Acoustic Insulation Optimization of Walls and Panels with Functional Graded Hollow Sections Using Graph Transformer Evaluator and Probability-Informed Genetic Algorithm

Building and Environment, Год журнала: 2025, Номер unknown, С. 112550 - 112550

Опубликована: Янв. 1, 2025

Язык: Английский

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A Review of Numerical Techniques for Frictional Contact Analysis

Lubricants, Год журнала: 2025, Номер 13(1), С. 18 - 18

Опубликована: Янв. 6, 2025

This review analyzes numerical techniques for frictional contact problems, highlighting their strengths and limitations in addressing inherent nonlinearities computational demands. Finite element methods (FEM), while dominant due to versatility, often require computationally expensive iterative solutions. Alternative methods, like boundary (BEM) meshless offer potential advantages but further exploration broader applicability. The choice of algorithm significantly impacts accuracy efficiency; penalty though efficient, can lack at high friction coefficients; whereas, Lagrange multiplier more accurate, are demanding. selection an appropriate constitutive model is crucial; the Coulomb law common, sophisticated models necessary represent real-world complexities, including surface roughness temperature dependence. paper delves into future research that prioritizes developing efficient algorithms parallel computing strategies. Advancements modelling vital improved accuracy, along with enhanced detection complex geometries large deformations. Integrating experimental data multiphysics capabilities will enhance reliability applicability these across various engineering applications. These advancements ultimately improve predictive power simulations diverse fields.

Язык: Английский

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Behavior Expectation‐Based Anomaly Detection in Bridge Deflection Using AOA‐BiLSTM‐TPA: Considering Temperature and Traffic‐Induced Temporal Patterns

Structural Control and Health Monitoring, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

In the realm of structural health monitoring (SHM), understanding expected behavior a structure is vital for timely identification anomalous activities. Existing methods often model only physical quantities data, neglecting corresponding temporal information. To address this, this paper presents an innovative deep learning framework that synergistically combines BiLSTM model, fortified by pattern attention (TPA) mechanism, with time‐encoded temperature and traffic‐induced deflection‐temporal patterns. The arithmetic optimization algorithm (AOA) employed optimal hyperparameter tuning, incremental was implemented to enable real‐time updates model. Based on proposed framework, anomaly detection method subsequently developed. This bidirectional: it uses quantile loss provide ranges behavior, identifying isolated anomalies, while windowed normalized mutual information (WNMI) based multivariate kernel density estimation (MKDE) helps detect trend variability caused decreases in stiffness. were validated using data from operational cable‐stayed bridge. results demonstrate effectively predicts detects highlighting critical role SHM.

Язык: Английский

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Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review

Polymers, Год журнала: 2024, Номер 16(18), С. 2607 - 2607

Опубликована: Сен. 14, 2024

This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type recurrent neural network (RNN), in field polymeric sciences. LSTM networks have shown notable effectiveness modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures dynamic processes polymers. delves into use models polymer properties, monitoring polymerization processes, evaluating degradation mechanical performance Additionally, it addresses challenges related to availability interpretability. Through various case studies comparative analyses, demonstrates different science applications. Future directions also discussed, with an emphasis on real-time applications need interdisciplinary collaboration. The goal this is connect advanced machine learning (ML) techniques science, thereby promoting innovation improving predictive capabilities field.

Язык: Английский

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Applied Artificial Intelligence in Materials Science and Material Design

Advanced Intelligent Systems, Год журнала: 2025, Номер unknown

Опубликована: Март 2, 2025

Materials science has traditionally relied on a combination of experimental techniques and theoretical modeling to discover develop new materials with desired properties. However, these processes can be time‐consuming, resource‐intensive, often limited by the complexity material systems. The advent artificial intelligence (AI), particularly machine learning, revolutionized offering powerful tools accelerate discovery, design, characterization novel materials. AI not only enhances predictive properties but also streamlines data analysis in like X‐Ray diffraction, Raman spectroscopy, scanning probe microscopy, electron microscopy. By leveraging large datasets, algorithms identify patterns, reduce noise, predict behavior unprecedented accuracy. In this review, recent advancements applications across various domains science, including synchrotron studies, microscopies, metamaterials, atomistic modeling, molecular drug are highlighted. It is discussed how AI‐driven methods reshaping field, making discovery more efficient, paving way for breakthroughs design real‐time analysis.

Язык: Английский

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A Systematic Review of Isogeometric Contact Analysis and Its Applications

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Апрель 29, 2024

Язык: Английский

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Finite Element Model Updating for Material Model Calibration: A Review and Guide to Practice

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 23, 2024

Abstract Finite element model updating (FEMU) is an advanced inverse parameter identification method capable of identifying multiple parameters in a material through one or few well-designed tests. The has become more mature thanks to the widespread use full-field measurement techniques, such as digital image correlation. Proper application FEMU requires extensive expertise. This paper offers review and guide practice. It also presents FEMU-DIC, open-source software package. We conclude by discussing challenges opportunities this field with intent inspiring future research.

Язык: Английский

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Machine Learning in 3D and 4D Printing of Polymer Composites: A Review

Polymers, Год журнала: 2024, Номер 16(22), С. 3125 - 3125

Опубликована: Ноя. 8, 2024

The emergence of 3D and 4D printing has transformed the field polymer composites, facilitating fabrication complex structures. As these manufacturing techniques continue to progress, integration machine learning (ML) is widely utilized enhance aspects processes. This includes optimizing material properties, refining process parameters, predicting performance outcomes, enabling real-time monitoring. paper aims provide an overview recent applications ML in composites. By highlighting intersection technologies, this seeks identify existing trends challenges, outline future directions.

Язык: Английский

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Reconstructing Geometric Models from the Point Clouds of Buildings Based on a Transformer Network

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 359 - 359

Опубликована: Янв. 22, 2025

Geometric building models are essential in BIM technology. The reconstruction results using current methods usually represented mesh, which is limited to visualization purposes and hard directly import into or modeling software for further application. In this paper, we propose a model method based on transformer network (DeepBuilding). Instead of reconstructing the polyhedron buildings, strive recover CAD operation constructing from point cloud. By representing with its sequence, can be imported We first translate procedure command sequence that vectorized processed by network. Then, transformer-based convert input clouds representation sequences decoding geometry information encoded features. A tool developed 3D shape (such as mesh) file format other supports. Comprehensive experiments conducted, evaluation demonstrate our produce competitive high geometric fidelity while preserving more details reconstruction.

Язык: Английский

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Finite element modeling of clavicle fracture fixations: a systematic scoping review

Medical & Biological Engineering & Computing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

Язык: Английский

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