Civil structure condition assessment by a two-stage FE model update based on neural network enhanced power mode shapes and an adaptive roaming damage method

2020 ◽  
Vol 207 ◽  
pp. 110234 ◽  
Author(s):  
Ricardo Perera ◽  
Sean Sandercock ◽  
Alberto Carnicero
Keyword(s):  
Neural Network ◽  
Condition Assessment ◽  
Mode Shapes ◽  
Fe Model ◽  
Two Stage ◽  
Structure Condition ◽  
Model Update ◽  
Power Mode

scholarly journals Civil structure condition assessment by FE model updating:

2001 ◽  
Vol 37 (10) ◽  
pp. 761-775 ◽  
Author(s):  
J.M.W. Brownjohn ◽  
Pin-Qi Xia ◽  
Hong Hao ◽  
Yong Xia
Keyword(s):  
Model Updating ◽  
Condition Assessment ◽  
Fe Model ◽  
Structure Condition ◽  
Fe Model Updating

Urban Railway Bridge Fast Evaluation Based on Cloud Computing

2011 ◽  
Vol 71-78 ◽  
pp. 4501-4505
Author(s):  
Ming Chen ◽  
Wan Zhou
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Model Updating ◽  
Condition Assessment ◽  
Fe Model ◽  
Railway Bridge ◽  
Fast Evaluation ◽  
Railway Bridges ◽  
Computing Model ◽  
Bridge Condition Assessment

Although modern bridge are carefully designed and well constructed, damage may occur in them due to unexpected causes. Currently, many different techniques have been proposed and investigated in bridge condition assessment. However, evaluation efficiency of condition assessment has not been paid much attention by the researchers. A fast evaluation of the urban railway bridge condition based on the cloud computing is presented. In this paper dynamic FE model and Artificial neural networks technique is applied to model updating. The cloud computing model provides the basis for fast analyses. It was found that when applied to the actually railway bridges, the proposed method provided results similar to those obtained by experts, but can improve efficiency of bridge

scholarly journals A Two-Stage Neural Network-Based Cold Start Item Recommender

2021 ◽  
Vol 11 (9) ◽  
pp. 4243
Author(s):  
Chieh-Yuan Tsai ◽  
Yi-Fan Chiu ◽  
Yu-Jen Chen
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Service Providers ◽  
Cold Start ◽  
Computational Time ◽  
Two Stage ◽  
The Neural Network ◽  
Textual Description ◽  
Recommendation Algorithms ◽  
Content Information

Nowadays, recommendation systems have been successfully adopted in variant online services such as e-commerce, news, and social media. The recommenders provide users a convenient and efficient way to find their exciting items and increase service providers’ revenue. However, it is found that many recommenders suffered from the cold start (CS) problem where only a small number of ratings are available for some new items. To conquer the difficulties, this research proposes a two-stage neural network-based CS item recommendation system. The proposed system includes two major components, which are the denoising autoencoder (DAE)-based CS item rating (DACR) generator and the neural network-based collaborative filtering (NNCF) predictor. In the DACR generator, a textual description of an item is used as auxiliary content information to represent the item. Then, the DAE is applied to extract the content features from high-dimensional textual vectors. With the compact content features, a CS item’s rating can be efficiently derived based on the ratings of similar non-CS items. Second, the NNCF predictor is developed to predict the ratings in the sparse user–item matrix. In the predictor, both spare binary user and item vectors are projected to dense latent vectors in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user–item matrix learning. Finally, appropriate item suggestions can be accurately obtained. The extensive experiments show that the DAE can significantly reduce the computational time for item similarity evaluations while keeping the original features’ characteristics. Besides, the experiments show that the proposed NNCF predictor outperforms several popular recommendation algorithms. We also demonstrate that the proposed CS item recommender can achieve up to 8% MAE improvement compared to adding no CS item rating.

A two-stage design optimization framework for multifield origami-inspired structures

2021 ◽  
pp. 1045389X2110116
Author(s):  
Wei Zhang ◽  
Saad Ahmed ◽  
Jonathan Hong ◽  
Zoubeida Ounaies ◽  
Mary Frecker
Keyword(s):  
Case Studies ◽  
Computing Time ◽  
Computational Cost ◽  
High Fidelity ◽  
Fe Model ◽  
Two Stage ◽  
Baseline Design ◽  
Optimization Framework ◽  
Highly Nonlinear ◽  
Stage 1

Different types of active materials have been used to actuate origami-inspired self-folding structures. To model the highly nonlinear deformation and material responses, as well as the coupled field equations and boundary conditions of such structures, high-fidelity models such as finite element (FE) models are needed but usually computationally expensive, which makes optimization intractable. In this paper, a computationally efficient two-stage optimization framework is developed as a systematic method for the multi-objective designs of such multifield self-folding structures where the deformations are concentrated in crease-like areas, active and passive materials are assumed to behave linearly, and low- and high-fidelity models of the structures can be developed. In Stage 1, low-fidelity models are used to determine the topology of the structure. At the end of Stage 1, a distance measure [Formula: see text] is applied as the metric to determine the best design, which then serves as the baseline design in Stage 2. In Stage 2, designs are further optimized from the baseline design with greatly reduced computing time compared to a full FEA-based topology optimization. The design framework is first described in a general formulation. To demonstrate its efficacy, this framework is implemented in two case studies, namely, a three-finger soft gripper actuated using a PVDF-based terpolymer, and a 3D multifield example actuated using both the terpolymer and a magneto-active elastomer, where the key steps are elaborated in detail, including the variable filter, metrics to select the best design, determination of design domains, and material conversion methods from low- to high-fidelity models. In this paper, analytical models and rigid body dynamic models are developed as the low-fidelity models for the terpolymer- and MAE-based actuations, respectively, and the FE model of the MAE-based actuation is generalized from previous work. Additional generalizable techniques to further reduce the computational cost are elaborated. As a result, designs with better overall performance than the baseline design were achieved at the end of Stage 2 with computing times of 15 days for the gripper and 9 days for the multifield example, which would rather be over 3 and 2 months for full FEA-based optimizations, respectively. Tradeoffs between the competing design objectives were achieved. In both case studies, the efficacy and computational efficiency of the two-stage optimization framework are successfully demonstrated.

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

Straked Riser Design With VIVA

2010 ◽  
Author(s):  
Dhyanjyoti Deka ◽  
Paul R. Hays ◽  
Kamaldev Raghavan ◽  
Mike Campbell
Keyword(s):  
Traveling Waves ◽  
Oil And Gas ◽  
Cross Flow ◽  
Oil And Gas Industry ◽  
Response Prediction ◽  
Design Tool ◽  
Mode Shapes ◽  
Fe Model ◽  
Viv Suppression ◽  
Modal Solution

VIVA is a vortex induced vibration (VIV) analysis software that to date has not been widely used as a design tool in the offshore oil and gas industry. VIVA employs a hydrodynamic database that has been benchmarked and calibrated against test data [1]. It offers relatively few input variables reducing the risk of user induced variability of results [2]. In addition to cross flow current induced standing wave vibration, VIVA has the capability of predicting traveling waves on a subsea riser, or a combination of standing and traveling waves. Riser boundary conditions including fixed, pinned, flex joint or SCR seabed interaction can be modeled using springs and dashpots. VIVA calculates riser natural frequencies and mode shapes and also has the flexibility to import external modal solutions. In this paper, the applicability of VIVA for the design of straked steel catenary risers (SCR) and top tensioned risers (TTR) is explored. The use of linear and rotational springs provided by VIVA to model SCR soil interaction and flex joint articulation is evaluated. Comparisons of the VIV fatigue damage output with internal and external modal solution is presented in this paper. This paper includes validation of the VIVA generated modal solution by comparing the modal frequencies and curvatures against a finite element (FE) model of the risers. Fatigue life is calculated using long term Gulf of Mexico (GoM) currents and is compared against the industry standard software SHEAR7. Three different lift curve selections in SHEAR7 are used for this comparison. The differences in riser response prediction by the two software tools are discussed in detail. The sensitivity of the VIVA predicted riser response to the absence of VIV suppression devices is presented in this paper. The riser VIV response with and without external FE generated modal input is compared and the relative merits of the two modeling approaches are discussed. Finally, the recommended approach for VIVA usage for SCR and TTR design is given.

scholarly journals A Two-Stage Exon Recognition Model Based on Synergetic Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhehuang Huang ◽  
Yidong Chen
Keyword(s):  
Neural Network ◽  
Digital Signal ◽  
Two Stage ◽  
Recognition Model ◽  
Artificial Fish Swarm Algorithm ◽  
Proposed Model ◽  
Artificial Fish Swarm ◽  
Recognition Efficiency ◽  
Processing Techniques ◽  
Signal Processing Techniques

Exon recognition is a fundamental task in bioinformatics to identify the exons of DNA sequence. Currently, exon recognition algorithms based on digital signal processing techniques have been widely used. Unfortunately, these methods require many calculations, resulting in low recognition efficiency. In order to overcome this limitation, a two-stage exon recognition model is proposed and implemented in this paper. There are three main works. Firstly, we use synergetic neural network to rapidly determine initial exon intervals. Secondly, adaptive sliding window is used to accurately discriminate the final exon intervals. Finally, parameter optimization based on artificial fish swarm algorithm is used to determine different species thresholds and corresponding adjustment parameters of adaptive windows. Experimental results show that the proposed model has better performance for exon recognition and provides a practical solution and a promising future for other recognition tasks.

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