Automated design of phononic crystals under thermoelastic wave propagation through deep reinforcement learning

Article Acoustics

Membrane-type acoustic metamaterial using cork sheets and attached masses based on reused materials

Giuseppe Ciaburro et al.

Summary: In this study, a new membrane-type acoustic metamaterial was developed using a recycled cork membrane and reusing masses. The results suggest that these structures can be used for the acoustic correction of rooms.

APPLIED ACOUSTICS (2022)

添加到收藏夹

Article Engineering, Civil

A data-driven approach for drive-by damage detection in bridges considering the influence of temperature change

Robert Corbally et al.

Summary: This paper proposes a new data-driven approach for drive-by monitoring of bridge condition using an Artificial Neural Network (ANN). The ANN is trained to predict bridge behavior based on acceleration measurements from a passing vehicle. The response at the tire-pavement contact point is inferred and used as the primary input to the ANN, along with vehicle speed. The proposed algorithm demonstrates improved performance in detecting bridge cracking at different speeds, pavement conditions, and temperature variations compared to traditional methods, making it a suitable approach for long term bridge condition monitoring.

ENGINEERING STRUCTURES (2022)

添加到收藏夹

Article Materials Science, Multidisciplinary

A deep learning approach based on a data-driven tool for classification and prediction of thermoelastic wave's band structures for phononic crystals

Shirin Javadi et al.

Summary: This article uses data-driven methods to predict the band structures of thermoelastic waves in nano-scale phononic crystal beams, revealing the importance of design parameters and effectively using deep neural networks for prediction and classification.

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES (2022)

添加到收藏夹

Article Physics, Multidisciplinary

Machine-learning-driven on-demand design of phononic beams

Liangshu He et al.

Summary: This study demonstrates how machine learning can be used to study phononic crystal beams through two inverse design schemes. It provides a method for inverse design of structural parameters to maximize the bandgap width and uses a neural network to solve the training difficulty problem and achieve inverse structure design with targeted topological properties.

SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY (2022)

添加到收藏夹

Article Energy & Fuels

Transportation energy demand forecasting in Taiwan based on metaheuristic algorithms

Ali Lashgari et al.

Summary: This study proposes a new methodology for forecasting future transportation energy demand in Taiwan. The study introduces a new improved version of Emperor Penguin Optimizer (IEPO) to provide an optimal and suitable forecasting model. The algorithms optimize the coefficients of three different models (linear, exponential, and quadratic) based on population, GDP growth rate, and total annual vehicle-km. The results demonstrate that the optimized exponential method shows better efficiency for transportation energy demand forecasting.

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS (2022)

添加到收藏夹

Article Engineering, Civil

Machine learning driven seismic performance limit state identification for performance-based seismic design of bridge piers

Borislav Todorov et al.

Summary: This study utilizes machine learning techniques to predict different damage states of reinforced concrete bridge piers under seismic loading, and compares them with existing physics-based empirical models. The machine learning models are found to have significantly improved accuracy and precision.

ENGINEERING STRUCTURES (2022)

添加到收藏夹

Review Nanoscience & Nanotechnology

Intelligent on-demand design of phononic metamaterials

Yabin Jin et al.

Summary: With the growing interest in artificial materials, the demand for advanced functionalities in phononic crystals and acoustic metamaterials is increasing. Machine learning, as an important branch of artificial intelligence, provides a powerful means of achieving efficient and accurate design processes. This review summarizes recent works on the combination of phononic metamaterials and machine learning and provides an outlook on future development directions.

NANOPHOTONICS (2022)

添加到收藏夹

Article Materials Science, Multidisciplinary

A deep learning approach for inverse design of the metasurface for dual-polarized waves

Fardin Ghorbani et al.

Summary: This article introduces a machine learning-based method for the inverse realization of metasurfaces, which uses Deep Neural Network (DNN) to generate unit cell structures for both TE and TM polarized waves. By deliberately designing annular models, metasurfaces can be automatically generated over wide frequencies. By confining the output, the number of calculations is reduced and learning speed is increased. This method achieves 92% accuracy for generating the desired metasurface structure for dual orthogonal polarizations.

APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING (2021)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Deep reinforcement learning for automated design of reinforced concrete structures

Jong-Hyun Jeong et al.

Summary: This study introduces a novel reinforcement learning framework for automated reinforced concrete beam design. By training an RL agent without hand-labeled dataset, the agent successfully self-learned cost-effective beam design and showed promising results in validation with 100 design cases.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

添加到收藏夹

Article Engineering, Civil

Isolation performance of a small modular reactor using 1D periodic foundation

Chunfeng Zhao et al.

Summary: This study validates the effectiveness of one-dimensional periodic foundation in reducing seismic response through experiments and numerical simulations, demonstrating its ability to act when seismic wave frequencies fall within the band gap range.

ENGINEERING STRUCTURES (2021)

添加到收藏夹

Article Engineering, Multidisciplinary

Machine learning paradigm for structural health monitoring

Yuequan Bao et al.

Summary: The conventional vibration-based methods face challenges in accurately detecting structural damages, thus necessitating the development of novel diagnosis and prognosis methods based on various monitoring data.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

添加到收藏夹

Article Engineering, Mechanical

Band structure analysis of Green-Naghdi-based thermoelastic wave propagation in cylindrical phononic crystals with energy dissipation using a meshless collocation method

Seyed Mahmoud Hosseini et al.

Summary: In this paper, a meshless collocation method based on the generalized finite difference (GFD) method is used to analyze the band structure of thermoelastic waves propagating in cylindrical phononic crystals. The study investigates the effects of material parameters and layer thickness on the band-gap properties of thermoelastic waves, with particular attention to the influence of the dimensionless speed of the thermal wave on the band-gaps.

INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES (2021)

添加到收藏夹

Review Computer Science, Information Systems

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Mahmoud Abbasi et al.

Summary: Modern communication systems generate massive and heterogeneous traffic data, prompting researchers to apply deep learning techniques for network traffic monitoring and analysis. This paper reviews the applications of deep learning in NTMA, discusses challenges and future research directions, and highlights the importance of recognizing hidden patterns in analyzing complex network traffic.

COMPUTER COMMUNICATIONS (2021)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Design of one-dimensional acoustic metamaterials using machine learning and cell concatenation

Rih-Teng Wu et al.

Summary: Metamaterial systems have opened new possibilities for designing acoustic devices. This study introduces two machine learning-based approaches for designing one-dimensional periodic and non-periodic metamaterial systems. The RL-based method is efficient for periodic metamaterial design, while the NN-based method is effective for non-periodic metamaterial design without the need for pre-defined material unit cell numbers.

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION (2021)

添加到收藏夹

Article Mechanics

Probability analysis of bistable composite laminates using the subset simulation method

Saeid Saberi et al.

Summary: The stacking sequence has a significant impact on the bistability probability of bistable composite laminates, with crossply composites being the most likely to be bistable; moisture absorption can dramatically reduce bistability probability; for bistable laminates subject to humidity, the coefficient of thermal and moisture expansions have the greatest influence on the bistability probability.

COMPOSITE STRUCTURES (2021)

添加到收藏夹

Article Engineering, Multidisciplinary

Topology optimization of phononic-like structures using experimental material interpolation model for additive manufactured lattice infills

Xuan Liang et al.

Summary: Phononic crystals have gained popularity for their band gap property in sound wave propagation. Topology optimization has been used in the design of Phononic crystals, with a focus on single micro-scale topology optimization. The proposed quasi two-scale topology optimization framework is suitable for additive manufacturing and has been applied to design 2D phononic-like structures for sound transmission coefficient optimization.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

添加到收藏夹

Article Engineering, Civil

Vibration analysis and band-gap characteristics of periodic multi-span power transmission line systems

W. Fan et al.

Summary: The study analyzed the vibration characteristics of periodic multi-span power transmission line systems and investigated their band-gap properties. By using complex mathematical methods, the interaction between wave propagation across different spans and the effects of parameters on band-gap characteristics were revealed.

ENGINEERING STRUCTURES (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

A New Structural Health Monitoring Approach Based on Smartphone Measurements of Magnetic Field Intensity

Ali Matin Nazar et al.

Summary: This paper proposes a new structural damage detection approach based on monitoring magnetic field intensity variations using smartphones, with experimental and numerical studies confirming its efficiency. Results show that magnetic field intensity increases as damage progresses, while detection accuracy declines as the distance between the smartphone and steel plate surface increases.

IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Hybrid machine learning based energy policy and management in the renewable-based microgrids considering hybrid electric vehicle charging demand

Ming Lei et al.

Summary: This paper proposes a secured energy management architecture for renewable hybrid AC-DC microgrids. It uses a whale optimization algorithm to minimize grid operation costs and introduces an intrusion detection system based on sequential hypothesis testing to detect cyber-attacks on advanced metering infrastructures. The feasibility and performance of the architecture are tested on a practical hybrid microgrid based on the IEEE 33-bus test system.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

添加到收藏夹

Article Acoustics

Modeling acoustic metamaterials based on reused buttons using data fitting with neural networka)

Giuseppe Ciaburro et al.

Summary: A new metamaterial was developed using a polyvinyl chloride membrane and buttons, with measurements of sound absorption coefficient used to train a simulation model based on artificial neural networks. The model demonstrated excellent generalization capacity, accurately estimating the acoustic absorption coefficient of the metamaterial, and was further used to conduct a sensitivity analysis to evaluate the impact of different input variables on the output.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2021)

添加到收藏夹

Article Computer Science, Information Systems

DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision-Making

Abbas Varmaghani et al.

Summary: The optimal use of node energy has always been one of the biggest challenges in wireless sensor networks, and researchers proposed a distributed computing method and a clustering and routing network method based on fuzzy multiple attribute decision-making. Results show that under the optimistic method, especially in extensive networks, energy consumption is lower; while in small networks, the blind scheme is more effective.

WIRELESS COMMUNICATIONS & MOBILE COMPUTING (2021)

添加到收藏夹

Article Multidisciplinary Sciences

A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)

Omid Ghorbanzadeh et al.

Summary: Earthquakes and heavy rainfalls are leading causes of landslides globally, and deep learning approaches like U-Net and ResU-Net have shown promising results in automatic landslide detection using high-resolution satellite imagery. This study successfully applied these algorithms to freely available data and achieved high detection performance in three case study areas.

SCIENTIFIC REPORTS (2021)

添加到收藏夹

Article Construction & Building Technology

Stochastic energy management and scheduling of microgrids in correlated environment: A deep learning-oriented approach

Tan Cheng et al.

Summary: This paper proposes a secured stochastic energy management scheme for hybrid AC-DC microgrids considering renewable energy sources, plug-in hybrid electric vehicles, and energy storage devices. The operation of microgrids is formulated as a single objective optimization problem solved using an interior search optimization algorithm. Additionally, a deep learning-based intrusion detection system is proposed to enhance data security in microgrids, with the performance of the scheme evaluated using the IEEE 33 bus test system.

SUSTAINABLE CITIES AND SOCIETY (2021)

添加到收藏夹

Article Engineering, Multidisciplinary

Designing phononic crystal with anticipated band gap through a deep learning based data-driven method

Xiang Li et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)

添加到收藏夹

Article Acoustics

Nonlocal coupled thermoelastic wave propagation band structures of nano-scale phononic crystal beams based on GN theory with energy dissipation: An analytical solution

Seyed Mahmoud Hosseini et al.

WAVE MOTION (2020)

添加到收藏夹

Article Engineering, Mechanical

Interactive inverse design of layered phononic crystals based on reinforcement learning

Chengcheng Luo et al.

EXTREME MECHANICS LETTERS (2020)

添加到收藏夹

Article Physics, Applied

A machine learning based approach for phononic crystal property discovery

Seid M. Sadat et al.

JOURNAL OF APPLIED PHYSICS (2020)

添加到收藏夹

Article Engineering, Civil

A refined subset simulation for the reliability analysis using the subset control variate

Azam Abdollahi et al.

STRUCTURAL SAFETY (2020)

添加到收藏夹

Article Engineering, Civil

Built-up structural steel sections as seismic metamaterials for surface wave attenuation with low frequency wide bandgap in layered soil medium

Muhammad et al.

ENGINEERING STRUCTURES (2019)

添加到收藏夹

Article Engineering, Civil

3D FEM analysis of the effect of buried phononic crystal barriers on vibration mitigation

Carlos Albino et al.

ENGINEERING STRUCTURES (2019)

添加到收藏夹

Article Environmental Sciences

Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

Omid Ghorbanzadeh et al.

REMOTE SENSING (2019)

添加到收藏夹

Review Engineering, Civil

Emerging artificial intelligence methods in structural engineering

Hadi Salehi et al.

ENGINEERING STRUCTURES (2018)

添加到收藏夹

Article Engineering, Civil

A novel unsupervised deep learning model for global and local health condition assessment of structures

Mohammad Hossein Rafiei et al.

ENGINEERING STRUCTURES (2018)

添加到收藏夹

Article Engineering, Multidisciplinary

Robust topology optimization of phononic crystals with random field uncertainty

Xiaopeng Zhang et al.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2018)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Path following techniques for geometrically nonlinear structures based on Multi-point methods

Ali Maghami et al.

COMPUTERS & STRUCTURES (2018)

添加到收藏夹

Article Construction & Building Technology

Compressive strength prediction of environmentally friendly concrete using artificial neural networks

Hosein Naderpour et al.

JOURNAL OF BUILDING ENGINEERING (2018)

添加到收藏夹

Article Engineering, Multidisciplinary

A local RBF collocation method for band structure computations of 2D solid/fluid and fluid/solid phononic crystals

Hui Zheng et al.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2017)

添加到收藏夹

Article Engineering, Civil

A phononic band gap model for long bridges. The 'Brabau' bridge case

G. Carta et al.

ENGINEERING STRUCTURES (2017)

添加到收藏夹

Article Engineering, Electrical & Electronic

Deep Reinforcement Learning A brief survey

Kai Arulkumaran et al.

IEEE SIGNAL PROCESSING MAGAZINE (2017)

添加到收藏夹

Article Mechanics

Shape optimization of phononic band gap structures using the homogenization approach

Jaroslav Vondrejc et al.

INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES (2017)

添加到收藏夹

Article Engineering, Multidisciplinary

Band structure computation of in-plane elastic waves in 2D phononic crystals by a meshfree local RBF collocation method

Hui Zheng et al.

ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS (2016)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

A meshfree local RBF collocation method for anti-plane transverse elastic wave propagation analysis in 2D phononic crystals

Hui Zheng et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2016)

添加到收藏夹

Article Multidisciplinary Sciences

Human-level control through deep reinforcement learning

Volodymyr Mnih et al.

NATURE (2015)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Topological optimization of two-dimensional phononic crystals based on the finite element method and genetic algorithm

Hao-Wen Dong et al.

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION (2014)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients

Ivo Grondman et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (2012)

添加到收藏夹

Automated design of phononic crystals under thermoelastic wave propagation through deep reinforcement learning

相关参考文献

导出引文

分享论文