scholarly journals Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chaohui Wang ◽  
Songyuan Tan ◽  
Qian Chen ◽  
Jiguo Han ◽  
Liang Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Multiple Regression ◽  
Dynamic Modulus ◽  
Prediction Error ◽  
Prediction Models ◽  
Asphalt Mixture ◽  
High Modulus ◽  
Output Stability ◽  
Svm Model

Dynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. The purpose is to achieve the accurate prediction of the dynamic modulus of the high-modulus asphalt mixture and further optimize the design process of the high-modulus asphalt mixture. Five high-temperature performance indexes of high-modulus asphalt and its mixture were selected. The correlation between the above five indexes and the dynamic modulus of the high-modulus asphalt mixture was analyzed. On this basis, the dynamic modulus prediction models of the high-modulus asphalt mixture based on small sample data were established by multiple regression, general regression neural network (GRNN), and support vector machine (SVM) neural network. According to parameter adjustment and cross-validation, the output stability and accuracy of different prediction models were compared and evaluated. The most effective prediction model was recommended. The results show that the SVM model has more significant prediction accuracy and output stability than the multiple regression model and the GRNN model. Its prediction error was 0.98–9.71%. Compared with the other two models, the prediction error of the SVM model declined by 0.50–11.96% and 3.76–13.44%. The SVM neural network was recommended as the dynamic modulus prediction model of the high-modulus asphalt mixture.

NEURAL NETWORK-BASED PREDICTION MODEL FOR THE DYNAMICS OF THE IONOSPHERIC EQUATORIAL ANOMALY USING THE TOTAL ELECTRONIC CONTENT

2021 ◽  
pp. 29-42
Author(s):  
A. M. Appalonov ◽  
Yu. S. Maslennikova
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Prediction Error ◽  
Prediction Models ◽  
Short Term Memory ◽  
Principal Component ◽  
Equatorial Anomaly ◽  
Seasonal Factors ◽  
Total Electronic Content ◽  
Electronic Content

In this paper we present the prediction model for the dynamics of the ionospheric equatorial anomaly that is based on the use of the Principal Component Analysis (PCA) and Artificial Neural Networks (ANN). The prediction model was developed by using global maps of the ionosphere Total Electronic Content (TEC) for the period from 2001 to 2018. We show that in case of correct data centering and elimination of diurnal and seasonal factors, the equatorial anomaly makes major contribution to the variance of fluctuations in the TEC data. We applied several neural network-based prediction models that were trained independently for each component of the decomposition. The approach based on a hybrid model consisting of a convolution network and a network with long short-term memory with preanalysis of the principal components reduced the prediction error of TEC maps by 2 hours. The prediction error of this model was 4 times less than the error of the linear regression model.

Experimental Analysis of Dynamic Modulus and Phase Angle of High Modulus Asphalt Mixture

2011 ◽  
Vol 243-249 ◽  
pp. 4220-4225
Author(s):  
Rui Bo Ren ◽  
Li Tao Geng ◽  
Li Zhi Wang ◽  
Peng Wang
Keyword(s):  
Phase Angle ◽  
Dynamic Modulus ◽  
Asphalt Mixture ◽  
Performance Testing ◽  
Asphalt Mixtures ◽  
Testing System ◽  
High Modulus ◽  
Temperature Performance ◽  
Different Temperatures ◽  
Confining Pressures

To study the mechanical properties of high modulus asphalt mixtures, dynamic modulus and phase angle of these two mixtures are tested with Simple Performance Testing System under different temperatures, loading frequencies and confining pressures. Testing results show the superiority of high modulus asphalt mixture in aspect of high temperature performance. Furthermore, the changing rules of dynamic modulus and phase angle are also discussed.

A Model Based on BP Neural Network for Audible Noise Prediction

2014 ◽  
Vol 986-987 ◽  
pp. 1356-1359
Author(s):  
You Xian Peng ◽  
Bo Tang ◽  
Hong Ying Cao ◽  
Bin Chen ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Transmission Lines ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Absolute Error ◽  
Research Area ◽  
Noise Prediction ◽  
Bp Network ◽  
Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

scholarly journals External Validation of the Dutch SOURCE Survival Prediction Model in Belgian Metastatic Oesophageal and Gastric Cancer Patients

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 834
Author(s):  
J.J. van Kleef ◽  
H.G. van den Boorn ◽  
R.H.A. Verhoeven ◽  
K. Vanschoenbeek ◽  
A. Abu-Hanna ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prediction Model ◽  
Oesophageal Cancer ◽  
Prediction Error ◽  
Prediction Models ◽  
Mean Difference ◽  
Survival Prediction ◽  
Cancer Model ◽  
The Mean ◽  
Mean Prediction Error

The SOURCE prediction model predicts individualised survival conditional on various treatments for patients with metastatic oesophageal or gastric cancer. The aim of this study was to validate SOURCE in an external cohort from the Belgian Cancer Registry. Data of Belgian patients diagnosed with metastatic disease between 2004 and 2014 were extracted (n = 4097). Model calibration and discrimination (c-indices) were determined. A total of 2514 patients with oesophageal cancer and 1583 patients with gastric cancer with a median survival of 7.7 and 5.4 months, respectively, were included. The oesophageal cancer model showed poor calibration (intercept: 0.30, slope: 0.42) with an absolute mean prediction error of 14.6%. The mean difference between predicted and observed survival was −2.6%. The concordance index (c-index) of the oesophageal model was 0.64. The gastric cancer model showed good calibration (intercept: 0.02, slope: 0.91) with an absolute mean prediction error of 2.5%. The mean difference between predicted and observed survival was 2.0%. The c-index of the gastric cancer model was 0.66. The SOURCE gastric cancer model was well calibrated and had a similar performance in the Belgian cohort compared with the Dutch internal validation. However, the oesophageal cancer model had not. Our findings underscore the importance of evaluating the performance of prediction models in other populations.

Research on the Application of Recycled PE in High Modulus Asphalt Mixture

2011 ◽  
Vol 287-290 ◽  
pp. 1155-1163
Author(s):  
Shao Long Huang ◽  
Fan Shen ◽  
Qing Jun Ding
Keyword(s):  
Molecular Weight ◽  
Dynamic Modulus ◽  
Asphalt Binder ◽  
Asphalt Mixture ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
High Modulus ◽  
Normal Mixture ◽  
Modified Asphalt ◽  
Modified Asphalt Binder

In this paper, recycled PE was added directly to the asphalt mixture to prepare high modulus asphalt mixture. To study the influence of the dosage and molecular weight of recycled PE on the performance of asphalt mixture, three kinds of recycled PE with different molecular weight and three asphalt binders (Conventional, SBS Modified and PE Modified) were used to prepare eight kinds of asphalt mixture. Various tests, including dynamic modulus, wheel tracking and Lottman test, were conducted to evaluate the performance of them. The results showed that 1) the dynamic modulus of asphalt mixture modified by recycled PE is higher than the normal mixture and mixture prepared with SBS modified asphalt binder; 2) adding recycled PE directly into the asphalt mixture during mixing is more effective than preparing asphalt mixture with PE modified asphalt binder in making high modulus asphalt mixture; 3) the recycled PE used to produce high modulus asphalt mixture should have certain big molecular weight, more than 27,000, and the dosage of recycled PE should be no less than 0.4% of the total weight of asphalt mixture. The performance tests indicted the good high temperature deformation resistance property of asphalt mixture modified by recycled PE.

Multivariate Multiple Regression Prediction Models: A Euclidean Distance Approach

2003 ◽  
Vol 92 (3) ◽  
pp. 763-769 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry
Keyword(s):  
Prediction Model ◽  
Multiple Regression ◽  
Effect Size ◽  
Euclidean Distance ◽  
Prediction Models ◽  
Regression Coefficients ◽  
Multiple Response ◽  
Multivariate Multiple Regression ◽  
Euclidean Distances ◽  
Distance Approach

An extension of a multiple regression prediction model to multiple response variables is presented. An algorithm using least sum of Euclidean distances between the multivariate observed and model-predicted response values provides regression coefficients, a measure of effect size, and inferential procedures for evaluating the extended multivariate multiple regression prediction model.

Characteristic Analysis of Dynamic Modulus for High Modulus Asphalt Concrete

2014 ◽  
Vol 505-506 ◽  
pp. 15-18 ◽  
Author(s):  
Xiao Long Zou ◽  
Ai Min Sha ◽  
Wei Jiang ◽  
Xin Yan Huang
Keyword(s):  
Dynamic Modulus ◽  
Testing Machine ◽  
Asphalt Mixture ◽  
Asphalt Mixtures ◽  
High Modulus ◽  
Characteristic Analysis ◽  
Modified Asphalt ◽  
Universal Testing ◽  
Different Temperatures ◽  
Dynamic Modulus Test

In order to analyze the characteristics of high modulus asphalt mixture dynamic modulus, Universal Testing Machine (UTM-25) was used for dynamic modulus test of three kinds of mixtures, which were PR Module modified asphalt mixture and PR PLAST.S modified asphalt mixture and virgin asphalt mixture, to investigate dynamic modulus and phase angle at different temperatures and frequencies. The results indicate that: the dynamic modulus order of the three asphalt mixtures is PR MODULE > PR PLAST.S > Virgin. PR MODULE asphalt mixture dynamic modulus is much larger than the other two.

Predicting Complete Ground Reaction Forces and Moments During Gait With Insole Plantar Pressure Information Using a Wavelet Neural Network

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Taeyong Sim ◽  
Hyunbin Kwon ◽  
Seung Eel Oh ◽  
Su-Bin Joo ◽  
Ahnryul Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Plantar Pressure ◽  
Prediction Models ◽  
Pressure Sensors ◽  
Principal Component ◽  
Wavelet Neural Network ◽  
Human Gait ◽  
Ground Reaction Forces ◽  
Reaction Forces

In general, three-dimensional ground reaction forces (GRFs) and ground reaction moments (GRMs) that occur during human gait are measured using a force plate, which are expensive and have spatial limitations. Therefore, we proposed a prediction model for GRFs and GRMs, which only uses plantar pressure information measured from insole pressure sensors with a wavelet neural network (WNN) and principal component analysis-mutual information (PCA-MI). For this, the prediction model estimated GRFs and GRMs with three different gait speeds (slow, normal, and fast groups) and healthy/pathological gait patterns (healthy and adolescent idiopathic scoliosis (AIS) groups). Model performance was validated using correlation coefficients (r) and the normalized root mean square error (NRMSE%) and was compared to the prediction accuracy of the previous methods using the same dataset. As a result, the performance of the GRF and GRM prediction model proposed in this study (slow group: r = 0.840–0.989 and NRMSE% = 10.693–15.894%; normal group: r = 0.847–0.988 and NRMSE% = 10.920–19.216%; fast group: r = 0.823–0.953 and NRMSE% = 12.009–20.182%; healthy group: r = 0.836–0.976 and NRMSE% = 12.920–18.088%; and AIS group: r = 0.917–0.993 and NRMSE% = 7.914–15.671%) was better than that of the prediction models suggested in previous studies for every group and component (p < 0.05 or 0.01). The results indicated that the proposed model has improved performance compared to previous prediction models.

scholarly journals Advanced approaches to hot-mix asphalt dynamic modulus prediction

2008 ◽  
Vol 35 (7) ◽  
pp. 699-707 ◽  
Author(s):  
Halil Ceylan ◽  
Kasthurirangan Gopalakrishnan ◽  
Sunghwan Kim
Keyword(s):  
Prediction Model ◽  
Dynamic Modulus ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Logical Structure ◽  
Ann Model ◽  
Model Predictions ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
National Cooperative

The dynamic modulus (|E*|) is one of the primary hot-mix asphalt (HMA) material property inputs at all three hierarchical levels in the new Mechanistic–empirical pavement design guide (MEPDG). The existing |E*| prediction models were developed mainly from regression analysis of an |E*| database obtained from laboratory testing over many years and, in general, lack the necessary accuracy for making reliable predictions. This paper describes the development of a simplified HMA |E*| prediction model employing artificial neural network (ANN) methodology. The intelligent |E*| prediction models were developed using the latest comprehensive |E*| database that is available to researchers (from National Cooperative Highway Research Program Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the Hirsch |E*| prediction model, which has a logical structure and a relatively simple prediction model in terms of the number of input parameters needed with respect to the existing |E*| models. The ANN-based |E*| predictions showed significantly higher accuracy compared with the Hirsch model predictions. The sensitivity of input variables to the ANN model predictions were also examined and discussed.

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