Abstract Reinforced concrete shear walls are essential components of seismic-resistant structures, yet accurately predicting their drift capacity remains challenging due to the nonlinear interaction among geometric, material, and loading parameters. Traditional empirical or analytical models often fail to capture these complexities, motivating the use of machine learning (ML) as a data-driven alternative. In this study, a dedicated experimental database of 208 reinforced concrete shear walls with rectangular and I-shaped cross-sections was derived from the ACI 445B Shear Wall Database. The selected input parameters include the wall length-to-thickness ratio, normalized shear coefficient, axial load ratio, transverse reinforcement spacing-to-diameter ratio, ultimate-to-yield strength ratio of boundary reinforcement, longitudinal reinforcement ratio in boundary and web regions, and transverse reinforcement ratio. Three tree-based machine learning regressors were optimized using nested cross-validation. To improve predictive accuracy, their outputs were combined into a stacking ensemble. The dataset was divided into a development subset and an independent hold-out test subset, with model development and hyperparameter optimization performed exclusively within the development data. In addition, the predictive capability of the machine learning models was compared with the empirical formulation proposed by Abdullah and Wallace. The results show that the ML-based approaches provide strong predictive performance on independent test data. Among the considered models, the Stacking Ensemble achieved the best overall results, highlighting the potential of ensemble learning to capture the complex nonlinear relationships governing the drift capacity of reinforced concrete shear walls. The findings support the use of data-driven models as complementary tools to traditional empirical approaches for deformation-capacity prediction.
Benchmarking identification methods is essential to validate newly proposed algorithms. The machine learning community has illustrated how benchmarks can be a driver for new developments in the field. However, thorough benchmarking is not commonplace in the system identification literature, and good benchmarking datasets and leaderboards are missing. This paper reflects on what constitutes a good system identification benchmark and introduces the nonlinearbenchmark.org platform for nonlinear system identification benchmarks and leaderboard together with a set of baseline results that have been generated for a set of widely used and promising benchmarking datasets.
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.
