Digital Twin Construction (DTC) is a data-centric mode of construction that leverages digital twin technologies to maintain a continuously updated representation of a project and to enable short cycle Plan Do Check Act (PDCA) planning and control with continuous feedback and improvement. Industrial implementations reported to date have been too narrow in scope and too few in number to provide comprehensive proof of feasibility and empirical evidence of impacts. We address these gaps using a laboratory setup that implements a complete DTC PDCA workflow for a precast residential project. The experimental DTC system stores project intent, monitors and captures current status, and supports human in the loop replanning or automated optimization across factory production, logistics, and erection on site for a 1:25 scale model building. Validation through numerous full construction project runs demonstrates consistent end-to-end operation and practical usability. The testbed itself provides a reference architecture for DTC systems and a platform for controlled and replicable experiments that provide comparable quantitative evidence on DTC impacts under varied levels of automation.

Recent advances in learning dynamical systems from data have shown significant promise. However, many existing methods assume access to the full state of the system—an assumption that is rarely satisfied in practice, where systems are typically monitored through a limited number of sensors, leading to partial observability. To address this challenge, we draw inspiration from the Mori–Zwanzig formalism, which provides a theoretical connection between hidden variables and memory terms. Motivated by this perspective, we introduce a constant-lag neural delay differential equations (NDDEs) framework, providing a continuous-time approach for learning non-Markovian dynamics directly from data. These memory effects are captured using a finite set of time delays, which are identified via the adjoint method. We validate the proposed approach on a range of datasets, including synthetic systems, chaotic dynamics, and experimental measurements, such as the Kuramoto–Sivashinsky equation and cavity-flow experiments. Results demonstrate that NDDEs compare favorably with existing approaches for partially observed systems, including long short-term memory (LSTM) networks and augmented neural ordinary differential equations (ANODEs). Overall, NDDEs offer a principled and data-efficient framework for modeling non-Markovian dynamics under partial observability. An open-source implementation accompanies this article.

While Structural Health Monitoring (SHM) has been widely studied, its reliability in real-world applications remains challenged by pronounced operational variability, particularly when system behavior changes discretely between operating regimes. Such methods generally rely on baseline comparisons; however, under multiple operating regimes, the baseline becomes distributed across several distinct regions, each associated with a specific regime. This multi-baseline behavior complicates anomaly detectability, as variations induced by changing operating conditions may mask the subtle changes caused by structural degradation. The challenge is particularly pronounced for systems exhibiting regime-dependent behavior, where transitions between approximately stationary conditions occur frequently and are difficult to isolate. To address this, an efficient probabilistic multi-model framework is proposed, in which each operating regime is represented by a locally Vector Autoregressive (VAR) model. A Bayesian formulation is adopted to account for parameter uncertainty explicitly and to enable sequential updating, allowing the regime models to adapt as additional data become available—an important feature for early-stage SHM. New observations are evaluated against the ensemble of regime-specific VAR models using the marginal likelihood, enabling assessment of statistical consistency with the learned reference behavior. Persistently low consistency is interpreted as indicative of anomalies, which may reflect structural changes or evolving degradation. The proposed method is demonstrated using vibration data from gearboxes onboard a Crew Transfer Vessel operating under multiple regimes. Despite the limitations and uncertainties inherent to early-phase SHM, the framework successfully identifies deviations from the learned reference behavior within consistent operating conditions, demonstrating its potential for SHM under realistic, time-varying operation.

Currently, engineering research is highly data-oriented. Probabilistic methods to uncover patterns in noisy data are an essential part of this trend. The aim of this study was to demonstrate two of those concepts to the field of elastomer research and thermal aging: statistical hypothesis tests of experimental data and model selection. The two concepts are used to quantitatively assess whether the fracture stress of the rubber of interest only degrades due to short-term thermal oxidative aging, or if the fracture stress also improves for some aging temperatures. First, model-free statistical hypothesis tests are directly applied to fracture stress data. These tests have the advantage that no aging models are required, and hence, their results are unaffected by modeling choices. Their disadvantage is that not all experimental data can be considered. To circumvent this problem, a number of aging models are subjected to model selection to investigate whether growth of the fracture stress should be included in the aging model, or only degradation. However, model selection is also not perfect, because it only compares the models proposed by the practitioner. Because each method overcomes the shortcomings of the other, the combined application of the two methods makes a strong investigative tool that can be applied to any property affected by any physical phenomenon, for example, crack length due to ozone attack, modulus change due to thermal aging, and surface embrittlement due to photo-oxidation.

Physics-informed neural networks (PINNs) are a promising alternative for extracting additional time-averaged (mean) flow quantities from experimental data. In the case of particle image velocimetry (PIV), for example, the measured mean flow field is contaminated by noise, has a limited field of view, is restricted to a uniform grid, and does not provide the pressure field. To overcome these limitations, we present a methodology in which PINNs are first trained on a Reynolds-averaged Navier–Stokes (RANS) simulation such that it learns all states at every location in the domain. We then apply transfer learning, which updates the PINN using sub-sampled PIV data. The resulting predictions are in significantly better agreement with the full PIV dataset than PINNs, which are trained on experimental data only. This work builds on the recent literature by integrating a Spalart-Allmaras turbulence model and applying hard constraints to the no-slip wall boundary condition. We apply this methodology to a two-dimensional NACA 0012 airfoil inclined at an angle of attack, $ \alpha $ = 15°, for two Reynolds numbers of Re = 10,000 and 75,000. The proposed methodology is initially validated using large eddy simulation (LES) data and then demonstrated on experimental PIV data. Our transfer learning approach results in improved predictions and a reduction in training time when compared to using a random network initialization.

The paper presents a framework to automatically identify crack patterns and the related features in existing reinforced concrete (RC) bridges. The challenge of this work is to define a tool for detecting the focused defect and highlighting the number and the orientation of cracks, allowing for correct interpretation and driving further evaluations on the residual life of the structure. The study is framed within the increasing interest in monitoring the structural health of existing bridges through automated tools, able to support engineers in the phase of visual assessment and interpretation of structural defects. When dealing with periodic inspection of large bridge portfolios, the support provided by automated tools can be fundamental for planning further strategies aimed at ensuring the structural safety and preventing future disasters. Given a stack of photos of a bridge structural element, an image stitching procedure is proposed to produce a near-complete image of the entire element. On the latter, a pipeline of deep-learning (DL) algorithms is employed to automatically detect and identify cracks (as a combination of object detection and segmentation algorithms). Finally, the proposed tool extracts cracks for counting and defines their orientation (i.e., vertical, horizontal, diagonal), in order to provide near-complete information about the crack pattern for the structural element. A full description of the methodology and the proposed algorithms is reported throughout the manuscript, showing the main pros and cons and assessing the effectiveness of the tool on a real-life case study.

A robot swarm executing a formation task is examined for the presence of anomalies in the robots’ motion behavior using a data-driven contextual anomaly-detection method. The detection of anomalies is particularly relevant for the employed graph-based formation control approach, as the motions of robots influence the behavior of other formation members. With the swarm using a graph-based formation control approach, robots are categorized as normal or anomalous based on an evaluation of the likelihoods of the motions performed by a robot. These likelihoods are estimated by a neural network that was trained on simulated data of normal robot behavior, and anomalous behavior is considered to be a deviation from these learned, normal robot motions. Consequently, the detection method is not restricted to known instances of anomalous behavior and can be tuned to an acceptable maximum false-positive rate on normal robot behavior. With the goal of evaluating the performance of the anomaly-detection method, three different types of anomalous behaviors are designed, and a test dataset is created by simulating the execution of different formation tasks in the presence of one anomalously behaving robot. During the evaluation, the anomalous behavior is correctly detected for more than 80% of the motions performed by the robots. An additional, fourth motion anomaly was simulated and investigated to determine the limits and robustness of the method’s applicability when considering an extrapolation of the method to out-of-context formation scenarios.

Accurate and predictive scale-resolving simulations of laser-ignited rocket engines are highly time-consuming because the problem includes turbulent fuel–oxidizer mixing dynamics, laser-induced energy deposition, and high-speed flame growth. This is conflated with the large design space primarily corresponding to the laser operating conditions and target location. To enable rapid exploration and uncertainty quantification, we propose a data-driven surrogate modeling approach that combines convolutional autoencoders (cAEs) with neural ordinary differential equations (neural ODEs). The present target application of an machine learning-based surrogate model to leading-edge multiphysics turbulence simulation is part of a paradigm shift in the deployment of surrogate models toward increasing real-world complexity. Sequentially, the cAE spatially compresses high-dimensional flow fields into a low-dimensional latent space, wherein the system’s temporal dynamics are learned via neural ODEs. Once trained, the model generates fast spatiotemporal predictions from initial conditions and specified operating inputs. By learning a surrogate to replace the entirety of the time-evolving simulation, the cost of predicting an ignition trial is reduced by several orders of magnitude, allowing efficient exploration of the input parameter space. Further, as the current framework yields a spatiotemporal field prediction, appraisal of the model output’s physical grounding is more tractable. This approach marks a significant step toward real-time digital twins for laser-ignited rocket combustors and represents surrogate modeling in a complex system context.

The cost of high-quality aerodynamics simulations for realistic automotive configurations makes comprehensive design studies unfeasible. Data-driven surrogates (learning from data) are an appealing alternative, and there is no shortage of approaches that target shape-to-aerodynamics predictions. However, there is a fundamental limitation (data insufficiency problem) in this context: owing to the proprietary nature of commercial automotive designs, training datasets are limited to a few freely-available geometries. In a previous work the authors, a strategy to construct datasets for training surrogates was introduced. It enables controlled generation of an arbitrary number of samples, by convex interpolation between a small number of basis geometries. In this work, we extend this strategy by introducing three features, namely size, density, and diversity that characterize more general datasets. These are important to assess how useful is a dataset for a specific prediction task (data for learning). A formal measure of diversity is developed and then, datasets of successively increasing diversity but constant size are constructed. We show that the dataset diversity has an impact on the predictive accuracy of machine learning surrogates. A power-law scaling, $ \varepsilon \hskip0.5em \propto \hskip0.5em {M}^{1/2}\hskip0.1em {m}^{-1/6} $, where $ \varepsilon $ is the prediction error, $ M $ is the diversity, and $ m $ the dataset size, collapses 23 controlled experiments onto a single curve, revealing that diversity dominates size in determining prediction error. The proposed framework allows for more rigorous a priori evaluation of models than is currently possible and can be applied readily to other shape optimization problems.

In Fluid Mechanics, developing deep learning models for relevant engineering configurations using exclusively high-fidelity data is often impractical due to the large computational cost of the corresponding simulations. We introduce a Machine Learning procedure that combines three algorithms to construct a multi-fidelity autoencoder capable of providing real-time high-fidelity predictions with quantified uncertainties while substantially reducing the computational cost of database construction. We demonstrate the method on a problem of practical interest: the prediction of jet flow fields emanating from a family of parametric nozzle configurations. The proposed multi-fidelity dataset comprises a limited number of computationally expensive Large Eddy Simulations augmented by a larger ensemble of computationally cheap Reynolds-Averaged Navier–Stokes simulations. The trained model learned the underlying relation between nozzle geometry and jet properties from low-fidelity data, and used high-fidelity ones to construct a correction term from low to high fidelity, thus providing a high-fidelity reduced-order model in the full design space.

The optimal design of products is crucial in the manufacturing industry due to its significant impact on economic performance, resource efficiency, and environmental sustainability. This study addresses a critical challenge in tire manufacturing: determining optimal extrusion initializations to improve production efficiency and product quality. To tackle this problem, the research explores the use of Digital Twins, a technology that facilitates the simulation of various scenarios without working directly with the physical system. Specifically, a data-driven Digital Twin approach is employed, which has shown promising computational efficiency in similar contexts. Despite its potential, several challenges associated with Digital Twins have been identified in the literature, including issues related to human–machine interaction, model adaptability to dynamic changes, data quality, and overall precision. This study advances Digital Twin technology by developing a framework that enables real-time identification and control of crucial physical parameters. The proposed approach emphasizes real-time computation, seamless process integration, adaptability to production demands, and user-friendly interaction for operators. This approach, according to our results, could avoid the 99% failed extrusions and could decrease 82% the time required to stabilize product quality. These outcomes demonstrate the practicality and effectiveness of the Digital Twin in improving manufacturing operations. The study also underscores the importance of incorporating human-centric design principles, ensuring that the technology not only enhances operational efficiency but also aligns with the needs of the operators. Overall, the research highlights the significant advancements and practical benefits of applying Digital Twin technology in tire manufacturing.

Three data-driven assimilation algorithms are compared in the flow around a surface mounted prism at $ \mathit{\operatorname{Re}}=1000 $. All algorithms employ proper orthogonal decomposition (POD) for dimensionality reduction and synthesize linear estimators (Kalman filters). Two estimators are new and exploit past historical patterns of the training data. They are constructed from the singular vectors of the time-delayed Hankel matrix assembled from the signals of the POD coefficients. The two estimators differ on the forcing term of the linear system and its statistics. They can reconstruct (and also forecast) the instantaneous velocity field from sparse data. The third algorithm is based on the n4sid system identification method. The performance of the algorithms is compared with respect to the number of sensor points, model order, computational time for synthesis, and accuracy at reproducing the statistics of the flow (Reynolds stresses) at design and off-design conditions. For all criteria, it is found that the two new estimators were more robust and accurate compared to n4sid. Between the two, the estimator with random noise forcing was slightly more accurate. The future evolution of the flow was also accurately forecast over long time horizons using only one sensor. Application of the model trained at the reference Reynolds number to an off-design condition (at the nearby $ \mathit{\operatorname{Re}}=800 $) showed that very satisfactory results can be obtained with only very few sensors. The new estimators are scalable, easy to construct, and physically interpretable and can provide flow reconstruction (and, if needed, forecasting over a rolling time window) in almost real time.

This work shows how adaptivity can enhance the value realization of digital twins (DTs) in civil engineering. We focus on adapting the state transition models within DTs represented through probabilistic graphical models. The bi-directional interaction between the physical and virtual domains is modeled using dynamic Bayesian networks. By treating state transition probabilities as random variables endowed with conjugate priors, we enable hierarchical online learning of transition dynamics from one state to another through effortless Bayesian updates. We provide the mathematical framework to account for a larger class of distributions with respect to the current literature on DTs. To compute dynamic policies with precision updates, we solve parametric Markov decision processes through reinforcement learning. The proposed adaptive DT framework enjoys enhanced personalization, increased robustness, and improved cost-effectiveness. We assess our approach on a case study involving structural health monitoring and maintenance planning of a railway bridge.

This study develops a robust framework for estimating the compressive strength of self-compacting concrete (SCC) incorporating recycled aggregates using supervised machine learning (ML) techniques. A comprehensive experimental database comprising 582 concrete mix designs was used, encompassing diverse input variables including binder content, water, coarse and fine aggregates, recycled aggregate proportion, superplasticizer dosage, and curing time. Seven ML algorithms—XGBoost, CatBoost, AdaBoost, Extra Trees, Bagging Regressor, K-Nearest Neighbors, and Radius Neighbors—were systematically trained using a stratified 70/15/15 data split and optimized via grid search with five-fold cross-validation. Model performance was evaluated using coefficient of determination (R2), root mean squared error, and MAE across training, validation, and testing datasets. Among all models, XGBoost demonstrated the highest accuracy, achieving an average R2 of 0.9799, RMSE of 2.87 MPa, and mean absolute error of 1.97 MPa. The Permutation Feature Importance analysis revealed that binder content, water, and coarse aggregate were the most influential predictors of strength. This study confirms that ensemble ML models, particularly XGBoost, can reliably predict the compressive strength of SCC with recycled aggregates, while offering transparent insights into material behavior. The results provide a valuable tool for sustainable mix design optimization and practical implementation in eco-efficient concrete construction.

This study presents a scene-based clustering methodology for underwater structural health monitoring (SHM), categorizing images by intrinsic scene attributes (visibility, color, and turbidity). Shifting focus from object detection to holistic scene understanding enhances automated condition assessment in underwater infrastructure inspection by prefiltering suboptimal images and optimizing downstream tasks such as image matching and alignment. Ensuring algorithms operate on images with consistent lighting, texture, and clarity reduces ambiguities and enhances feature correspondence. It integrates local and global feature extraction, correlation analysis, feature selection, dimensionality reduction, and outlier detection to improve clustering accuracy and pipeline robustness. Clustering uses hierarchical and centroid-based algorithms, evaluated by Silhouette Score, Calinski-Harabasz Index, F1-score, V-measure, Adjusted Mutual Information, and Adjusted Rand Index, applying the Hungarian algorithm for label mapping in pseudo-labeled cases. The methodology is validated on real and synthetic underwater datasets, including a bridge inspection video dataset with abovewater and underwater segments, and controlled laboratory experiments assessing turbidity effects on submerged structural elements. A key contribution is assessing feature matching performance across clustered and unclustered image sets, demonstrating that scene-based clustering significantly enhances keypoint detection, feature correspondence, and inlier ratios while reducing mismatches from illumination and viewpoint variations. Additionally, engineered features outperform pretrained deep learning features in this unsupervised setting, providing more reliable global scene representation and improved clustering and feature matching outcomes. These findings establish the proposed pipeline as a valuable tool for underwater SHM, supporting applications such as 3D reconstruction, infrastructure assessment, and autonomous underwater inspections.

Heart rate is one of the most commonly used metrics for human health. Measuring human heart rate typically involves direct body contact, whether from wearable devices or manual measurement. However, such devices can cause discomfort in some patients, particularly in sensitive populations where such monitoring is more critical. Other past work for noncontact or remote heart rate measurement (e.g., camera- or radio-based methods) is often limited by line-of-sight and privacy requirements, which inhibit application in real-world environments. This article presents FloorPulse, an indirect heart rate monitoring system for human beings using heartbeat-induced floor vibrations. The key insight is that the human body generates a small wave of pressure and sound with each heartbeat. These waves are propagated as vibration through the structures the person is in contact with (e.g., a chair), then into and through the floor. FloorPulse detects and interprets these small floor vibrations to monitor the person’s heart rate. We developed a highly sensitive vibration sensing system and heartbeat pattern modeling to identify these tiny vibrations among other body motions and ambient noise. We evaluated FloorPulse in a real home environment, demonstrating an average heart rate error similar to medical device standards on the floor near the participants’ chair, and on the order of 10 beats per minute (bpm) on the floor 2 m away from the subject.

This work presents an end-to-end strategy for solving inverse problems constrained by Partial Differential Equations within a fully differentiable Machine Learning framework. The proposed formulation provides a unified and user-friendly methodology applicable to a wide range of problems, from data assimilation to closure modeling. Our approach combines a baseline differentiable PDE solver, which predicts the state $ \boldsymbol{w} $ from the nonlinear system $ \boldsymbol{R}\left(\boldsymbol{w}\right)=0 $, with a generic additive, parametrized, and differentiable correction $ {\boldsymbol{f}}_{\boldsymbol{\vartheta}}\left(\boldsymbol{w}\right) $, with trainable parameters $ \boldsymbol{\vartheta} $. We show how to optimize $ \boldsymbol{\vartheta} $ within a fully differentiable Python workflow by reformulating the PDE as an implicit layer, enabling its integration into arbitrary objective functions, while leveraging PyTorch’s automatic differentiation graph. The method is demonstrated on the Reynolds-averaged Navier–Stokes equations for compressible flows, where the closure term, or a portion of it, is modeled using trainable parameters or a Neural Network. The first application considers the 2D NASA Wall-Mounted Hump test case, where a production-term parameter is optimized against time-averaged LES data. A second application is carried out on the VKI LS-59 turbine blade, where the Spalart–Allmaras eddy viscosity field is reconstructed through the optimization of a trainable spatial field. A dataset is generated starting from the VKI LS-59 turbine blade geometry using the differentiable BROADCAST solver with the Spalart–Allmaras turbulence model. The results highlight the flexibility of the framework, showing its applicability beyond turbulence modeling to a broader class of physics-informed PDE-constrained problems with data-driven components.

Thanks to the availability of massive amounts of data, computing resources, and advanced algorithms, artificial intelligence (AI) has entered nearly every sector. This has sparked significant investment and interest, particularly in building data centers to develop and operate AI models. In this technical review article, we present energy consumption scenarios of data centers and their impact on GHG emissions, considering both near-term projections (up to 2030) and the long-term outlook (2035 and beyond). Through a scenario synthesis augmented by probabilistic uncertainty analysis, we decouple the analysis of AI’s physical energy footprint from its qualitative potential as a decarbonization enabler. In the near-term, the growing demand for AI will likely strain computing resources, increasing electricity consumption and associated CO2 emissions. However, the long-term outlook includes significant, albeit highly uncertain, promise. AI has the potential to fundamentally optimize processes across industries, from energy production to logistics, potentially decreasing the broader global carbon footprint. While establishing a definitive “net impact” requires complex macroeconomic tracking of rebound effects outside our current scope, this synthesis highlights the pathways required—including zero-carbon grids and algorithmic efficiency—to ensure AI’s systemic decarbonization benefits can realistically offset its substantial infrastructure footprint.

Structural condition-based maintenance for naval vessels combines onboard monitoring to sense and evaluate the current structural health of a vessel and make projections about the future state, plus a decision framework to determine the optimal moment when maintenance activities should be performed. This paper explores the implementation of structural condition-based maintenance aboard naval vessels in an end-to-end perspective, meaning from conceptualization to defining a baseline ship structural health monitoring sensor system and to linking with a decision framework for making informed maintenance decisions. Beginning with considering the motivations and necessities when operating naval vessels, possible maintenance strategies are defined. Competing motivations based on the relevant stakeholders are discussed, especially related to the desired outcomes of implementing a condition-based maintenance system for hull structural integrity. The major components of a condition-based maintenance system are outlined, focusing on structural damage due to fatigue, corrosion, and extreme loading. Special attention is paid to structural health monitoring and the different sensing techniques required, along with the state-of-the-art research supporting such techniques. Multiple monitoring concepts are explored, focusing on the different ways to monitor the wave environment exciting the vessel and the resulting structural damage. Three selected previous monitoring campaigns and resulting lessons learned offer guidance on a resulting baseline sensor system for structural condition-based maintenance for naval vessels. Specific areas of needed future research are pointed out to further advance structural condition-based maintenance onboard naval vessels.