Journal ArticleDOI
Emerging artificial intelligence methods in structural engineering
Hadi Salehi,Rigoberto Burgueño +1 more
TLDR
Techniques concerning applications of the noted AI methods in structural engineering developed over the last decade are summarized.About:
This article is published in Engineering Structures.The article was published on 2018-09-15. It has received 435 citations till now.read more
Citations
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Pattern Recognition and Machine Learning
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.
Journal ArticleDOI
A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications
TL;DR: This paper aims to fulfill the gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
Journal ArticleDOI
Roles of artificial intelligence in construction engineering and management: A critical review and future trends
Yue Pan,Limao Zhang +1 more
TL;DR: A systematic review under both scientometric and qualitative analysis is presented to present the current state of AI adoption in the context of CEM and discuss its future research trends.
Journal ArticleDOI
Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach
TL;DR: An intelligent approach based on the machine learning technique is proposed for predicting the compressive strength of concrete by employing the adaptive boosting algorithm to construct a strong learner by integrating several weak learners, which can find the mapping between the input data and output data.
Journal ArticleDOI
Machine learning prediction of mechanical properties of concrete: Critical review
TL;DR: Examination of several Machine Learning models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms are examined.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).