scholarly journals Feasibility of Using Neural Networks to Obtain Simplified Capacity Curves for Seismic Assessment

Buildings ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 151 ◽  
Author(s):  
João Estêvão
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Seismic Vulnerability ◽  
Seismic Assessment ◽  
Small Scale ◽  
Eurocode 8 ◽  
Seismic Behaviour ◽  
Capacity Curves ◽  
N2 Method ◽  
Building Typology

The selection of a given method for the seismic vulnerability assessment of buildings is mostly dependent on the scale of the analysis. Results obtained in large-scale studies are usually less accurate than the ones obtained in small-scale studies. In this paper a study about the feasibility of using Artificial Neural Networks (ANNs) to carry out fast and accurate large-scale seismic vulnerability studies has been presented. In the proposed approach, an ANN was used to obtain a simplified capacity curve of a building typology, in order to use the N2 method to assess the structural seismic behaviour, as presented in the Annex B of the Eurocode 8. Aiming to study the accuracy of the proposed approach, two ANNs with equal architectures were trained with a different number of vectors, trying to evaluate the ANN capacity to achieve good results in domains of the problem which are not well represented by the training vectors. The case study presented in this work allowed the conclusion that the ANN precision is very dependent on the amount of data used to train the ANN and demonstrated that it is possible to use ANN to obtain simplified capacity curves for seismic assessment purposes with high precision.

Seismic Vulnerability of Existing RC Buildings and Influence of the Decoupling of the Effective Masonry Panels from the Structural Frames

2012 ◽  
Vol 268-270 ◽  
pp. 646-655
Author(s):  
Fabio de Angelis ◽  
Donato Cancellara
Keyword(s):  
Reinforced Concrete ◽  
Seismic Vulnerability ◽  
Seismic Analysis ◽  
Effective Interaction ◽  
Collapse Mechanism ◽  
Limit States ◽  
Capacity Curves ◽  
Masonry Infills ◽  
N2 Method ◽  
Structural Frame

In the present work we discuss on the seismic vulnerability of reinforced concrete existing buildings. In particular we consider a reinforced concrete building originally designed for only gravitational loads and located in a zone recently defined at seismic risk. According to the Italian seismic code NTC 2008 a displacement based approach is adopted and the N2-method is considered for the nonlinear seismic analysis. In the analysis all the masonry infill panels in effective interaction with the structural frame are considered for the nonlinear modeling of the structure. The influence of the effective masonry infills on the seismic response of the structure is analyzed and it is discussed how the effect of the masonry infills irregularly located within the building can give rise to a worsening of the seismic performance of the structure. It is shown that in the present case a not uniform positioning of the masonry infills within the building can give rise to a fragile structural behavior in the collapse mechanism. Furthermore a comparative analysis is performed by considering both the structure with the effective masonry infills and the bare structural frame. For these two structures a pushover analysis is performed, the relative capacity curves are derived and it is shown that fragile collapse mechanisms can occur depending on the irregular positioning of the effective masonry infills. Accordingly it is discussed how in the present case a decoupling of the effective masonry infills from the structural frame can give rise to a smoother response of the capacity curves. For the examined case of an obsolete building with irregular positioning of the masonry panels, the choice of decoupling the effective masonry panels from the structural frame may facilitate the retrofitting strategies for the achievement of the proper safety factors at the examined limit states.

scholarly journals Extensive deep neural networks for transferring small scale learning to large scale systems

2019 ◽  
Vol 10 (15) ◽  
pp. 4129-4140 ◽  
Author(s):  
Kyle Mills ◽  
Kevin Ryczko ◽  
Iryna Luchak ◽  
Adam Domurad ◽  
Chris Beeler ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Small Scale ◽  
Large Scale Systems ◽  
Energy Entropy ◽  
Large Systems ◽  
Number Of Particles

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with scaling.

Seismic Vulnerability of Existing RC Buildings and Influence of the Decoupling of the Effective Masonry Panels from the Structural Frames

2012 ◽  
Vol 256-259 ◽  
pp. 2244-2253 ◽  
Author(s):  
Fabio de Angelis ◽  
Donato Cancellara
Keyword(s):  
Reinforced Concrete ◽  
Seismic Vulnerability ◽  
Seismic Analysis ◽  
Effective Interaction ◽  
Collapse Mechanism ◽  
Limit States ◽  
Capacity Curves ◽  
Masonry Infills ◽  
N2 Method ◽  
Structural Frame

In the present work we discuss on the seismic vulnerability of reinforced concrete existing buildings. In particular we consider a reinforced concrete building originally designed for only gravitational loads and located in a zone recently defined at seismic risk. According to the Italian seismic code NTC 2008 a displacement based approach is adopted and the N2-method is considered for the nonlinear seismic analysis. In the analysis all the masonry infill panels in effective interaction with the structural frame are considered for the nonlinear modeling of the structure. The influence of the effective masonry infills on the seismic response of the structure is analyzed and it is discussed how the effect of the masonry infills irregularly located within the building can give rise to a worsening of the seismic performance of the structure. It is shown that in the present case a not uniform positioning of the masonry infills within the building can give rise to a fragile structural behavior in the collapse mechanism. Furthermore a comparative analysis is performed by considering both the structure with the effective masonry infills and the bare structural frame. For these two structures a pushover analysis is performed, the relative capacity curves are derived and it is shown that fragile collapse mechanisms can occur depending on the irregular positioning of the effective masonry infills. Accordingly it is discussed how in the present case a decoupling of the effective masonry infills from the structural frame can give rise to a smoother response of the capacity curves. For the examined case of an obsolete building with irregular positioning of the masonry panels, the choice of decoupling the effective masonry panels from the structural frame may facilitate the retrofitting strategies for the achievement of the proper safety factors at the examined limit states.

scholarly journals Sense Element Engagement Theory Explains How Neural Networks Produce Cortical Prosthetic Vision

2021 ◽  
Author(s):  
Raymond Pavloski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Large Scale ◽  
Small Scale ◽  
Engagement Theory ◽  
Sense Element ◽  
Neural Interactions ◽  
Network Simulations ◽  
New Research ◽  
Parameter Values

<p>Demonstrating that an understanding of how neural networks produce a specific quality of experience has been achieved would provide a foundation for new research programs and neurotechnologies. The phenomena that comprise cortical prosthetic vision have two desirable properties for the pursuit of this goal: 1) Models of the subjective qualities of cortical prosthetic vision can be constructed; and 2) These models can be related in a natural way to models of the objective aspects of cortical prosthetic vision. Sense element engagement theory portrays the qualities of cortical prosthetic vision together with coordinated objective neural phenomena as constituting sensible spatiotemporal patterns that are produced by neural interactions. Small-scale neural network simulations are used to illustrate how these patterns are thought to arise. It is proposed that simulations and an electronic neural network (ENN) should be employed in devising tests of the theory. Large-scale simulations can provide estimates of parameter values that are required to construct an ENN. The ENN will be used to develop a prosthetic device that is predicted by the theory to produce visual forms in a novel fashion. According to the theory, confirmation of this prediction would also provide evidence that this ENN is a sentient device.</p>

scholarly journals Blocking-based Neighbor Sampling for Large-scale Graph Neural Networks

2021 ◽  
Author(s):  
Kai-Lang Yao ◽  
Wu-Jun Li
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Sampling Strategy ◽  
Unbiased Estimation ◽  
Small Scale ◽  
Original Graph ◽  
Exponential Increase ◽  
Benchmark Datasets ◽  
Graph Neural Networks ◽  
Memory Complexity

The exponential increase in computation and memory complexity with the depth of network has become the main impediment to the successful application of graph neural networks (GNNs) on large-scale graphs like graphs with hundreds of millions of nodes. In this paper, we propose a novel neighbor sampling strategy, dubbed blocking-based neighbor sampling (BNS), for efficient training of GNNs on large-scale graphs. Specifically, BNS adopts a policy to stochastically block the ongoing expansion of neighboring nodes, which can reduce the rate of the exponential increase in computation and memory complexity of GNNs. Furthermore, a reweighted policy is applied to graph convolution, to adjust the contribution of blocked and non-blocked neighbors to central nodes. We theoretically prove that BNS provides an unbiased estimation for the original graph convolution operation. Extensive experiments on three benchmark datasets show that, on large-scale graphs, BNS is 2X~5X faster than state-of-the-art methods when achieving the same accuracy. Moreover, even on the small-scale graphs, BNS also demonstrates the advantage of low time cost.

scholarly journals Deep Forest: Towards An Alternative to Deep Neural Networks

2017 ◽  
Author(s):  
Zhi-Hua Zhou ◽  
Ji Feng
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Parallel Implementation ◽  
Parameter Tuning ◽  
Training Data ◽  
Small Scale ◽  
Great Effort ◽  
Training Cost ◽  
Deep Forest

In this paper, we propose gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. In contrast to deep neural networks which require great effort in hyper-parameter tuning, gcForest is much easier to train; even when it is applied to different data across different domains in our experiments, excellent performance can be achieved by almost same settings of hyper-parameters. The training process of gcForest is efficient, and users can control training cost according to computational resource available. The efficiency may be further enhanced because gcForest is naturally apt to parallel implementation. Furthermore, in contrast to deep neural networks which require large-scale training data, gcForest can work well even when there are only small-scale training data.

scholarly journals Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 51 ◽  
Author(s):  
Qingge Ji ◽  
Jie Huang ◽  
Wenjie He ◽  
Yankui Sun
Keyword(s):  
Neural Networks ◽  
Optical Coherence Tomography ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Natural Images ◽  
Small Scale ◽  
Optical Coherence ◽  
Deep Convolutional Neural Networks ◽  
Macular Diseases ◽  
High Level

Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves their performance on retinal optical coherence tomography (OCT) images. Deep features of pre-trained DNN are high-level features of natural images. These features harm the training of transfer learning. Our strategy is to remove some deep convolutional layers of the state-of-the-art pre-trained networks: GoogLeNet, ResNet and DenseNet. We try to find the optimized deep neural networks on small-scale and large-scale OCT datasets, respectively, in our experiments. Results show that optimized deep neural networks not only reduce computational burden, but also improve classification accuracy.

scholarly journals Sense Element Engagement Theory Explains How Neural Networks Produce Cortical Prosthetic Vision

2021 ◽  
Author(s):  
Raymond Pavloski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Large Scale ◽  
Small Scale ◽  
Engagement Theory ◽  
Sense Element ◽  
Neural Interactions ◽  
Network Simulations ◽  
New Research ◽  
Parameter Values

<p>Demonstrating that an understanding of how neural networks produce a specific quality of experience has been achieved would provide a foundation for new research programs and neurotechnologies. The phenomena that comprise cortical prosthetic vision have two desirable properties for the pursuit of this goal: 1) Models of the subjective qualities of cortical prosthetic vision can be constructed; and 2) These models can be related in a natural way to models of the objective aspects of cortical prosthetic vision. Sense element engagement theory portrays the qualities of cortical prosthetic vision together with coordinated objective neural phenomena as constituting sensible spatiotemporal patterns that are produced by neural interactions. Small-scale neural network simulations are used to illustrate how these patterns are thought to arise. It is proposed that simulations and an electronic neural network (ENN) should be employed in devising tests of the theory. Large-scale simulations can provide estimates of parameter values that are required to construct an ENN. The ENN will be used to develop a prosthetic device that is predicted by the theory to produce visual forms in a novel fashion. According to the theory, confirmation of this prediction would also provide evidence that this ENN is a sentient device.</p>

Small-Scale Science to Large-Scale Profession

2000 ◽  
Vol 45 (4) ◽  
pp. 396-398
Author(s):  
Roger Smith
Keyword(s):  
Large Scale ◽  
Small Scale
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