Journal ArticleDOI
Machine learning for structural engineering: A state-of-the-art review
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TLDR
An overview of ML techniques for structural engineering is presented in this article with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets.About:
This article is published in Structures.The article was published on 2022-04-01. It has received 89 citations till now. The article focuses on the topics: Computer science & Structural health monitoring.read more
Citations
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A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings
TL;DR: In this paper , an Artificial Neural Network (ANN)-based model was developed to predict risk priorities for reinforced-concrete (RC) buildings that constitute a large part of the existing building stock.
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Prediction of the load-shortening curve of CFST columns using ANN-based models
TL;DR: In this paper , a novel approach to predict and plot the complete axial load-shortening curve of concentrically loaded rectangular and circular CFST columns using ANN method is presented.
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Partially online damage detection using long-term modal data under severe environmental effects by unsupervised feature selection and local metric learning
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Revealing the nonlinear behavior of steel flush endplate connections using ANN-based hybrid models
Viet-Linh Tran,Jin-Kook Kim +1 more
TL;DR: Wang et al. as discussed by the authors proposed a hybrid ANN-HPO model, which is made by integrating artificial neural networks (ANN) and hunter-prey optimization (HPO), for predicting the moment rotation (M-θ) behavior of steel flush endplate (FEP) connections.
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Probabilistic data self-clustering based on semi-parametric extreme value theory for structural health monitoring
TL;DR: In this article , the authors proposed a probabilistic data self-clustering method for damage detection of large-scale civil structures by getting an idea from semi-parametric extreme value theory.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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