Offshore wind turbines (OWTs) empoly various foundation types, among which Jacket-type offshore wind turbines (JOWTs) are often used in shallow waters with challenging soil conditions due to their lattice framework foundations and multiple anchoring points. However, prolonged exposure to harsh marine environments (e.g. storms) and age-related degradation issues like corrosion, fatigue cracking, and mechanical damage increases failure risks. To address these issues, this paper introduces a Digital Healthcare Engineering (DHE) framework, which provides a proactive strategy for enhancing the safety and sustainability of JOWTs: (1) Real-time health monitoring using IoT; (2) Data transmission via advanced communication technologies; (3) Analytics and simulations using digital twins; (4) AI-powered diagnostics and recommendations; as well as (5) Predictive analysis for maintenance planning. The paper reviews recent technological advances that support each DHE module, assesses the framework's feasibility. Additionally, a prototype DHE system is proposed to enable continuous, early fault detection, and health assessment.

Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maintenance costs. However, under complex external conditions and varying structural parameters, existing methods struggle to accurately and quickly detect damage. Understanding the factors that influence structural health is critical for effective long-term monitoring, as these factors directly affect the accuracy and timeliness of damage identification. This study comprehensively analyzed 5 MW offshore wind turbine measurement data, including constructing a digital twin model, establishing a surrogate model, and performing a sensitivity analysis. For monopile-based turbines, sensors in x and y directions were installed at four heights on the pile foundation and tower. Via Bayesian optimization, the finite element model's structural parameters were updated to align its modal parameters with sensor data analysis results. The update efficiencies of different objective functions and the impacts of neural network hyperparameters on the surrogate model were examined. The sensitivity of the turbine's structural parameters to modal parameters was studied. The results showed that the modal flexibility matrix is more effective in iteration. A 128-neuron, double-hidden-layer neural network balanced computational efficiency and accuracy well in the surrogate model for modal analysis. Flange damage and soil degradation near the pile mainly impacted the turbine's health.

This research proposes an artificial intelligence (AI)-powered digital twin framework for highway slope stability risk monitoring and prediction. For highway slope stability, a digital twin replicates the geological and structural conditions of highway slopes while continuously integrating real-time monitoring data to refine and enhance slope modeling. The framework employs instance segmentation and a random forest model to identify embankments and slopes with high landslide susceptibility scores. Additionally, artificial neural network (ANN) models are trained on historical drilling data to predict 3D subsurface soil type point clouds and groundwater depth maps. The USCS soil classification-based machine learning model achieved an accuracy score of 0.8, calculated by dividing the number of correct soil class predictions by the total number of predictions. The groundwater depth regression model achieved an RMSE of 2.32. These predicted values are integrated as input parameters for seepage and slope stability analyses, ultimately calculating the factor of safety (FoS) under predicted rainfall infiltration scenarios. The proposed methodology automates the identification of embankments and slopes using sub-meter resolution Light Detection and Ranging (LiDAR)-derived digital elevation models (DEMs) and generates critical soil properties and pore water pressure data for slope stability analysis. This enables the provision of early warnings for potential slope failures, facilitating timely interventions and risk mitigation.

This article describes the process of developing a digital twin aimed at being used for the predictive maintenance of scour on the Espartxo Bridge in San Sebastian, which is continuously monitored in real time. The main objectives of the research are to describe a methodology for creating the digital twin and to establish an effective strategy for the early identification of scour in bridge foundations, which pose a significant risk of structural damage. The developed methodology is based on hydraulic, geotechnical, and structural analyses, combining experimental tests on the bridge with the development of numerical models. As a result of the research, a biunivocal correlation is established between the variation of a specific frequency and the depth of scour, and the analysis of horizontal vibration modes is determined to be an effective strategy for detecting scour.

The accurate assessment and effective management of deep excavation risk have faced longstanding challenges due to the highly complicated and uncertain construction process. A digital twin, designed with the datamechanism-fused (DMF) physical and virtual models, is developed to solve problems by integrating Building Information Modeling (BIM), data mining (DM), and physical mechanisms. In the DMF physical model, a mechanical model is embedded into the digital twin to implement real-time interaction and inversion between fieldmeasured and simulated data, thus revealing the evolution law of mechanical properties and creating a multisource DMF database. In the virtual model, the random forest (RF) regression is applied to fully learn the multisource database and accurately predict retaining wall behaviors on behalf of excavation risk. The proposed digital twin facilitates practical applications to imitate physical construction process, predict excavation-induced behavior, and realize closed-loop risk management with a high degree of automation, intelligence, and reliability.

Economic and human losses from flooding have had a significant global impact. Undeveloped nations often require extended periods to recover from flood-related damage, exacerbating the climate poverty trap, specifically in flood-prone regions. To address this issue, early warning systems (EWS) provide lead time for preparedness and measures to reduce vulnerability. However, EWS are mainly empirical at large scales and often do not incorporate hydrodynamic behaviors in flood forecasting, at least in developing regions with a lack of information. This study presents an open-source system integrating a hydrodynamic model with satellite rainfall data (PERSIANN PDIR-Now) and weather prediction data (GFS). It functions as a near real-time Digital Twin (DT) and Early Warning System for high-resolution flood forecasting. Simulated data can be compared with gauge stations in real-time through the model monitoring interface. A proof-of-concept was made by assessing the model capabilities in two case studies. First, the system simulated two consecutive extreme events (hurricanes ETA and IOTA) over the Sula Valley, Honduras, showing fidelity in streamflow responses. Second, the system worked as a DT and EWS to monitor the current and future hydrological states for two periods in 2022 and 2023. Results indicate that satellite data coupled with DT can provide up-to-date system conditions for flood forecasts for regions of lack of data for extreme rainfall events. This tool offered insights to enhance civil protection and societal engagement through warning dissemination against extreme events to build resilience to cope with the increasing magnitude and frequency of disasters in regions with data scarcity.

Development of digital twins is emerging rapidly in geotechnical engineering, and it often requires real-time updating of numerical models (e.g., finite element model) using multiple sources of monitoring data (e.g., settlement and pore water pressure data). Conventional model updating, or calibration, often involves repeated executions of the numerical model, using monitoring data from a specific source or at limited spatial locations only. This leads to a critical research need of real-time model updating and predictions using a numerical model improved continuously by multi-source monitoring data. To address this need, a physics-informed machine learning method called multi-source sparse dictionary learning (MS-SDL) is proposed in this study. Originated from signal decomposition and compression, MS-SDL utilizes results from a suite of numerical models as basis functions, or dictionary atoms, and employs multi-source monitoring data to select a limited number of important atoms for predicting multiple, spatiotemporally varying geotechnical responses. As monitoring data are collected sequentially, no repeated evaluations of computational numerical models are needed, and an automatic and real-time model calibration is achieved for continuously improving model predictions. A real project in Hong Kong is presented to illustrate the proposed approach. Effect of monitoring data from different sources is also investigated.

The volume of Earth system observations has grown massively in recent decades. However, multivariate or multisource analyses at the interface of atmosphere and land are still hampered by the sparsity of ground measurements and the abundance of missing values in satellite observations. This can hinder robust multivariate analysis and introduce biases in trends. Nevertheless, gap-filling is often done univariately, which can obscure physical dependencies. Here, we apply the new multivariate gap-filling framework CLIMate data gapFILL (CLIMFILL). CLIMFILL combines state-of-the-art spatial interpolation with an iterative approach accounting for dependencies across multiple incomplete variables. CLIMFILL is applied to a set of remotely sensed and in situ observations over land that are central to observing land-atmosphere interactions and extreme events. The resulting gridded monthly time series covers 1995-2020 globally with gap-free maps of nine variables: surface layer soil moisture from European Space Agency (ESA)-Climate Change Initiative (CCI), land surface temperature and diurnal temperature range from Moderate-resolution Imaging Spectroradiometer, precipitation from GPM, terrestrial water storage from GRACE, ESA-CCI burned area, and snow cover fraction as well as 2-m temperature and precipitation from CRU. Time series of anomalies are reconstructed better compared to state-of-the-art interpolation. The gap-filled data set shows high correlations with ERA5-Land, and soil moisture estimates compare favorably to in situ observations from the International Soil Moisture Network. Soil moisture drying trends in ESA-CCI only agree with the reanalysis product ERA5-Land trends after gap-filling. We furthermore showcase that key features of droughts and heatwaves in major fire seasons are well represented. The data set can serve as a step toward the fusion of multivariate multisource observations.