Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks
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In this paper, the authors use accurate and efficient means to evaluate system reliability again and optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, and it is essential to use accurate, efficient, and accurate means for evaluating system reliability.Abstract:
To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability again...read more
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Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization
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Vibration‐based structural state identification by a 1‐dimensional convolutional neural network
TL;DR: In this paper, a vibration-based structural damage detection by CNNs is presented. But the vibration is not considered in the analysis of the CNNs' performance in detecting structural damage.
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Deep neural network with high-order neuron for the prediction of foamed concrete strength
TL;DR: A new, high‐order neuron was developed for the deep neural network model to improve the performance and the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model.
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Generative adversarial network for road damage detection
TL;DR: Combining a progressive growing GAN along with Poisson blending artificially generates road damage images that can be used as new training data to improve the accuracy of road damage detection and the new Road Damage Dataset 2019 is released.
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Concrete crack detection using context‐aware deep semantic segmentation network
TL;DR: A novel context‐aware deep convolutional semantic segmentation network is presented to effectively detect cracks in structural infrastructure under various conditions to segment the cracks on images with arbitrary sizes without retraining the prediction network.
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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.
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TL;DR: XSEDE's integrated, comprehensive suite of advanced digital services federates with other high-end facilities and with campus-based resources, serving as the foundation for a national e-science infrastructure ecosystem.
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Surrogate-based Analysis and Optimization
Nestor V. Queipo,Raphael T. Haftka,Wei Shyy,Tushar Goel,Rajkumar Vaidyanathan,P. Kevin Tucker +5 more
TL;DR: The multi-objective optimal design of a liquid rocket injector is presented to highlight the state of the art and to help guide future efforts.
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The energy release in great earthquakes
TL;DR: In this paper, a new magnitude scale M_w is defined in terms of W_0 through the standard energy-magnitude relation log W_ 0 = 1.5M_w + 11.8.
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Assessing the accuracy of prediction algorithms for classification: an overview
TL;DR: A unified overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, and correlation coefficients, and to information theoretic measures such as relative entropy and mutual information are provided.