scholarly journals Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model

2020 ◽  
Vol 10 (18) ◽  
pp. 6476
Author(s):  
Sungsik Yoon ◽  
Jeongseob Kim ◽  
Minsun Kim ◽  
Hye-Young Tak ◽  
Young-Joo Lee
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Surrogate Model ◽  
Seismic Risk ◽  
Transportation Networks ◽  
Training Data ◽  
System Level ◽  
High Dimensional ◽  
Seismic Risk Assessment

In this study, an artificial neural network (ANN)-based surrogate model is proposed to evaluate the system-level seismic risk of bridge transportation networks efficiently. To estimate the performance of a network, total system travel time (TSTT) was introduced as a performance index, and an ANN-based surrogate model was incorporated to evaluate a high-dimensional network with probabilistic seismic hazard analysis (PSHA) efficiently. To generate training data, the damage states of bridge components were considered as the input training data, and TSTT was selected as output data. An actual bridge transportation network in South Korea was considered as the target network, and the entire network map was reconstructed based on geographic information system data to demonstrate the proposed method. For numerical analysis, the training data were generated based on epicenter location history. By using the surrogate model, the network performance was estimated for various earthquake magnitudes at the trained epicenter with significantly-reduced computational time cost. In addition, 20 historical epicenters were adopted to confirm the robustness of the epicenter. Therefore, it was concluded that the proposed ANN-based surrogate model could be used as an alternative for efficient system-level seismic risk assessment of high-dimensional bridge transportation networks.

scholarly journals System-Level Seismic Risk Assessment of Bridge Transportation Networks Employing Probabilistic Seismic Hazard Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Hye-Young Tak ◽  
Wonho Suh ◽  
Young-Joo Lee
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Transportation Networks ◽  
Hazard Analysis ◽  
Transportation Network ◽  
System Level ◽  
Seismic Fragility ◽  
Probabilistic Seismic Hazard ◽  
Seismic Risk Assessment ◽  
The Matrix

Earthquakes can have significant impacts on transportation networks because of the physical damage they can cause to bridges. Hence, it is essential to assess the seismic risk of a bridge transportation network accurately. However, this is a challenging task because it requires estimating the performance of a bridge transportation network at the system level. Moreover, it is necessary to deal with various possible earthquake scenarios and the associated damage states of component bridges considering the uncertainty of earthquake locations and magnitudes. To overcome these challenges, this study proposes a new method of system-level seismic risk assessment for bridge transportation networks employing probabilistic seismic hazard analysis (PSHA). The proposed method consists of three steps: (1) seismic fragility estimation of the bridges based on PSHA; (2) system-level performance estimation using a matrix-based framework; and (3) seismic risk assessment based on the total probability theorem. In the proposed method, PSHA enables the seismic fragility estimation of the component bridges considering the uncertainty of earthquake locations and magnitudes, and it is systemically used to carry out a posthazard bridge network flow capacity analysis by employing the matrix-based framework. The proposed method provides statistical moments of the network performance and component importance measures, which can be used by decision makers to reduce the seismic risk of a target area. To test the proposed method, it is applied to a numerical example of an actual transportation network in South Korea. In the seismic risk assessment of the example, PSHA is successfully integrated with the matrix-based framework to perform system reliability analysis in a computationally efficient manner.

An Assessment of Machine Learning Techniques for Predicting Turbine Airfoil Component Temperatures, Using FEA Simulations for Training Data

2019 ◽  
Author(s):  
James A. Tallman ◽  
Michal Osusky ◽  
Nick Magina ◽  
Evan Sewall
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Surrogate Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Artificial Neural

Abstract This paper provides an assessment of three different machine learning techniques for accurately reproducing a distributed temperature prediction of a high-pressure turbine airfoil. A three-dimensional Finite Element Analysis thermal model of a cooled turbine airfoil was solved repeatedly (200 instances) for various operating point settings of the corresponding gas turbine engine. The response surface created by the repeated solutions was fed into three machine learning algorithms and surrogate model representations of the FEA model’s response were generated. The machine learning algorithms investigated were a Gaussian Process, a Boosted Decision Tree, and an Artificial Neural Network. Additionally, a simple Linear Regression surrogate model was created for comparative purposes. The Artificial Neural Network model proved to be the most successful at reproducing the FEA model over the range of operating points. The mean and standard deviation differences between the FEA and the Neural Network models were 15% and 14% of a desired accuracy threshold, respectively. The Digital Thread for Design (DT4D) was used to expedite all model execution and machine learning training. A description of DT4D is also provided.

Seismic Risk Assessment of Bridges in Jakarta Transportation Networks using Incremental Dynamic Analysis

2017 ◽  
Vol 109 (4) ◽  
pp. 3394-3401
Author(s):  
Mutiara Puspahati Cripstyani ◽  
Ireng Guntorojati ◽  
Dimas Pramudya ◽  
S.A Kristiawan ◽  
Senot Sangadji
Keyword(s):  
Risk Assessment ◽  
Dynamic Analysis ◽  
Seismic Risk ◽  
Transportation Networks ◽  
Incremental Dynamic Analysis ◽  
Seismic Risk Assessment

scholarly journals Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models

2020 ◽  
Vol 2 (1) ◽  
pp. 1-19
Author(s):  
Kaixu Yang ◽  
Tapabrata Maiti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Feature Vector ◽  
Single Layer ◽  
Training Data ◽  
Classification Model ◽  
High Dimensional ◽  
Neural Network Models ◽  
Artificial Neural

An artificial neural network (ANN) is an automatic way of capturing linear and nonlinear correlations, spatial and other structural dependence among features. This machine performs well in many application areas such as classification and prediction from magnetic resonance imaging, spatial data and computer vision tasks. Most commonly used ANNs assume the availability of large training data compared to the dimension of feature vector. However, in modern applications, as mentioned above, the training sample sizes are often low, and may be even lower than the dimension of feature vector. In this paper, we consider a single layer ANN classification model that is suitable for analyzing high-dimensional low sample-size (HDLSS) data. We investigate the theoretical properties of the sparse group lasso regularized neural network and show that under mild conditions, the classification risk converges to the optimal Bayes classifier’s risk (universal consistency). Moreover, we proposed a variation on the regularization term. A few examples in popular research fields are also provided to illustrate the theory and methods.

Seismic risk assessment of liquid storage tanks via a nonlinear surrogate model

2017 ◽  
Vol 46 (15) ◽  
pp. 2851-2868 ◽  
Author(s):  
Konstantinos Bakalis ◽  
Dimitrios Vamvatsikos ◽  
Michalis Fragiadakis
Keyword(s):  
Risk Assessment ◽  
Surrogate Model ◽  
Seismic Risk ◽  
Storage Tanks ◽  
Seismic Risk Assessment ◽  
Liquid Storage
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