Modeling Vehicles’ Offset Impacts Using Recurrent Artificial Neural Networks

2000 ◽  
Author(s):  
Tarek A. Omar ◽  
Azim Eskandarian ◽  
Nabih E. Bedewi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Dynamic ◽  
Numerical Examples ◽  
Fe Simulations ◽  
First Case ◽  
Highly Nonlinear ◽  
Vehicle Structure ◽  
Artificial Neural ◽  
The Impact

Abstract In the last few years, the demand for general-purpose Finite Element (FE) vehicle models with fine mesh and small elements has increased the size of these models dramatically. The FE simulation of these models requires extensive CPU time, which makes the simulation cost an important issue to consider. The main objective of this research is to develop an accurate and computationally inexpensive method to predict a vehicle’s crash performance in the event of a collision. This becomes very important as the demand for performing several impact scenarios for each vehicle becomes excessive. This demand is driven by the desire to investigate different impact scenarios and to study the effect of the impact velocity, the offset-barrier ratio, and the impact angle on the dynamic behavior of the vehicle structure in crash events. In the last decade, Artificial Neural Networks (ANN) emerged as a reliable tool for solving nonlinear problems in variety of applications. The most important feature in ANNs is its ability to infer the nonlinear characteristics of any complex system, even if the mathematical model of the system does not exist. This is an extremely important feature when dealing with highly nonlinear dynamic problems such as vehicles collision. In a previous research conducted by the authors, advanced ANNs were developed and trained to model vehicles frontal impacts. This paper extends the concept and technique in order to use ANNs in modeling vehicles offset-barrier impacts. Special ANNs were developed, trained and tested through numerical examples for two different offset impact cases. The first case was 50% offset-barrier impact at five different impact velocities, while the second case was 35 mph frontal impact at five different offset-barrier ratios. Validated FE vehicle model was used to perform FE simulations for many different offset-barrier impacts. The crash profiles obtained from the FE simulations were used to train and test the developed ANNs. The results of these numerical examples indicated the ability of the ANNs to accurately capture the nonlinear dynamic characteristics of the vehicle structure for offset impacts. The trained networks could then be used to predict the crash profiles of any offset impact scenario within the training range.

scholarly journals Atmospheric Pollutant Flow and Precipitation: Modeling Effects on the Vegetation Ecosystem

2018 ◽  
Vol 7 (3) ◽  
pp. 157-161
Author(s):  
Allag Fateh ◽  
Saddek Bouharati ◽  
Lamri Tedjar ◽  
Mohamed Fenni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Wide Distribution ◽  
Climatic Conditions ◽  
Mathematical Methods ◽  
Atmospheric Pollutant ◽  
Artificial Neural ◽  
Precipitation Modeling ◽  
The Impact

Because of their fixed life and wide distribution, plants are the first victims of air pollution. The atmosphere is considered polluted when the increase of the rate of certain components causes harmful effects on the different constituents of the ecosystems. The study of the flow of air near a polluting source (cement plant in our case), allows to predict its impact on the surrounding plant ecosystem. Different factors are to be considered. The chemical composition of the air, the climatic conditions, and the impacted plant species are complex parameters to be analyzed using conventional mathematical methods. In this study, we propose a system based on artificial neural networks. Since artificial neural networks have the capacity to treat different complex parameters, their application in this domain is adequate. The proposed system makes it possible to match the input and output spaces. The variables that constitute the input space are the chemical composition, the concentration of the latter in the rainwater, their duration of deposition on the leaves and stems, the climatic conditions characterizing the environment, as well as the species of plant studied. The output variable expresses the rate of degradation of this species under the effect of pollution. Learning the system makes it possible to establish the transfer function and thus predict the impact of pollutants on the vegetation.

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

2018 ◽  
Vol 235 ◽  
pp. 394-403 ◽  
Author(s):  
Gabriela Polezer ◽  
Yara S. Tadano ◽  
Hugo V. Siqueira ◽  
Ana F.L. Godoi ◽  
Carlos I. Yamamoto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Respiratory Disease ◽  
Artificial Neural ◽  
The Impact

scholarly journals Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

2017 ◽  
Vol 14 (18) ◽  
pp. 4101-4124 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Bin Fang ◽  
Alexandra G. Konings ◽  
Filipe Aires ◽  
Julia K. Green ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Eddy Covariance ◽  
Latent Heat ◽  
Gross Primary Production ◽  
Turbulent Fluxes ◽  
Uncertainty Estimates ◽  
Artificial Neural ◽  
The Impact

Abstract. A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1°  ×  1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.

Artificial neural networks for predicting nonlinear dynamic helicopter loads

AIAA Journal ◽  
1994 ◽  
Vol 32 (5) ◽  
pp. 1072-1077 ◽  
Author(s):  
A. B. Cook ◽  
C. R. Fuller ◽  
W. F. O'Brien ◽  
R. H. Cabell
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Dynamic ◽  
Artificial Neural

scholarly journals Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes using solar-induced fluorescence

2016 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Bin Fang ◽  
Alexandra G. Konings ◽  
Julia K. Green ◽  
Jana Kolassa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Latent Heat ◽  
Gross Primary Production ◽  
Sensible Heat Flux ◽  
Turbulent Fluxes ◽  
Input Dataset ◽  
Artificial Neural ◽  
The Impact

Abstract. A new global estimate of surface turbulent fluxes, including latent heat flux (LE), sensible heat flux (H), and gross primary production (GPP) is developed using remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. The approach uses an artificial neural network (ANN) with a Bayesian perspective to learn from the training datasets: a target input dataset is generated using three independent data sources and a triple collocation (TC) algorithm to define a prior distribution. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides surface turbulent fluxes from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are validated using FLUXNET tower measurements across various climates and conditions. WECANN performs well in most cases and is strongly constrained by SIF information. The impact of SIF on WECANN retrievals is evaluated by removing it from the input dataset of the ANN, and it shows that SIF has significant influence, especially in regions of high vegetation cover and in humid conditions. When compared to in situ eddy covariance observations, WECANN typically outperforms other estimates, particularly for sensible and latent heat fluxes.

scholarly journals Approximation of the process of changes in deformation of land surface using artificial neural networks

2018 ◽  
Vol 55 ◽  
pp. 00009
Author(s):  
Maria Mrówczyńska ◽  
Jacek Sztubecki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Land Surface ◽  
Reference System ◽  
Surface Deformation ◽  
Copper Mining ◽  
Vertical Displacements ◽  
Artificial Neural ◽  
The Stability ◽  
The Impact

Artificial neural networks are an interesting method for modelling phenomena, including spatial phenomena, which are difficult to describe with known mathematical models. The properties of neural networks enable their practical application for solving such problems as: approximation, interpolation, identification and classification of patterns, compression, prediction, etc. The article presents the use of multilayer feedforward artificial neural networks for describing the process of changes in land surface deformation in the area of the Legnica-Głogów Copper Mining Centre, located in the southern part of the Fore Sudetic Monocline. Results provided by geodesic monitoring, which consists of land surveying and interpreting data obtained in this way, are undoubtedly significant in terms of identifying the impact of mining on the land surface the results of measurements carried out by precise levelling in the years 19672014 were used to determine changes in land deformation in the Legnica-Głogów Copper Mining Centre. The concept of a flexible reference system was used to assess the stability of points in the measurement and control network stabilized in order to determine vertical displacements. However, the reference system itself was identified on the basis of the critical value of the increment of the square of the norm of corrections to the observations.

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