scholarly journals Development of an ANN-Based Lumped Plasticity Model of RC Columns Using Historical Pseudo-Static Cyclic Test Data

2019 ◽  
Vol 9 (20) ◽  
pp. 4263 ◽  
Author(s):  
Zhenliang Liu ◽  
Suchao Li
Keyword(s):  
Test Data ◽  
Cyclic Test ◽  
Critical Parameters ◽  
Model Parameters ◽  
Ann Model ◽  
Rc Columns ◽  
Ultimate Deformation ◽  
Lp Model ◽  
Rc Frame Structures ◽  
Lumped Plasticity

This study explores the possibility of using an ANN-based model for the rapid numerical simulation and seismic performance prediction of reinforced concrete (RC) columns. The artificial neural network (ANN) method is implemented to model the relationship between the input features of RC columns and the critical parameters of the commonly used lumped plasticity (LP) model: The strength and the yielding, capping and ultimate deformation capacity. Cyclic test data of 1163 column specimens obtained from the PEER and NEEShub database and other sources are collected and divided into the training set, test set and validation set for the ANN model. The effectiveness of the proposed ANN model is validated by comparing it with existing explicit formulas and experimental results. Results indicated that the developed model can effectively predict the strength and deformation capacities of RC columns. Furthermore, the response of two RC frame structures under static force and strong ground motion were simulated by the ANN-based, bi-linear and tri-linear LP model method. The good agreement between the proposed model and test results validated that the ANN-based method can provide sufficiently accurate model parameters for modeling the seismic response of RC columns using the LP model.

scholarly journals Mining Camera Traces to Estimate Interactions Between Healthcare Workers and Patients

2020 ◽  
Vol 41 (S1) ◽  
pp. s12-s12
Author(s):  
D. M. Hasibul Hasan ◽  
Philip Polgreen ◽  
Alberto Segre ◽  
Jacob Simmering ◽  
Sriram Pemmaraju
Keyword(s):  
Time Of Day ◽  
Microsoft Kinect ◽  
Critical Parameters ◽  
Model Parameters ◽  
Patient Room ◽  
University Of Iowa ◽  
Velocity Change ◽  
Fine Grained ◽  
Visit Frequency ◽  
Contact Patterns

Background: Simulations based on models of healthcare worker (HCW) mobility and contact patterns with patients provide a key tool for understanding spread of healthcare-acquired infections (HAIs). However, simulations suffer from lack of accurate model parameters. This research uses Microsoft Kinect cameras placed in a patient room in the medical intensive care unit (MICU) at the University of Iowa Hospitals and Clinics (UIHC) to obtain reliable distributions of HCW visit length and time spent by HCWs near a patient. These data can inform modeling efforts for understanding HAI spread. Methods: Three Kinect cameras (left, right, and door cameras) were placed in a patient room to track the human body (ie, left/right hands and head) at 30 frames per second. The results reported here are based on 7 randomly selected days from a total of 308 observation days. Each tracked body may have multiple raw segments over the 2 camera regions, which we “stitch” up by matching features (eg, direction, velocity, etc), to obtain complete trajectories. Due to camera noise, in a substantial fraction of the frames bodies display unnatural characteristics including frequent and rapid directional and velocity change. We use unsupervised learning techniques to identify such “ghost” frames and we remove from our analysis bodies that have 20% or more “ghost” frames. Results: The heat map of hand positions (Fig. 1) shows that high-frequency locations are clustered around the bed and more to the patient’s right in accordance with the general medical practice of performing patient exams from their right. HCW visit frequency per hour (mean, 6.952; SD, 2.855) has 2 peaks, 1 during morning shift and 1 during the afternoon shift, with a distinct decrease after midnight. Figure 2 shows visit length (in minutes) distribution (mean, 1.570; SD, 2.679) being dominated by “check in visits” of <30 seconds. HCWs do not spend much time at touching distance from patients during short-length visits, and the fraction of time spent near the patient’s bed seems to increase with visit length up to a point. Conclusions: Using fine-grained data, this research extracts distributions of these critical parameters of HCW–patient interactions: (1) HCW visit length, (2) HCW visit frequency as a function of time of day, and (3) time spent by HCW within touching distance of patient as a function of visit length. To the best of our knowledge, we provide the first reliable estimates of these parameters.Funding: NoneDisclosures: None

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

scholarly journals Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Panagiotis G. Asteris ◽  
Athanasios K. Tsaris ◽  
Liborio Cavaleri ◽  
Constantinos C. Repapis ◽  
Angeliki Papalou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fundamental Period ◽  
Critical Parameters ◽  
Frame Structures ◽  
Rc Frame ◽  
Infill Walls ◽  
Rc Structures ◽  
Artificial Neural ◽  
Rc Frame Structures

The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

scholarly journals Flutter Test Data Processing Based on Improved Hilbert-Huang Transform

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hua Zheng ◽  
Junhao Liu ◽  
Shiqiang Duan
Keyword(s):  
Data Processing ◽  
Test Data ◽  
Model Parameters ◽  
Modal Parameter ◽  
Hilbert Huang Transform ◽  
Modal Density ◽  
Flutter Boundary ◽  
Modal Parameter Estimation ◽  
Nonstationary Conditions ◽  
Typical Mode

Flutter tests are conducted primarily for the purpose of modal parameter estimation and flutter boundary prediction, the accuracy of which is severely affected by the acquired data quality, structural modal density, and nonstationary conditions. An improved Hilbert-Huang Transform (HHT) algorithm is presented in this paper which mitigates the typical mode mixing effect via modulation. The algorithm is validated by theory, by numerical simulation, and per actual flight flutter test data. The results show that the proposed method could extract the flutter model parameters and predict the flutter speed more accurately, which is feasible for the current flutter test data processing.

scholarly journals Lateral shear strength of rectangular RC columns subjected to combined P-V-M monotonic loading

2020 ◽  
Vol 53 (4) ◽  
pp. 227-241
Author(s):  
Aysha M Zaneeb ◽  
Rupen Goswami ◽  
C V R Murty
Keyword(s):  
Shear Strength ◽  
Test Data ◽  
Rectangular Cross Section ◽  
Resistance Mechanism ◽  
Bending Moment ◽  
Shear Resistance ◽  
Shear Capacity ◽  
Longitudinal Reinforcement ◽  
Lateral Shear ◽  
Rc Columns

An analytical method is presented to estimate lateral shear strength (and identify likely mode and location of failure) in reinforced concrete (RC) cantilever columns of rectangular cross-section under combined axial force, shear force and bending moment. Change in shear capacity of concrete with flexural demand at a section is captured explicitly and the shear resistance offered by concrete estimated; this is combined with shear resistance offered by transverse and longitudinal reinforcement bars to estimate the overall shear capacity of RC columns. Shear–moment (V-M) interaction capacity diagram of an RC column, viewed alongside the demand diagram, identifies the lateral shear strength and failure mode. These analytical estimates compare well with test data of 107 RC columns published in literature; the test data corresponds to different axial loads, transverse reinforcement ratios, longitudinal reinforcement ratios, shear span to depth ratios, and loading conditions. Also, the analytical estimates are compared with those obtained using other analytical methods reported in literature; in all cases, the proposed method gives reasonable accuracy when estimating shear capacity of RC columns.  In addition, the method provides insights into the shear resistance mechanism in RC columns under the combined action of P-V-M, and it is simple to use.

Calculation on the Free Energy of Mixing of some Silane Systems

2011 ◽  
Vol 391-392 ◽  
pp. 1017-1021
Author(s):  
Ru Zhang ◽  
Yan Fen Wu ◽  
Ping Hu
Keyword(s):  
Free Energy ◽  
Thermodynamic Properties ◽  
Activity Coefficients ◽  
Influence Factors ◽  
Critical Parameters ◽  
Model Parameters ◽  
Free Energies ◽  
Wilson Model ◽  
Energy Of Mixing ◽  
Free Energy Of Mixing

Six binary silane systems were chosen to calculate the activity coefficients (γ) and free energies of mixing (ΔGm). These systems included: methyldichlorosilane + methyltrichlorosilane, methyldichlorosilane + methylvinyldichlorosilane, methyldichlorosilane + toluene, methyltrichlorosilane + methylvinyldichlorosilane, methyltrichlorosilane + toluene, methylvinyldichlorosilane + toluene. Based on the Antoine constants, critical parameters of the pure components and Wilson model parameters, γ and ΔGmwere calculated. The influence factors of these thermodynamic properties were also discussed.

Optimal Model Parameters of Inverse Kinematics Solution of a 3R Robotic Manipulator Using ANN Models

2020 ◽  
pp. 814-832
Author(s):  
Nikolaos E. Karkalos ◽  
Angelos P. Markopoulos ◽  
Michael F. Dossis
Keyword(s):  
Network Architecture ◽  
Inverse Kinematics ◽  
Input Data ◽  
Robotic Manipulator ◽  
Industrial Applications ◽  
Model Parameters ◽  
Ann Model ◽  
Inverse Kinematics Problem ◽  
Ann Models ◽  
And Training

Solution of inverse kinematics equations of robotic manipulators constitutes usually a demanding problem, which is also required to be resolved in a time-efficient way to be appropriate for actual industrial applications. During the last few decades, soft computing models such as Artificial Neural Networks (ANN) models were employed for the inverse kinematics problem and are considered nowadays as a viable alternative method to other analytical and numerical methods. In the current paper, the solution of inverse kinematics equations of a planar 3R robotic manipulator using ANN models is presented, an investigation concerning optimum values of ANN model parameters, namely input data sample size, network architecture and training algorithm is conducted and conclusions concerning models performance in these cases are drawn.

Calculation of the Separation Factor for some Silane Systems

2011 ◽  
Vol 391-392 ◽  
pp. 1012-1016
Author(s):  
Zu Min Qiu ◽  
Zhong Wei Liu ◽  
Ping Hu
Keyword(s):  
Mole Fraction ◽  
Separation Factor ◽  
Critical Parameters ◽  
Effect Of Temperature ◽  
Model Parameters ◽  
Wilson Model ◽  
Separation Factors

Six ternary silane systems were selected for the separation factor (s) calculation. These systems were: methyldichlorosilane+methyltrichlorosilane+benzene, methyldichlorosilane+ methyltrichlorosilane+dimethyldichlorosilane, methyldichlorosilane+methyltrichlorosilane+ toluene, methyltrichlorosilane+methylvinyldichlorosilane+toluene,methyldichlorosilane+methyltrichloro- silane+methylvinyldichlorosilane, dimethyldiethoxysilane+methyltriethoxysilane+ethanol. Based on the Antoine constants, critical parameters of the pure components and Wilson model parameters, the separation factors were obtained through a program. The effect of temperature and mole fraction to s was also discussed.

scholarly journals Steady-State Steering Characteristics of Mathematical Model for Semitrailer Based on Variations in Camber Parameters

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yi Wei ◽  
Shuilong He ◽  
Enyong Xu ◽  
Genge Zhang ◽  
Rongjiang Tang ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Steady State ◽  
Test Data ◽  
Dynamic State ◽  
Model Parameters ◽  
Stability Factor ◽  
Camber Angle ◽  
Body Roll ◽  
Vehicle Test ◽  
Real Vehicle

To master the basic characteristics of steady-state cornering for a semitrailer, this paper summarises the current modelling methods for handling and stability and discusses their limitations. The classical linear mathematical model for a two-degree-of-freedom (DOF) handling and stability system is used to develop a new model. Analysis methods are proposed to introduce the influence of the camber angle and body roll into the model parameters. Thus, a mathematical model for the lateral stability of semitrailer with five DOFs is established. At the same time, a modified formula to calculate the stability factor of the semitrailer is developed with a MATLAB model to solve the dynamic state equation. The mathematical model, which considers the body roll and the changes in the camber angle caused by roll, compares the turning radius ratio and yaw rate as the evaluation index with the classical linear mathematical model of a two-DOF system. The vehicle parameters for three different types of semi-tractor trailers are used to calculate and compare two mathematical models for handling and stability using real vehicle test data. The results show that the new modelling and analysis method proposed in this paper has a high calculation accuracy and fast calculation speed, is clear and concise, and is consistent with the real vehicle test data. In addition, the accuracy of the new mathematical model for handling and stability and the improved stability factor are verified.

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