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 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.
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.
Three data-driven assimilation algorithms are compared in the flow around a surface mounted prism at Re = 1000 $ \mathit{\operatorname{Re}}=1000 $ re equals 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 Re = 800 $ \mathit{\operatorname{Re}}=800 $ re equals 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.
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.
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, α $ \alpha $ 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.
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.
The Bayesian approach offers a systematic framework for updating finite element (FE) models and quantifying the remaining uncertainty given measured data. However, an inappropriate formulation of the probabilistic model can compromise accuracy. This paper presents an improved hierarchical Bayesian method for FE model updating by formulating the likelihood function in a fully probabilistic manner and incorporating time-varying stiffness parameters. A key methodological novelty lies in latent variable treatment of unmeasured mode shapes within the Bayesian hierarchy, yielding the joint inference of structural parameters and modal quantities in a fully generative manner without explicit eigenvalue decomposition. Furthermore, the geometric nature of mode shapes is rigorously respected by constraining them to the unit hypersphere using a Bingham distribution. A Metropolis-within-Gibbs sampling algorithm is developed to approximate the posterior distribution, with QR and Cholesky decompositions ensuring computational efficiency and accuracy. Three case studies, including synthetic, lab, and field test data, validate the effectiveness of the proposed approach. The updated model can be used as a reference model for structural damage detection and condition assessment in structural health monitoring. Impact statement This study introduces a consistent hierarchical Bayesian method for time-varying finite element model updating, enabling more accurate and reliable structural health monitoring under environmental variability. Formulated in a coherent Bayesian framework, the method provides more reliable and realistic parameter estimates than traditional approaches. It avoids the need for tedious modal matching and handles incomplete modal data, making it practical for real-world applications. The proposed sampling strategy ensures computational efficiency and robustness. This work supports better decision-making in structural maintenance and enhances the sustain-ability of infrastructure systems.
Space utilization is a key driver for yacht design. Larger hulls increase the cost of the yacht substantially, and so space is always at a premium. The designs should feel spacious and luxurious, making the best use of the available volume while also conforming to standard engineering constraints such as safety and efficiency. To maximize the useful space, genetic algorithms have been utilized to design the layout with the aim of supporting the concept design process. Although a number of approaches have been developed, integrating these tools within the design process requires a substantial effort in human-machine teaming. This is at least in part due to the layout optimization problem being trivial in a general sense of providing effective space utilization, but the general layouts produced do not resonate with the designers as they do not conform to a brand often developed over decades. Essentially, does the yacht feel like other designs by the same company to a human designer? This article explores these two aspects of integrating an AI tool into a traditional design studio, generating concepts that resonate with the industrial designers while conforming to engineering constraints. It is shown that this only requires two objectives to achieve, in contrast to other approaches that use a large number of constraints or objectives to push the layout into a specific part of the design space. The resulting AI layout engineer is integrated into the team as the designs are competitive with those developed by the design team.
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 w from the nonlinear system R(w) = 0, with a generic additive, parametrized, and differentiable correction f_ϕ(w), with trainable parameters ϕ. We show how to optimize phi 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.
Underwater three-dimensional (3D) scanning systems play a crucial role in marine archaeology, offshore engineering, and underwater robotics by capturing detailed representations of areas of interest. However, conventional underwater scanning methods are often time-consuming and inefficient, frequently collecting redundant data points that add little value to the overall representation. This study introduces an artificial intelligence (AI)-driven approach to underwater surface scanning that leverages machine learning techniques such as Bayesian optimisation and Gaussian Process regression to address these inefficiencies. A prototype 3D scanner, controlled by machine learning algorithms, was developed and tested in laboratory conditions that replicated the conditions of offshore deployment. Surfaces with different geometries, including flat, conical, and wavy shapes, were scanned to evaluate the performance of the proposed method against traditional approaches. The new scanning method autonomously selects the most informative measurement locations, reducing the number of scans required while exceeding the accuracy of conventional techniques. The results demonstrate that the proposed approach provides more precise surface representations for most geometries while significantly reducing scanning time. The approach not only reduces computational and storage requirements but also enables efficient data transmission in low-bandwidth scenarios, such as with underwater wireless communication systems. This research highlights the potential of machine learning-enhanced scanning systems to outperform traditional methods, offering a faster, more adaptable, and more accurate solution for underwater visualization in diverse scientific and engineering applications.
Accurate prediction of bridge crack evolution is essential for infrastructure safety assurance and maintenance optimization. This study develops an interpretable machine learning framework to predict the expansion of cracks on the main beam in small-and medium-span highway beam bridges and identify the underlying mechanisms of structural deterioration. A comprehensive database was constructed from inspection and monitoring records of over 100 bridges, featuring critical degradation indicators, including crack density (CD) and maximum crack width (MCW). Following data preprocessing and feature selection through correlation analysis, three machine learning algorithms, that is, support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost), were implemented and evaluated using statistical metrics (R2, RMSE, and MAE). The XGBoost model demonstrated superior predictive performance with R2 values of 0.9433 and 0.9413 for MCW and CD, respectively, reducing RMSE by up to 66.8% and MAE by up to 72% compared to alternative models. SHAP (SHapley Additive exPlanations) analysis revealed that four factors, namely, vehicle load (VL), annual average daily truck traffic (ADTT), bridge age (BA), and annual average daily traffic (ADT), collectively contributed 61.45 +/- 2.35% to crack development, with VL (19.7%) being the most influential factor. These findings identify excessive traffic loading and aging as the dominant drivers of crack propagation in beam bridges, providing valuable insights for targeted maintenance strategies and bridge management.
Thin-walled truncated conical shells subjected to axial compression are extremely susceptible to buckling, with experimentally observed buckling loads often falling well below classical theoretical predictions. The ratio of the experimentally measured critical load to its theoretical counterpart is defined as the Knockdown Factor (KDF). Although design guidelines proposed by agencies such as NASA provide conservative estimates of KDFs to ensure safety, recent research has highlighted the need to revisit and refine these provisions due to their excessive conservatism. In this context, the present study compares robust machine learning (ML) models for predicting buckling loads, or equivalently KDFs, of truncated conical shells using Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest Regression (RFR) and Histogram Gradient Boosting (HGB). These models are able to capture strong nonlinear and complex feature interactions which are inherent in buckling phenomena. A comprehensive database compiled from existing literature and complemented with a set of simulated data is employed for model training and testing. To lead a new direction in the line of data-driven KDF prediction, a novel hybrid ML framework integrating Gaussian Process Regression (GPR) with Extreme Gradient Boosting (XGB), referred to as (GPR + XGB), is proposed. Additionally, a sensitivity analysis is performed to identify the most influential features governing the KDF predictions of truncated conical shells. The proposed hybrid framework that leverages experimental data as well as simulated data to accurately predict buckling KDFs of truncated conical shells, achieve significantly improved accuracy over existing ML models and conservative design guidelines.
Simulating turbulent fluid flows is a computationally prohibitive task, as it requires the resolution of fine-scale structures and the capture of complex nonlinear interactions across multiple scales. Consequently, extensive research has focused on analysing turbulent flows from a data-driven perspective. However, due to the complex and chaotic nature of these systems, traditional models often become unstable. To overcome these limitations, we propose a purely stochastic approach that separately addresses the evolution of large-scale coherent structures and the closure of high-fidelity statistical data. To this end, the dynamics of the filtered data are learnt using an autoregressive model. This combines a variational-autoencoder (VAE) and Transformer architecture. The VAE projection is probabilistic, ensuring consistency between the model’s stochasticity and the flow’s statistical properties. The mean realisation of stochastically sampled trajectories from our model shows relative $ {L}_1 $ and $ {L}_2 $ distances of 6% and 10%, respectively. Moreover, our framework enables the construction of meaningful confidence intervals, achieving a prediction interval coverage probability of 80% with minimal interval width. To recover high-fidelity velocity fields from the filtered space, Gaussian Process (GP) regression is employed. This strategy has been tested in the context of a Kolmogorov flow exhibiting chaotic behavior. We compare the performance of our model with state-of-the-art probabilistic baselines, including a VAE and a diffusion model. We demonstrate that our Gaussian process-based closure outperforms these baselines in capturing first and second moment statistics in this particular test bed, providing robust and adaptive confidence intervals.
Point clouds derived from UAV photogrammetry are a cost-effective alternative to LiDAR for infrastructure inspections, but they often include both structural and non-structural elements that complicate analysis. Traditional denoising filters remove outliers indiscriminately and frequently erode edges, making it difficult to preserve the curved tunnel lining while distinguishing bolts, access gates, or pipelines. In contrast, segmentation-based approaches leverage geometric context to explicitly separate lining surfaces from ancillary components, thereby enabling more accurate deformation analysis and structural assessment. To that end, this paper presents a novel approach for denoising image point clouds using a synthetic training dataset to address the scarcity of labeled public data for enhancing point cloud quality. Unlike other denoising approaches that rely on projections or assume points lie on a predefined surface shape, this segmentation-based denoising method retains only meaningful points in their original locations, allowing for more accurate analysis of deformation. Enhanced by synthetic training datasets, the application of the proposed denoising method to a road tunnel image point cloud and a subway tunnel terrestrial laser scanning point cloud demonstrates its potential to enhance point cloud quality in tunnels with diverse geometries and point cloud data resources, even when data are limited. The method achieves an 80% mean intersection over union for both the road tunnel and the subway tunnel from manual annotation. This enables an improvement in structural deformation analysis at the mm level.
Punching shear failure in slab-column connections is a brittle collapse mode that threatens the safety of flat reinforced concrete (RC) slabs. Conventional design provisions are generally conservative but exhibit inconsistencies across geometric and material variations. This study develops an eXtreme Gradient Boosting (XGBoost) model to predict the ultimate punching shear capacity of flat RC slabs, using a database of experimental results categorized by four different geometric domains, including square slab with square column, circular slab with circular column, square slab with circular column, and circular slab with square column, covering the geometric, materials strength, and reinforcement properties of input parameters. The model achieved high predictive accuracy across the domains with coefficient of determination (R2) values > 0.930 in unseen testing datasets with minimal bias (0.994–1.006) and reduced scatter. Model interpretability, addressed through the SHapley Additive exPlanations analysis, confirmed slab thickness and average effective depth as the most critical predictors of shear capacity, followed by concrete strength and reinforcement parameters, while boundary condition parameters showed negligible influence due to the predominance of interior column cases. These findings demonstrate that XGBoost provides accurate, reliable, and interpretable predictions of punching shear capacity, offering a data-driven alternative to code-based methods and supporting safer and more consistent design of flat RC slabs.
Addressing and predicting degenerative phenomena in domains such as health care and engineering, two fundamental fields of vital importance for society, offers valuable insights into early warning steps and critical event forecasting, leading to far-reaching implications for safety and resource allocation. By harnessing the power of data-driven insights, prognostics becomes the principal component of predicting such phenomena. Developing clustering techniques as feature extractors acts as an intermediate step between the raw incoming data and prognostics and provides the opportunity to unveil hidden relationships within complex datasets. However, when limited, noisy, and multimodal data are available in a label-free format, extensive preprocessing, and unreliable, complicated models are required for extracting meaningful features. This prohibits the development of adaptable methods in diverse domains that are in favor of robustness and interpretability. In this regard, this study introduces a novel unsupervised deep clustering model for feature extraction in degenerative phenomena. The model innovatively extracts prognostic-related features from raw data via clustering analysis, characterized by an increasing monotonic behavior representing system deterioration. This monotonicity is partial rather than complete, to incorporate the potential occurrence of oscillations in the degradation trajectory of the system or noise-related data, reflecting real-world scenarios. Its performance, robustness, generalizability, and interpretability are evaluated across diverse domains utilizing three datasets from health care and engineering featuring limited, noisy, high-dimensional, and multimodal raw signals. Results show that the model extracts meaningful prognostic-related features in both domains and all datasets, without a significant alteration in its architecture and independently of the chosen prognostic algorithm.
This paper presents a reliability-constrained Bayesian optimization framework for structural design under uncertainty, addressing challenges in stochastic optimization where the objectives and constraints are defined implicitly by potentially expensive numerical models. Our approach explicitly accounts for parameter uncertainty using results from Bayesian quadrature for uncertainty propagation in Gaussian process surrogate models. The method accommodates arbitrary probability distributions and employs gradient-based optimization for acquisition function maximization, strategically selecting sample points to minimize numerical model evaluations. We demonstrate our algorithm’s superior performance over random search and conventional Bayesian optimization through both an analytical test function and a prestressed tie-beam design case study, showing its practical applicability to structural optimization problems.
Surrogate models have gained widespread popularity for their effectiveness in replacing computationally expensive numerical analyses, particularly in scenarios such as design optimization procedures, requiring hundreds or thousands of simulations. While one-shot sampling methods—where all samples are generated in a single stage without prior knowledge of the required sample size—are commonly adopted in the creation of surrogate models, these methods face significant limitations. Given that the characteristics of the underlying system are generally unknown prior to training, adopting one-shot sampling can lead to suboptimal model performance or unnecessary computational costs, especially in complex or high-dimensional problems. This paper addresses these challenges by proposing a novel, model-independent adaptive sampling approach with batch selection, termed Cross-Validation Batch Adaptive Sampling for High-Efficiency Surrogates (CV-BASHES). CV-BASHES is first validated using two analytical functions to explore its flexibility and accuracy under different configurations, confirming its robustness. Comparative studies on the same functions with two state-of-the-art methods, maximum projection (MaxPro) and scalable adaptive sampling (SAS), demonstrate the superior accuracy and robustness of CV-BASHES. Its applicability is further demonstrated through a geotechnical application, where CV-BASHES is used to develop a surrogate model to predict the horizontal deformation of a diaphragm wall supporting a deep excavation. Results show that CV-BASHES efficiently selects training samples, reducing the dataset size while maintaining high surrogate accuracy. By offering more efficient sampling strategies, CV-BASHES streamlines and enhances the process of creating machine learning models as surrogates for tackling complex problems in general engineering disciplines.
