scholarly journals Development of a methodology to backcalculate pavement layer moduli using the traffic speed deflectometer

2018 ◽  
Vol 45 (5) ◽  
pp. 377-385 ◽  
Author(s):  
Omar Elbagalati ◽  
Momen Mousa ◽  
Mostafa A. Elseifi ◽  
Kevin Gaspard ◽  
Zhongjie Zhang
Keyword(s):  
Traffic Control ◽  
Coefficient Of Determination ◽  
Falling Weight Deflectometer ◽  
Ann Model ◽  
Acceptable Accuracy ◽  
Traffic Speed ◽  
Artificial Neural Network Ann ◽  
Falling Weight ◽  
Good Agreement

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) measurements; however, the stationary nature of FWD requires lane closure and traffic control. To overcome these limitations, a number of continuous deflection devices were introduced in recent years. The objective of this study was to develop a methodology to incorporate traffic speed deflectometer (TSD) measurements in the backcalculation analysis. To achieve this objective, TSD and FWD measurements were used to train and to validate an artificial neural network (ANN) model that would convert TSD deflection measurements to FWD deflection measurements. The ANN model showed acceptable accuracy with a coefficient of determination of 0.81 and a good agreement between the backcalculated moduli from FWD and TSD measurements. Evaluation of the model showed that the backcalculated layer moduli from TSD could be used in pavement analysis and in structural health monitoring with a reasonable level of accuracy.

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

Mechanistic-Based Approach to Utilize Traffic Speed Deflectometer Measurements in Backcalculation Analysis

2020 ◽  
Vol 2674 (5) ◽  
pp. 208-222
Author(s):  
Zia U. A. Zihan ◽  
Mostafa A. Elseifi ◽  
Patrick Icenogle ◽  
Kevin Gaspard ◽  
Zhongjie Zhang
Keyword(s):  
Asphalt Concrete ◽  
Traffic Control ◽  
Parametric Study ◽  
Complex Modulus ◽  
Falling Weight Deflectometer ◽  
Software Application ◽  
Traffic Speed ◽  
Definition Of ◽  
Falling Weight ◽  
Surface Deflections

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) deflection measurements; however, the stationary nature of the FWD requires lane closure and traffic control. In recent years, traffic speed deflection devices such as the traffic speed deflectometer (TSD), which can continuously measure pavement surface deflections at traffic speed, have been introduced. In this study, a mechanistic-based approach was developed to convert TSD deflection measurements into the equivalent FWD deflections. The proposed approach uses 3D-Move software to calculate the theoretical deflection bowls corresponding to FWD and TSD loading configurations. Since 3D-Move requires the definition of the constitutive behaviors of the pavement layers, cores were extracted from 13 sections in Louisiana and were tested in the laboratory to estimate the dynamic complex modulus of asphalt concrete. The 3D-Move generated deflection bowls were validated with field TSD and FWD data with acceptable accuracy. A parametric study was then conducted using the validated 3D-Move model; the parametric study consisted of simulating pavement designs with varying thicknesses and material properties and their corresponding FWD and TSD surface deflections were calculated. The results obtained from the parametric study were then incorporated into a Windows-based software application, which uses artificial neural network as the regression algorithm to convert TSD deflections to their corresponding FWD deflections. This conversion would allow backcalculation of layer moduli using TSD-measured deflections, as equivalent FWD deflections can be used with readily available tools to backcalculate the layer moduli.

Development of an Artificial Neural Network-Based Procedure for the Verification of Traffic Speed Deflectometer Measurements

2021 ◽  
pp. 036119812110057
Author(s):  
Hossam Abohamer ◽  
Mostafa A. Elseifi ◽  
Zia U. A. Zihan ◽  
Zhong Wu ◽  
Nathan Kebede ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
The United States ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Measuring Device ◽  
Spatial Coverage ◽  
Artificial Neural ◽  
Traffic Speed ◽  
Artificial Neural Network Ann

Since the 1980s, the falling weight deflectometer (FWD) has been the primary deflection-measuring device in the United States to evaluate the structural conditions of in-service pavements. However, the stop and go nature of the FWD limits its application at the network level. In the early 2000s, the traffic speed deflectometer (TSD) was introduced as an alternate deflection-measuring device for network-level applications. TSD collects deflection measurements while traveling at traffic speed, which provides improved spatial coverage and no traffic disturbance. The verification of TSD measurements is of great interest as many agencies move toward widespread implementation. This study aims at developing a reliable and straightforward procedure for the verification of TSD measurements using limited FWD measured deflection measurements. The verification procedure employs a trained artificial neural network (ANN) model to shift TSD deflections to their corresponding FWD deflections. The ANN model was trained and verified based on FWD and TSD measurements from two deflection-testing programs. The developed model accurately predicted FWD measurements with a coefficient of determination (R2) of 0.994. The suitability of the proposed verification procedure was evaluated using statistical and engineering-based measures and showed acceptable accuracy. Results also validated that the proposed method could be used to verify TSD measurements before its use for conducting deflection measurements at the network level.

scholarly journals Application of the SHS-GC-FID method and HPLC-DAD method in the prediction of the shelf-life of gluten-free cookies

2017 ◽  
Vol 82 (12) ◽  
pp. 1343-1355
Author(s):  
Marijana Sakac ◽  
Lato Pezo ◽  
Pavle Jovanov ◽  
Natasa Nedeljkovic ◽  
Anamarija Mandic ◽  
...  
Keyword(s):  
Shelf Life ◽  
Hplc Method ◽  
Coefficient Of Determination ◽  
Relevant Parameter ◽  
Gluten Free ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Gas Chromatographic Method

The aim of this study was to compare the sensitivity of two analytical methods for the prediction of the shelf-life of unpacked and packed gluten-free rice?buckwheat cookies kept at ambient (23?1?C) and elevated (40?1?C) temperature during storage, namely the static headspace gas chromatographic method with flame ionisation detection (SHS-GC-FID) for volatile saturated aldehydes (propanal (C3), pentanal (C5), hexanal (C6), heptanal (C7) and octanal (C8)) and the HPLC method for malondialdehyde (MDA) determination. Both methods resulted in obtaining the same end-points of cookie shelf-life, i.e., 3 and 5 months for unpacked and packed cookies kept at elevated temperature, respectively, and 11 and 14 months for unpacked and packed cookies kept at ambient temperature, respectively. Two computational approaches, i.e., the second order polynomial (SOP) and artificial neural network (ANN) models, were used accordingly. The calculations of the contents of aldehydes and MDA could be predicted with an overall coefficient of determination of 0.722 using the ANN model compared to 0.312?0.773 for SOP models. According to sensitivity analysis, it might be suggested that the relevant parameter for the prediction of the end-point of cookie shelf-life is the MDA rather than the C3, C5, C6, C7 and C8 content.

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sunil K. Deokar ◽  
Nachiket A. Gokhale ◽  
Sachin A. Mandavgane
Keyword(s):  
Three Dimensional ◽  
Current Approach ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Operational Parameters ◽  
Initial Concentration ◽  
Combined Application ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
Biomass Ashes

Abstract Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

Prediction of Surface Roughness and Coefficient of Friction Using Artificial Neural Network in Tribotesting of Bio-Lubricants

2019 ◽  
Vol 895 ◽  
pp. 52-57 ◽  
Author(s):  
Prasanna Vineeth Bharadwaj ◽  
T.P. Jeevan ◽  
P.S. Suvin ◽  
S.R. Jayaram
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Coefficient Of Friction ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network ◽  
Lubrication Conditions ◽  
Good Agreement

Tribotesting is necessary to understand the behaviour of the material under various operating lubrication conditions. This paper deals with the training of an artificial neural network (ANN) model with Bio-lubricant properties and machining conditions for prediction of surface roughness and coefficient of friction in Tribotesting by Tool chip Tribometer. Experimental results obtained from Tool chip tribometer for tested bio-lubricants are compared with those obtained by ANN prediction. A good agreement in results recommends that a well trained neural network is competent enough to predict the parameters in Tribotesting process.

scholarly journals Construction of Process Window to Predict Hardness in Tailored Tool Thermomechanical Treatment and its Application

Metals ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 50
Author(s):  
Jae-Hong Kim ◽  
Seon-Bong Lee ◽  
Byung-Min Kim
Keyword(s):  
Thermomechanical Treatment ◽  
Process Parameters ◽  
Hot Stamping ◽  
Process Window ◽  
Shaped Part ◽  
Hardness Prediction ◽  
Artificial Neural Network Ann ◽  
Quench Factor Analysis ◽  
Good Agreement

Recently, in order to improve crashworthiness and achieve weight reduction of car body, a hot stamping process has been applied to the production of the part with tailored properties using tailored tool thermomechanical treatment. In the tailored tool thermomechanical treatment process, process parameters influence the mechanical properties of final product such as strength and hardness. Therefore, the prediction of hardness for final product is very important to manufacture hot-stamped part considering various process parameters. The purpose of this study is to propose a process window, which can predict hardness for various process parameters in tailored tool thermomechanical treatment. To determine the process window, finite element (FE) simulation coupled with quench factor analysis (QFA) has been performed for combinations of various process parameters. Subsequently, the process window was constructed through the training of artificial neural network (ANN) and experiment of tailored tool thermomechanical treatment for hat-shaped part was performed to verify effectiveness of hardness prediction. Then, the process parameters were determined from process window for hot stamping of the hat-shaped part with the required distribution of hardness. Hardness predicted by process window was in good agreement with measured one within 3.1% error in additional experiment. Therefore, the suggested process window can be used efficiently for hardness prediction and determination of process parameters in tailored tool thermomechanical treatment of hot-stamping parts.

Interpretation of Transverse Profiles to Determine the Source of Rutting within an Asphalt Pavement System

2005 ◽  
Vol 1905 (1) ◽  
pp. 73-81 ◽  
Author(s):  
Claude Villiers ◽  
Reynaldo Roque ◽  
Bruce Dietrich
Keyword(s):  
Surface Layer ◽  
Literature Review ◽  
Asphalt Pavement ◽  
Asphalt Mixture ◽  
Falling Weight Deflectometer ◽  
Relative Contribution ◽  
Transverse Profile ◽  
Falling Weight ◽  
Different Sources

The transverse profilograph has been recognized as one of the most accurate devices for the measurement of rut depth. However, interpretation of surface transverse profile measurements poses a major challenge in determining the contributions of the different layers to rutting. A literature review has shown that the actual rutting mechanism can be estimated from a surface transverse profile for determination of the relative contribution of the layers to rutting. Unfortunately, much of the research yielded no verification or data. In addition, some techniques presented cannot be used if the rut depth is not well pronounced. Other techniques may be costly and time-consuming. The present research developed an approach that integrates ( a) falling weight deflectometer and core data along with 3.6-m transverse profile measurements to assess the contributions of different pavement layers to rutting and ( b) identifies the presence (or absence) of instability within the asphalt surface layer. This approach can be used regardless of the magnitude of the rut depth. On the basis of the analysis conducted, absolute rut depth should not be used to interpret the performance of the asphalt mixture. In addition, continued instability may not result in an increase in rut depth because the rutted basin broadens as traffic wander compacts or moves the dilated portion of the mixture. The approach developed appears to provide a reasonable way to distinguish between different sources of rutting. The conclusions drawn from analysis of the approach agreed well with observations from the trench cuts taken from four sections.

scholarly journals Comparing Traffic Speed Deflectometer and Falling Weight Deflectometer Data

2018 ◽  
Vol 2672 (40) ◽  
pp. 22-31 ◽  
Author(s):  
Eyal Levenberg ◽  
Matteo Pettinari ◽  
Susanne Baltzer ◽  
Britt Marie Lekven Christensen
Keyword(s):  
Experimental Data ◽  
Loading Conditions ◽  
Falling Weight Deflectometer ◽  
Engineering Community ◽  
Taylor Diagram ◽  
Comparison Results ◽  
Pavement Engineering ◽  
Traffic Speed ◽  
Falling Weight

In recent years the pavement engineering community has shown increasing interest in shifting from a stationary falling weight deflectometer (FWD) to moving testing platforms such as the traffic speed deflectometer (TSD). This paper dealt with comparing TSD measurements against FWD measurements; it focused on the comparison methodology, utilizing experimental data for demonstration. To better account for differences in loading conditions between the two devices a new FWD deflection index was formulated first. This index served as reference/benchmark for assessing the corresponding TSD measurements. Next, a Taylor diagram was proposed for visualizing several comparison statistics. Finally, a modern agreement metric was identified and applied for ranking comparison results across different datasets. Overall, the suggested methodology is deemed generic and highly applicable to future situations, especially for assessing the worth of emerging device upgrades or improved interpretation schemes (or both).

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