Author
Heng Liu
Other affiliations: Federal Highway Administration
Bio: Heng Liu is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Deep learning & National Bridge Inventory. The author has an hindex of 5, co-authored 8 publications receiving 80 citations. Previous affiliations of Heng Liu include Federal Highway Administration.
Papers
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63 citations
TL;DR: A deep learning based structural steel damage condition assessment method that uses images for post-hazard inspection of ultra-low cycle fatigue induced damage in structural steel fuse members and a saliency-based visualization method is employed to visualize the feature-related pattern recognized by the deep learning model.
51 citations
TL;DR: Research findings suggest that the deep learning model offers a promising tool as a data-driven condition forecasting approach for bridge components with a demonstrated prediction accuracy over 85%.
Abstract: This paper presents a deep learning-based bridge condition rating data modeling approach using selected data from the National Bridge Inventory (NBI) database. The objective of this research is to ...
45 citations
TL;DR: In this article, the results of experimental and numerical simulation study of replaceable cast steel link beams under cyclic loading, including the ductility, strength, stiffness and energy dissipation of seven shear link specimens with and without circular perforations were presented.
15 citations
TL;DR: A deep learning-based damage detection procedure using acceleration data is proposed as an automated post-hazard inspection tool for rapid structural condition assessment in concentrically braced frame structures after earthquakes.
Abstract: Automated and robust damage detection tool is needed to enhance the resilience of civil infrastructures. In this article, a deep learning-based damage detection procedure using acceleration data is...
10 citations
Cited by
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
TL;DR: The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed.
Abstract: Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.
232 citations
TL;DR: A deep learning-based axial capacity prediction for cold-formed steel channel sections is developed using Deep Belief Network and it was found that the DBN was conservative by 9%, 6% and 8% for stub columns, intermediate columns, and slender columns, respectively.
63 citations
63 citations
TL;DR: Based on DBN prediction data, a comprehensive reliability analysis was conducted, which shows the proposed equations can predict the enhanced and reduced axial capacity of CFS channel sections with edge-Stiffened/un-stiffened web holes accurately.
Abstract: This paper proposes a framework of deep belief network (DBN) for studying the structural performance of cold-formed steel (CFS) channel sections with edge-stiffened/un-stiffened web holes, under axial compression. A total of 50,000 data points for training the DBN are generated from elasto plastic finite element analysis, which incorporates both initial geometric imperfections and residual stresses. A comparison against 23 experimental results was conducted, and it was found that the DBN predictions were conservative by 3% for columns with un-stiffened web holes, and 8% for columns with edge-stiffened web holes. When compared with Backpropagation Neural Network (a typical shallow artificial neural network) and linear regression model based on PaddlePaddle, it was found that the proposed DBN outperformed better than both the methods, using the same big training data used in this paper. When the same comparison was made for Effective Width Method and Direct Strength Method, the results from them were conservative by 5% and 12% against the experimental results, respectively for columns with un-stiffened web holes. Hole effects on the structural performance of channel sections under axial compression were also investigated. Based on the DBN output data, design recommendations of axial capacity enhancement/reduction factors were given for columns (stub, intermediate and slender) with edge-stiffened/un-stiffened web holes. Based on DBN prediction data, a comprehensive reliability analysis was conducted, which shows the proposed equations can predict the enhanced and reduced axial capacity of CFS channel sections with edge-stiffened/un-stiffened web holes accurately.
60 citations