Field test validation of water film depth (WFD) prediction models for pavement surface drainage

2017 ◽  
Vol 20 (10) ◽  
pp. 1170-1181 ◽  
Author(s):  
Wenting Luo ◽  
Kelvin C. P. Wang ◽  
Lin Li
Keyword(s):  
Field Test ◽  
Prediction Models ◽  
Water Film ◽  
Test Validation ◽  
Surface Drainage ◽  
Pavement Surface

scholarly journals Modeling of Surface Drainage during the Service Life of Asphalt Pavements Showing Long-Term Rutting: A Modular Hydromechanical Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Stefan Alber ◽  
Barbara Schuck ◽  
Wolfram Ressel ◽  
Ronny Behnke ◽  
Gustavo Canon Falla ◽  
...  
Keyword(s):  
Water Film ◽  
Material Behavior ◽  
Asphalt Pavements ◽  
Computationally Efficient ◽  
Surface Drainage ◽  
Pavement Surface ◽  
Road Geometry ◽  
Longitudinal Slope ◽  
Steady State Rolling

This paper presents a modular hydromechanical approach to assess the short- and long-term surface drainage behavior of arbitrarily deformable asphalt pavements. The modular approach consists of three steps. In the first step, the experimental characterization of the thermomechanical asphalt material behavior is performed. In the second step, information about the long-term material behavior of the asphalt mixtures is integrated on the structural scale via a finite element (FE) tire-pavement model for steady-state rolling conditions and time homogenization in order to achieve a computationally efficient long-term prediction of inelastic deformations of the pavement surface (rut formation). In the third step, information regarding the current pavement geometry (deformed pavement surface) is used to carry out a surface drainage analysis to predict, e.g., the thickness of the water film or the water depth in the pavement ruts as a function of several influencing quantities. For chosen numerical examples, the influence of road geometry (cross and longitudinal slope), road surface (mean texture depth and state of rut deformation), and rainfall properties (rain intensity and duration) on the pavement surface drainage capacity is assessed. These parameters are strongly interrelated, and general statements are not easy to find. Certain trends, however, have been identified and are discussed.

Field test validation of the degadis model

1987 ◽  
Vol 16 ◽  
pp. 231-245 ◽  
Author(s):  
T.O. Spicer ◽  
J.A. Havens
Keyword(s):  
Field Test ◽  
Test Validation

Development of Mode-Wise Noise Prediction Models for the Noise Generated Due to Tyre-Pavement Surface Interaction

2013 ◽  
Vol 723 ◽  
pp. 50-57 ◽  
Author(s):  
Arnepalli Syamkumar ◽  
Kamineni Aditya ◽  
Venkaiah Chowdary
Keyword(s):  
Prediction Models ◽  
Surface Interaction ◽  
Noise Model ◽  
Significant Variable ◽  
Noise Prediction ◽  
Vehicle Speed ◽  
Vehicle Exhaust ◽  
Cement Concrete ◽  
Factors Affecting ◽  
Pavement Surface

The main objective of this study is to evaluate the noise generated due to tyre-pavement surface interaction for various modes and to develop a noise prediction model for each mode by taking into account various factors affecting the noise generation. Eight asphalt pavement and four cement concrete pavement stretches were selected for measurement of the noise. Tyre-pavement interaction noise was measured using controlled pass-by method by eliminating the noise generated from engine and the vehicle exhaust systems. Noise levels were measured as a function of vehicle type, vehicle speed, loading condition, pavement temperature, direction of wind, and type of pavement. The influence of each of these variables are analyzed and quantified in this paper. The vehicle speed is found to be the most significant variable affecting the noise generated due to tyre-pavement surface interaction followed by other variables. Further, individual noise prediction models are developed for each mode in each survey location and a combined tyre-pavement interaction noise model is developed for each mode for both asphalt and cement concrete pavements.

Validation Study of Road Surface Water Film Depth Prediction Model

2014 ◽  
Vol 1079-1080 ◽  
pp. 379-385 ◽  
Author(s):  
Jing Luo ◽  
Jian Bei Liu ◽  
Teng Feng Guo ◽  
Cheng Yu Hu
Keyword(s):  
Comparative Analysis ◽  
Surface Water ◽  
Film Thickness ◽  
Experimental Validation ◽  
Prediction Models ◽  
Water Film ◽  
Small Scale ◽  
Model Parameters ◽  
Water Film Thickness ◽  
Depth Prediction

Surface water film thickness is one of the main factors, which affect the vehicle safety on slippery roads. Water film depth is influenced by rainfall intensity, grades, cross slopes, drainage length and pavement texture. This paper reviews the research status and makes some comparative analysis of several pavement water film depth prediction models. An experimental validation has verified and calibrated the existing water film depth prediction models results. The experimental validation of the variable in the slope water flow model has been implemented by means of a small scale physical road model in a rainfall simulator, which is constructed in a laboratory. The results of comparative analysis have shown that in the existing water film depth prediction models, the regression models predict values are more closely than mathematical-physical models. Because under different experimental conditions, the regression model calibration parameters are different. In the case of specific road characteristics for prediction of water film thickness, the model parameters can be calibrated to further improve predicting accuracy.

scholarly journals Linear and Nonlinear Prediction Models Show Comparable Precision for Maximal Mean Speed in a 4x1000 m Field Test

2017 ◽  
Vol 16 (2) ◽  
pp. 78-87
Author(s):  
J. M. Jäger ◽  
J. Kurz ◽  
H. Müller
Keyword(s):  
Oxygen Uptake ◽  
Field Test ◽  
Maximal Oxygen Uptake ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Endurance Performance ◽  
Step Test ◽  
Nonlinear Prediction ◽  
Linear And Nonlinear ◽  
Mlr Model

AbstractMaximal oxygen uptake (VO2max) is one of the most distinguished parameters in endurance sports and plays an important role, for instance, in predicting endurance performance. Different models have been used to estimate VO2max or performance based on VO2max. These models can use linear or nonlinear approaches for modeling endurance performance. The aim of this study was to estimate VO2max in healthy adults based on the Queens College Step Test (QCST) as well as the Shuttle Run Test (SRT) and to use these values for linear and nonlinear models in order to predict the performance in a maximal 1000 m run (i.e. the speed in an incremental 4×1000 m Field Test (FT)). 53 female subjects participated in these three tests (QCST, SRT, FT). Maximal oxygen uptake values from QCST and SRT were used as (a) predictor variables in a multiple linear regression (MLR) model and as (b) input variables in a multilayer perceptron (MLP) after scaling in preprocessing. Model output was speed [km·h−1] in a maximal 1000 m run. Maximal oxygen uptake values estimated from QCST (40.8 ± 3.5 ml·kg−1·min−1) and SRT (46.7 ± 4.5 ml·kg−1·min−1) were significantly correlated (r = 0.38, p < 0.01) and maximal mean speed in the FT was 12.8 ± 1.6 km·h−1. Root mean squared error (RMSE) of the cross validated MLR model was 0.89 km·h−1while it was 0.95 km·h−1for MLP. Results showed that the accuracy of the applied MLP was comparable to the MLR, but did not outperform the linear approach.

Field Test Validation of Low Voltage Grid Applications in a Danish Setting

2021 ◽  
pp. 82-89
Author(s):  
Kamal Shahid ◽  
Rasmus Løvenstein Olsen ◽  
Rolf Kirk
Keyword(s):  
Field Test ◽  
Low Voltage ◽  
Test Validation ◽  
Grid Applications
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