53 eligible studies. Across diverse hazards and settings, the evidence converges on a governance-to-capability pathway: empowerment becomes resilient performance only when the delegated decision space is matched with financed capacity (time, training, contingency resources), timely risk information and functional communication/digital infrastructure, institutionalized cross-sector coordination (education–DRR–health–protection–local government), and learning-oriented accountability (after-action review and adaptive revision rather than punitive compliance). Reported outcomes include higher preparedness quality, earlier protective action, improved learning continuity and safeguarding, and more sustainable teacher well-being/retention. Predictable failure modes include mandate–resource mismatch, accountability overload, unstable centralization–autonomy dynamics, and inequitable empowerment distribution affecting rural schools, women, and contract teachers, and disability inclusion. The evidence gaps remain pronounced for chronic hazards (especially heat and wildfire smoke), high-vulnerability contexts (fragile/conflict settings and informal settlements), and standardized measures of equity, burden distribution, governance performance, and cost-effectiveness. Policies should prioritize integrated governance packages with explicit protection and equity safeguards. Full article

In highway widening projects, the wet–dry cycling effect of weakly expansive soil fill under seasonal groundwater fluctuations exacerbates differential settlement. This study establishes a three-dimensional numerical model for a widened road with weakly expansive soil, based on a redeveloped numerical method and actual engineering projects. Through multi-scenario numerical simulations, the influence patterns and weighting factors of widening methods, road height, and water level on differential settlement were clarified. Three safety levels for differential settlement were defined using 6 cm and 12 cm as thresholds. A prediction model based on support vector machines was established to determine the combined threshold limits of key parameters under different differential settlement boundaries. The control effectiveness of sand replacement, water-blocking layers, and wicking geotextiles was comparatively evaluated: sand replacement reduces differential settlement by approximately 70% on average and is applicable to all scenarios; water-blocking layers reduce settlement by about 50% and are more suitable for bilateral widening or unilateral widening of low embankments; wicking geotextiles are unsuitable for controlling differential settlement in high-water-level areas. Selection principles for control methods under different conditions were proposed based on engineering requirements, and field tests validated the effectiveness of the proposed solutions. Full article

Current methods for predicting deep excavation deformation suffer from insufficient accuracy and limited generalization capability. Moreover, the applicability of these methods to different types of monitoring data also requires in-depth analysis. To address this, a machine learning-based prediction model, i.e., the VMD-GWO-CNN model, integrating Variational Mode Decomposition (VMD), the Grey Wolf Optimizer (GWO), and the Convolutional Neural Network (CNN), is proposed to predict various types of monitoring data. The GWO algorithm optimizes both the key parameters of VMD and the hyperparameters of the CNN. The optimized CNN model predicts each subsequence decomposed by VMD, and the final prediction is obtained by superimposing these results. Furthermore, the prediction performance of the proposed model is evaluated against the LSTM, CNN, and GWO-CNN models using four metrics (RMSE, MAE, MAPE, R²). The results indicate that all four algorithms possess effective predictive capability for the monitoring data, in which the VMD-GWO-CNN model demonstrates the best performance across all metrics. Specifically, its RMSE for surface settlement prediction is reduced by 59.2%, 34.1%, and 33.0% compared to the LSTM, CNN, and GWO-CNN models, respectively. Moreover, the VMD-GWO-CNN model exhibits strong predictive performance for deformation in slope engineering and subgrade engineering, demonstrating its good applicability across different geotechnical engineering. The findings provide a scientific basis for safe excavation construction and contribute to efficient and rapid execution of foundation pit projects. Full article

The load transfer method is important for the settlement prediction of axially loaded piles, but in multi-layered complex soils, it lacks analytical solutions. Traditional numerical methods such as the finite element method suffer from strong dependence on mesh generation, time-consuming iterative calculations, and high computational costs for back-analysis. This paper proposes a load transfer analysis model based on a Domain Decomposition Physics-Informed Neural Network. A multi-subnet parallel architecture is adopted to simulate multi-layered soils, solving the problem of inter-layer stress–strain discontinuity through interface coupling and gradient continuity constraints; a non-dimensionalization system and a hard constraint mechanism are introduced to enhance training efficiency and physical consistency; and a two-stage analysis framework comprising surrogate model forward analysis and field data inversion is established. Numerical experimental results indicate that the forward analysis of this model is in high agreement with FEM simulation results, and computational efficiency is improved by six orders of magnitude; based on a small amount of field static load test data, multi-layer soil parameters are accurately inverted, achieving more precise pile settlement prediction than FEM. Comparative analysis validates the effectiveness of the domain decomposition multi-subnet over a single network, demonstrating extensibility to hyperbolic and exponential multi-soil constitutive models. Full article

With rapid economic development in China’s coastal regions, more oil stations are being built on soft soil foundations, facing risks such as foundation settlement and pipeline failures. Mechanical vibrations of oil pumps can induce resonance in pipelines, leading to rupture, leakage, and fire or explosion, threatening both safety and sustainable operation. Traditional monitoring methods, relying on physical models or data-driven approaches alone, are limited in capturing these coupled risks. This study proposes an ABC-XGBoost hybrid risk warning model, where the artificial bee colony algorithm optimizes XGBoost hyperparameters (iteration number, tree depth, learning rate) to improve predictive accuracy. By using multidimensional data—such as internal pressure, vibration amplitude, and ground settlement—the model evaluates stress and resonance risks in real time, supporting sustainable safety management. Validation with real station data shows an accuracy of 95.22%, 2.61% higher than the unoptimized model, demonstrating effective early warning and contribution to sustainable pipeline operation. Full article

The identification of potential landslides and targeted risk analysis is crucial for the warning and prevention of geological landslide disasters. This article presents a time series deformation prediction framework based on a Long Short-Term Memory (LSTM) network deep learning model for analyzing Interferometric Synthetic Aperture Radar (InSAR) data. By employing an advanced stacked LSTM network model, we effectively capture temporal dependencies and move beyond traditional methods that depend on explicit deformation. This approach enables short- to medium-term deformation prediction through structured time dynamic modeling, identifies potential landslide targets in the high-altitude regions upstream of Lake Sarez, and classifies associated risk levels. The results indicate that: (1) In short-term forecasting, the stacked LSTM model effectively captures trend turning points, producing stable and reliable predictions with a Mean Absolute Error (MAE) of 0.164 mm and a Root Mean Square Error (RMSE) of 0.194 mm; (2) From 2019 to 2022, regional surface deformation characteristics exhibited significant spatial heterogeneity, with the potential landslide on the right bank identified as the most critical settlement center, demonstrating a line of sight (LOS) deformation rate consistently exceeding 49 mm per year, while the Usoi Dam displayed relatively good stability during this period; (3) By integrating InSAR deformation rate maps with Sentinel-2 optical images, we identified a total of 72 potential landslide targets in the region, four of which exhibited deformation rates exceeding −30 mm per year, indicating significant activity and classifying them as high-risk areas requiring attention. This provides a targeted reference list for the prevention and control of geological landslides around Lake Sarez and establishes a reliable technical pathway for the early identification of landslides under complex geological conditions in high-altitude mountainous areas. Full article

In the 20th century, several large-magnitude earthquakes (M > 7.0) occurred in Greece and surrounding areas, some of which caused extensive structural damage and significant loss of life. Unfortunately, for these earthquakes, there was no recorded ground motion intensity data to extract information about the macroseismic intensity distribution within the affected areas. A characteristic example of such an earthquake is the M7.2 of 12 August 1953 on Cephalonia island, which led to the almost complete destruction of settlements across the Cephalonia, Zakynthos, and Ithaca islands in western Greece. Although the vulnerability of the buildings affected in 1953 substantially differs from modern structures, the intensity and spatial extent of the shaking indicate that an event of similar magnitude could, even today, place the built environment and critical infrastructure of the region at high seismic risk. This study aims to estimate peak ground acceleration and velocity (PGA–PGV) and macroseismic intensity for the Cephalonia, Zakynthos, and Ithaca islands associated with earthquake scenarios comparable to the 1953 event (M7.2), incorporating seismotectonic information about active faults linked to the historical earthquake and considering associated uncertainties. Ground motion prediction models recently developed for Greece are employed. High PGA values (0.41–0.44 g) are estimated for the M7.2 earthquake, for rock site conditions (Vs30 $\ge$ 790 m/s), covering almost the entire island of Cephalonia; these can be considered as the minimum values expected on rock site conditions for a similar earthquake scenario. Full article

Optical and passive remote sensing-based estimation of aboveground biomass (AGB) using forest structural stratification has shown improvements over global models. This study investigated whether stratification by human-mediated disturbances improves prediction accuracy. Disturbance variables included proximity to mines, roads, and settlements, evaluated across three regimes: the full Atewa landscape (“FSR”), the Atewa Range Forest Reserve (“FR”), and the surrounding disturbed area (“SR”). Predictor selection for regimes was performed using recursive feature elimination with cross-validation, applied to random forest (RF) and support vector machine (SVM) algorithms. AGB was then estimated using local, global, and retuned global models, and the results were compared using the coefficient of determination (r²) and root mean square error (RMSE). The global RF model achieved the best performance (r² = 0.54; RMSE = 57.71 Mg/ha), likely due to structured heterogeneity captured across combined regimes. The “SR” models, however, performed poorly, indicating that excessive unstructured heterogeneity introduces noise and redundancy that weaken predictions. The low performance of the “FR” regime was attributed to spectral saturation and limited variance in observed AGB. Although disturbance factors added minimal bias, heteroscedasticity was evident in the “SR” and “FSR” regimes. Overall, this study indicates that disturbance-based stratification may not necessarily improve AGB estimation accurately compared to global models. However, it highlights the value of disturbance information for AGB modeling in heterogeneous forest landscapes. Full article

Urbanisation is a major driver of ecological change, altering the composition and functioning of ecosystems through land use conversion, pollution, and environmental fragmentation. Although some authors reported that air pollutants could be absorbed and detoxified by the endophytic microbiome of urban trees, the specific mechanisms by which urban air pollution shapes the endophytic microbiome and, consequently, the trees’ capacity for pollutant degradation, remain largely unexplored. The aim of the present study was to: (1) analyse the structure of endophytic bacteriome of the phyllosphere of three widely planted ornamental tree species—Tilia tomentosa, Fraxinus excelsior, and Pinus nigra, growing at four locations within the city of Plovdiv, Bulgaria, with different anthropogenic load; and (2) assess the effects of host species and urban environmental exposure on bacteriome diversity, taxonomic composition, and functional capacity. Functional profiling based on 16S rRNA gene sequencing revealed enrichment of the metabolic pathways associated with nitrogen cycling, carbon metabolism, and hydrocarbon degradation, particularly in samples originating from more urbanised or polluted locations. These predicted functional traits suggest that endophytic bacteria may actively contribute to detoxification processes within plant tissues. Tilia tomentosa and Fraxinus excelsior were enriched in nitrogen and carbon cycling pathways, including denitrification, methanol oxidation, and methanotrophy—functions associated with oxidative stress mitigation and nutrient regulation. In contrast, Pinus nigra showed higher relative abundance of chemoheterotrophy, ureolysis, and sulphur respiration, indicating a more conservative and stress-tolerant microbiome. Although the study involved only one settlement, these results suggest that endophytic communities may contribute to urban tree sustainability by supporting ecosystem functions under stress conditions. By integrating microbial ecology with urban environmental assessment, this research provides new insights into the adaptive potential of endophytic microbiota in urban forests and highlights their importance in the sustainable management of green infrastructure through microbiome-informed strategies. Full article

The advancement of large-scale marine infrastructure demands increasingly accurate prediction of settlement in deep-water foundations. The caisson is an important type of deep-water foundation whose additional settlement induced by superstructure construction directly impacts the overall safety of the project. This study focuses on the main tower foundation of the Changtai Yangtze River Bridge, recognized as the world’s largest deep-water caisson foundation. A three-dimensional finite element model was developed using the hardening soil model with small-strain stiffness (HSS) constitutive model to simulate the settlement response of the caisson foundation throughout the entire superstructure construction process. The model’s reliability was verified through systematic comparison with field monitoring data. Furthermore, an inversion analysis was conducted on the initial shear modulus ($G_0^{\mathrm{ref}}$), the most sensitive parameter of the HSS model, based on the measured data. The results reveal that its optimal value exhibits significant load dependency, varying according to the construction stage. Accordingly, practical strategies for parameter determination are proposed: a fixed-value method ($G_0^{\mathrm{ref}}$ = 2$E_{\mathrm{ur}}^{\mathrm{ref}}$) suitable for conventional design and a more precise stage-specific value method. Both approaches markedly enhance the settlement prediction accuracy, particularly under high-load conditions. The findings offer valuable insights for the refined design and safety assessment of similar deep-water mega-foundation projects. Full article