Mechanistic-Empirical Rut Prediction Model for In-Service Pavements

2000 ◽  
Vol 1730 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Hyung Bae Kim ◽  
Neeraj Buch ◽  
Dong-Yeob Park
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Flexible Pavements ◽  
Proposed Model ◽  
Major Mode ◽  
Global Positioning ◽  
Pavement Engineering ◽  
Using Data ◽  
Consistency Properties

Rutting is a major mode of failure in flexible pavements. Development of accurate predictive rut performance models is an ongoing pursuit of the pavement engineering community. This has resulted in a plethora of rut prediction models ranging from purely mechanistic to empirical. Presented is the development of a mechanistic-empirical rut prediction model that uses data from 39 in-service flexible pavements from Michigan. The proposed model accounts for the rut contribution of the subgrade, subbase, base, and asphalt concrete layers. The model addresses inventory-type variables like pavement cross section, ambient temperature, and asphalt consistency properties. The applicability of the model was validated by using data from 24 Long-Term Pavement Performance–Global Positioning System (GPS) sites. For 19 of the 24 GPS sites, the predicted rut depth was within 5 mm of the measured rut depth.

Evaluation of Mechanistic-Empirical Performance Prediction Models for Flexible Pavements

1998 ◽  
Vol 1629 (1) ◽  
pp. 169-180 ◽  
Author(s):  
Hesham A. Ali ◽  
Shiraz D. Tayabji
Keyword(s):  
Prediction Models ◽  
Flexible Pavements ◽  
Fatigue Cracking ◽  
Continuous Functions ◽  
Poor Agreement ◽  
Empirical Performance ◽  
Pavement Structures ◽  
Using Data ◽  
Program Data

In recognition of the potential of mechanistic-empirical (M-E) methods in analyzing pavements and predicting their performance, pavement engineers around the country have been advocating the movement toward M-E design methods. In fact, the next AASHTO Guide for Design of Pavement Structures is planned to be mechanistically based. Since many of the performance models used in the M-E methods are laboratory-derived, it is important to validate these models using data from in-service pavements. The Long-Term Pavement Performance (LTPP) program data provide the means to evaluate and improve these models. The fatigue and rutting performances of LTPP flexible pavements were predicted using some well-known M-E models, given the loading and environmental conditions of these pavements. The predicted performances were then compared with actual fatigue cracking and rutting observed in these pavements. Although more data are required to arrive at a more conclusive evaluation, fatigue cracking models appeared to be consistent with observations, whereas rutting models showed poor agreement with the observed rutting. Continuous functions that relate fatigue cracking to fatigue damage were developed.

Probabilistic model predictive control for extended prediction horizons

2021 ◽  
Vol 69 (9) ◽  
pp. 759-770
Author(s):  
Tim Brüdigam ◽  
Johannes Teutsch ◽  
Dirk Wollherr ◽  
Marion Leibold ◽  
Martin Buss
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Models ◽  
Computational Effort ◽  
Probabilistic Constraints ◽  
Detailed Model ◽  
Control Approach ◽  
Proposed Model ◽  
Sampling Times

Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.

scholarly journals Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques

2020 ◽  
Vol 12 (23) ◽  
pp. 9790
Author(s):  
Sanghoon Lee ◽  
Keunho Choi ◽  
Donghee Yoo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Prediction Model ◽  
Internal Control ◽  
Prediction Models ◽  
Influential Factors ◽  
Financial Information ◽  
Data Mining Techniques ◽  
Feasibility Assessment ◽  
Using Data

The government makes great efforts to maintain the soundness of policy funds raised by the national budget and lent to corporate. In general, previous research on the prediction of company insolvency has dealt with large and listed companies using financial information with conventional statistical techniques. However, small- and medium-sized enterprises (SMEs) do not have to undergo mandatory external audits, and the quality of accounting information is low due to weak internal control. To overcome this problem, we developed an insolvency prediction model for SMEs using data mining techniques and technological feasibility assessment information as non-financial information. We divided the dataset into two types of data based on three years of corporate age. The synthetic minority over-sampling technique (SMOTE) was used to solve the data imbalance that occurred at this time. Six insolvency prediction models were created using logistic regression, a decision tree, an artificial neural network, and an ensemble (i.e., boosting) of each algorithm. By applying a boosted decision tree, the best accuracies of 69.1% and 82.7% were derived, and by applying a decision tree, nine and seven influential factors affected the insolvency of SMEs established for fewer than three years and more than three years, respectively. In addition, we derived several insolvency rules for the two types of SMEs from the decision tree-based prediction model and proposed ways to enhance the health of loans given to potentially insolvent companies using these derived rules. The results of this study show that it is possible to predict SMEs’ insolvency using data mining techniques with technological feasibility assessment information and find meaningful rules related to insolvency.

scholarly journals A New Click-Through Rates Prediction Model Based on Deep&Cross Network

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 342
Author(s):  
Guojing Huang ◽  
Qingliang Chen ◽  
Congjian Deng
Keyword(s):  
Neural Networks ◽  
Prediction Model ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
State Of The Art ◽  
Online Advertising ◽  
Optimization Technique ◽  
Proposed Model ◽  
Great Progress ◽  
Online Advertisement

With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the user’s click probability of an online advertisement. Recently, great progress has been made with the introduction of Deep Neural Networks (DNN) into CTR. In order to further advance the DNN-based CTR prediction models, this paper introduces a new model of FO-FTRL-DCN, based on the prestigious model of Deep&Cross Network (DCN) augmented with the latest optimization technique of Follow The Regularized Leader (FTRL) for DNN. The extensive comparative experiments on the iPinYou datasets show that the proposed model has outperformed other state-of-the-art baselines, with better generalization across different datasets in the benchmark.

scholarly journals Validation of prediction models of severe disease course and non-achievement of remission in juvenile idiopathic arthritis: part 1—results of the Canadian model in the Nordic cohort

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Veronika Rypdal ◽  
◽  
Jaime Guzman ◽  
Andrew Henrey ◽  
Thomas Loughin ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Prediction Model ◽  
Prediction Models ◽  
Severe Disease ◽  
Disease Onset ◽  
Fine Tuning ◽  
Disease Course ◽  
Internal Validation ◽  
Long Term Outcome

Abstract Background Models to predict disease course and long-term outcome based on clinical characteristics at disease onset may guide early treatment strategies in juvenile idiopathic arthritis (JIA). Before a prediction model can be recommended for use in clinical practice, it needs to be validated in a different cohort than the one used for building the model. The aim of the current study was to validate the predictive performance of the Canadian prediction model developed by Guzman et al. and the Nordic model derived from Rypdal et al. to predict severe disease course and non-achievement of remission in Nordic patients with JIA. Methods The Canadian and Nordic multivariable logistic regression models were evaluated in the Nordic JIA cohort for prediction of non-achievement of remission, and the data-driven outcome denoted severe disease course. A total of 440 patients in the Nordic cohort with a baseline visit and an 8-year visit were included. The Canadian prediction model was first externally validated exactly as published. Both the Nordic and Canadian models were subsequently evaluated with repeated fine-tuning of model coefficients in training sets and testing in disjoint validation sets. The predictive performances of the models were assessed with receiver operating characteristic curves and C-indices. A model with a C-index above 0.7 was considered useful for clinical prediction. Results The Canadian prediction model had excellent predictive ability and was comparable in performance to the Nordic model in predicting severe disease course in the Nordic JIA cohort. The Canadian model yielded a C-index of 0.85 (IQR 0.83–0.87) for prediction of severe disease course and a C-index of 0.66 (0.63–0.68) for prediction of non-achievement of remission when applied directly. The median C-indices after fine-tuning were 0.85 (0.80–0.89) and 0.69 (0.65–0.73), respectively. Internal validation of the Nordic model for prediction of severe disease course resulted in a median C-index of 0.90 (0.86–0.92). Conclusions External validation of the Canadian model and internal validation of the Nordic model with severe disease course as outcome confirm their predictive abilities. Our findings suggest that predicting long-term remission is more challenging than predicting severe disease course.

scholarly journals An Influence Prediction Model for Microblog Entries on Public Health Emergencies

2019 ◽  
Vol 3 (2) ◽  
pp. 102-115 ◽  
Author(s):  
Lu An ◽  
Xingyue Yi ◽  
Yuxin Han ◽  
Gang Li
Keyword(s):  
Public Health ◽  
Prediction Model ◽  
Latent Dirichlet Allocation ◽  
Prediction Models ◽  
Data Sets ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Public Health Emergencies ◽  
Proposed Model ◽  
The Individual

Abstract This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

scholarly journals Rain Attenuation Prediction Model for Lagos at Millimeter Wave Bands

2014 ◽  
Vol 31 (3) ◽  
pp. 639-646 ◽  
Author(s):  
Abayomi Isiaka Yussuff ◽  
Nor Hisham Haji Khamis
Keyword(s):  
Prediction Model ◽  
Millimeter Wave ◽  
Prediction Models ◽  
Percentage Error ◽  
Radio Communication ◽  
Southwestern Nigeria ◽  
Coastal Station ◽  
International Telecommunication Union ◽  
Proposed Model ◽  
X Band

Abstract Lagos, Nigeria (6.35°N, 3.2°E), is a coastal station in the rain forest area of southwestern Nigeria with an altitude of 38 m. Since most communication now takes place above the X band because of congestion of lower bands, it was necessary to look into ways of maximizing X-band usage. There are inadequate data for use in rain propagation studies at microwave frequencies, and even less so at millimeter wave bands where most of the signal depolarization and fading has been discovered to exist. The proposed model is a modification of the International Telecommunication Union–Radio Communication Sector (ITU-R) model combined with locally obtained regression coefficients for estimating specific attenuation as proposed by G. Olalere Ajayi. The Dissanayake, Allnutt, and Haidara (DAH), Simple Attenuation Model (SAM), and ITU-R attenuation prediction models were investigated along with the proposed model. The ITU-R model was observed to produce the best results at 40 GHz, with percentage error values of 0.61%, 0.55%, and 0.49% at 0.1%, 0.01%, and 0.001% of the time, respectively. In comparison, the proposed prediction model showed good performance at 20-GHz down-link frequency, with percentage error values of 3.6%, 3.3%, and 2.9% at 0.1%, 0.01%, and 0.001% of the time, respectively. The obtained results also showed good agreement with other similar works in the open literature. The results presented in this work are valuable for the design and planning of a satellite link in the tropical regions.

scholarly journals A Simple Water Retention Model Based on Grain Size Distribution

2021 ◽  
Vol 11 (20) ◽  
pp. 9452
Author(s):  
Andrew Vidler ◽  
Olivier Buzzi ◽  
Stephen Fityus
Keyword(s):  
Plant Growth ◽  
Water Retention ◽  
Prediction Models ◽  
Mine Waste ◽  
Coarse Grained ◽  
Retention Model ◽  
Proposed Model ◽  
Life Options ◽  
Using Data ◽  
Drying Curves

The Hunter valley region in NSW Australia is an area with a heavy coal mining presence. As some mines come to their end of life, options are being investigated to improve the topsoil on post mining land for greater plant growth, which may allow economically beneficial farmland to be created. This research is part of an investigation into mixing a mine waste material, coal tailings, with topsoil in order to produce an improved soil for plant growth. Implementing such a solution requires estimation of the drying path of the water retention curves for the tailings and topsoil used. Instead of a lengthy laboratory measurement, a prediction of the drying curve is convenient in this context. No existing prediction models were found that were suitable for these mine materials, hence this paper proposes a simple and efficient model that can more accurately predict drying curves for these mine materials. The drying curves of two topsoils and two tailings from Australian coal mines were measured and compared with predictions using the proposed model, which performs favorably compared to several existing models in the literature. Additionally, the proposed model is assessed using data from a variety of fine- and coarse-grained materials in the literature. It is shown that the proposed model is overall more accurate than every other model assessed, indicating the model may be useful for various materials other than those considered in this study.

scholarly journals Driver Lane-Changing Behavior Prediction Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Cheng Wei ◽  
Fei Hui ◽  
Asad J. Khattak
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Traffic Safety ◽  
Prediction Models ◽  
Time Window ◽  
Driving Safety ◽  
Physiological Data ◽  
Lane Changing ◽  
Proposed Model ◽  
Neural Network Prediction

A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.

scholarly journals Ordinal Prediction Model of 90-Day Modified Rankin Scale in Ischemic Stroke

2021 ◽  
Vol 12 ◽  
Author(s):  
Michelle Y. Zhang ◽  
Michael Mlynash ◽  
Kristin L. Sainani ◽  
Gregory W. Albers ◽  
Maarten G. Lansberg
Keyword(s):  
Clinical Trials ◽  
Ischemic Stroke ◽  
Prediction Model ◽  
Functional Outcomes ◽  
Prediction Models ◽  
Clinical Care ◽  
External Validation ◽  
Modified Rankin Scale ◽  
Forward Selection ◽  
Using Data

Background and Purpose: Prediction models for functional outcomes after ischemic stroke are useful for statistical analyses in clinical trials and guiding patient expectations. While there are models predicting dichotomous functional outcomes after ischemic stroke, there are no models that predict ordinal mRS outcomes. We aimed to create a model that predicts, at the time of hospital discharge, a patient's modified Rankin Scale (mRS) score on day 90 after ischemic stroke.Methods: We used data from three multi-center prospective studies: CRISP, DEFUSE 2, and DEFUSE 3 to derive and validate an ordinal logistic regression model that predicts the 90-day mRS score based on variables available during the stroke hospitalization. Forward selection was used to retain independent significant variables in the multivariable model.Results: The prediction model was derived using data on 297 stroke patients from the CRISP and DEFUSE 2 studies. National Institutes of Health Stroke Scale (NIHSS) at discharge and age were retained as significant (p < 0.001) independent predictors of the 90-day mRS score. When applied to the external validation set (DEFUSE 3, n = 160), the model accurately predicted the 90-day mRS score within one point for 78% of the patients in the validation cohort.Conclusions: A simple model using age and NIHSS score at time of discharge can predict 90-day mRS scores in patients with ischemic stroke. This model can be useful for prognostication in routine clinical care and to impute missing data in clinical trials.

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