Identification of concrete surface damage based on probabilistic deep learning of images

This publication has 40 references indexed in Scilit:

• Framework for automated UAS-based structural condition assessment of bridges
  Automation in Construction, 2019
• Modal frequency-environmental condition relation development using long-term structural health monitoring measurement: Uncertainty quantification, sparse feature selection and multivariate prediction
  Measurement, 2018
• Probabilistic Damage Detection of a Steel Truss Bridge Model by Optimally Designed Bayesian Neural Network
  Sensors, 2018
• Structural damage identification based on autoencoder neural networks and deep learning
  Engineering Structures, 2018
• Model updating and uncertainty analysis for creep behavior of soft soil
  Computers and Geotechnics, 2018
• Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning
  Computer-Aided Civil and Infrastructure Engineering, 2017
• Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
  Computer-Aided Civil and Infrastructure Engineering, 2017
• A texture‐Based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces
  Computer-Aided Civil and Infrastructure Engineering, 2017
• Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
  Journal of Sound and Vibration, 2017
• Online updating and uncertainty quantification using nonstationary output-only measurement
  Mechanical Systems and Signal Processing, 2016