Open AccessJournal Article
Computer Aided Civil and Infrastructure Engineering
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This article is published in Computer-aided Civil and Infrastructure Engineering.The article was published on 2009-01-01 and is currently open access. It has received 590 citations till now. The article focuses on the topics: Civil engineering software & Railway engineering.read more
Citations
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Journal ArticleDOI
Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks
TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.
Journal ArticleDOI
Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types
TL;DR: A framework for quasi real-time damage detection on video using the trained networks is developed and the robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures.
Journal ArticleDOI
Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network
Allen Zhang,Allen Zhang,Kelvin C. P. Wang,Kelvin C. P. Wang,Baoxian Li,Enhui Yang,Xianxing Dai,Yi Peng,Yue Fei,Yang Liu,Joshua Q. Li,Cheng Chen +11 more
TL;DR: The CrackNet, an efficient architecture based on the Convolutional Neural Network, is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel‐perfect accuracy.
Journal ArticleDOI
Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network
TL;DR: The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures as mentioned in this paper, however, the current manual crack description is inadequate and outdated.
Journal ArticleDOI
Structural Damage Detection with Automatic Feature-Extraction through Deep Learning
TL;DR: A novel damage detection approach to automatically extract features from low‐level sensor data through deep learning using a deep convolutional neural network, leading to an excellent localization accuracy on both noise‐free and noisy data set.
References
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Book ChapterDOI
A Study on CNN Transfer Learning for Image Classification
TL;DR: This work proposes the study and investigation of such a CNN architecture model (i.e. Inception-v3) to establish whether it would work best in terms of accuracy and efficiency with new image datasets via Transfer Learning, and the results are compared to some state-of-the-art approaches.
Proceedings ArticleDOI
Traffic Estimation And Prediction Based On Real Time Floating Car Data
TL;DR: This paper proposes two algorithms, respectively based on artificial neural networks and pattern-matching, designed to on-line perform short-term predictions of link travel speeds by using current and near-past link average speeds estimated by the OCTOTelematics FCD system.
Journal ArticleDOI
A probabilistic neural network for earthquake magnitude prediction
Hojjat Adeli,Ashif Panakkat +1 more
TL;DR: The PNN model presented in this paper complements the recurrent neural network model developed by the authors previously, where good results were reported for predicting earthquakes with magnitude greater than 6.0.
Journal ArticleDOI
Concrete Crack Detection by Multiple Sequential Image Filtering
TL;DR: A new robust automated image processing method for detecting cracks in surface images of concrete structures using genetic programming and elimination of residual noise after filtering and detection of indistinct cracks by iterative applications of the image filter to the local regions surrounding the cracks.
Journal ArticleDOI
Spatiotemporal Patterns of Urban Human Mobility
TL;DR: This study considers the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns and presents a simple mobility model for predicting peoples’ visited locations using the popularity of places in the city as an interaction parameter between different individuals.