论文与项目

研究成果

2024 / Computer-Aided Civil and Infrastructure Engineering

Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion

Abstract: Structural health monitoring (SHM) aims to assess civil infrastructures’ performance and ensure safety. Automated detection of in situ events of interest, such as earthquakes, from...

Anomaly Detection Structural Health Monitoring Extreme Events Bayesian Fusion Seismic Event Detection Data Anomalies Faulty Data Interference SHM Deep Learning DL BF

2023 / Measurement

Data anomaly detection for structural health monitoring by multi-view representation based on local binary patterns

Abstract: Structural health monitoring (SHM) systems provide opportunities to understand the structural behaviors remotely in real-time. However, anomalous measurement data are frequently collected...

SHM Data Anomalies LBP Anomaly Detection Multi-View Representation Random Forests RF Local Binary Patterns Structural Health Monitoring

2023 / Construction and Building Materials

A data-driven multi-scale constitutive model of concrete material based onpolynomial chaos expansion and stochastic damage model

Nonlinearity and randomness are two intrinsic characteristics of the mechanical behavior of concrete material. The structural response under large excitation can barely be predicted without...

Concrete Material Data-Driven Modeling Polynomial Chaos Expansion Stochastic Damage Model Stress-Strain Relationship Multi-Scale Constitutive Model PCE

2023 / Structural Control and Health Monitoring

Machine-learning-based methods for output-only structural modal identification

Abstract:In this study, we propose a machine-learning-based approach to identify the modal parameters of the output-only data for structural health monitoring (SHM) that makes full use of the...

Structural Modal Identification Structural Health Monitoring Output-Only Data Self-Coding Neural Network Neural Network Complex Loss Function Vibration Data ML Mode Shapes SHM Modal Parameters Unsupervised Learning Modal Separation Machine Learning

2023 / Mechanical Systems and Signal Processing

Clarifying and quantifying the geometric correlation forprobability distributions of inter-sensor monitoring data: Afunctional data analytic methodology

Abstract: In structural health monitoring (SHM), revealing the underlying correlations of monitoring data is of considerable significance, both theoretically and practically. In contrast to the...

Functional Data Analysis Correlation Analysis Data Dependence Probability Distributions Monitoring Data Distributional Correlations Time-Series Data Inter-Sensor Monitoring Data Random Excitations Statistical Methods Sensor Networks Structural Responses SHM Geometric Correlation Structural Health Monitoring

2023 / Reliability Engineering & System Safety

Deep reinforcement learning-based sampling method for structuralreliability assessment

Abstract: Surrogate model methods are widely used in structural reliability assessment, but conventional sampling methods require a large number of experimental points to construct a surrogate model....

Reward Function Benchmark Problem AlphaGo Numerical Examples DRL Structural Reliability Assessment Deep Reinforcement Learning Sampling Method Limit State Surface Experimental Points Surrogate Model Sampling Space DNN Deep Neural Network Optimization

2023 / Engineering

The state of the art of data science and engineering in structural health monitoring

Abstract:Structural health monitoring(SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments,...

Structural Health Monitoring Data Reconstruction Data Engineering Data Processing Data Compression Data Acquisition Data Science Anomaly Detection SHM

2023 / Structural Health Monitoring

Group sparsity-aware convolutionalneural network for continuous missingdata recovery of structural healthmonitoring

Abstract:In structural health monitoring, data quality is crucial to the performance of data-driven methods for structural damage identification, condition assessment, and safety warning. However,...

Structural Health Monitoring Data Quality Compressive Sensing Continuous Missing Data Structural Damage Identification CNN Data Imperfection Multi-Channel Data Matrix Completion Condition Assessment SHM Convolutional Neural Network Basis Matrix Safety Warning Regression Problem Group Sparsity Data Recovery

2023 / Structural Health Monitoring

Compressive-sensing datareconstruction for structural healthmonitoring: a machine-learningapproach

唐志一

Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare damage...

Data Reconstruction Machine Learning Structural Health Monitoring ML Compressive Sensing Data Acquisition Data Reconstruction Algorithms SHM CS

2023 / Applied Sciences (MDPI)

An interpretable deep learning method for identifying extreme events under faulty data interference

Abstract:Structural health monitoring systems continuously monitor the operational state of structures, generating a large amount of monitoring data during the process. The structural responses of...

Anomaly Detection Extreme Events Seismic Data Transfer Learning Deep Learning TL DL Faulty Data Interference Faulty Data Interpretability