DIPLOBACK: Neural-Network-Based Backcalculation Program for Composite Pavements

1997 ◽  
Vol 1570 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Lev Khazanovich ◽  
Jeffery Roesler
Keyword(s):  
Neural Network ◽  
Winkler Foundation ◽  
Multilayer Composite ◽  
Falling Weight Deflectometer ◽  
Subgrade Reaction ◽  
Elastic Parameters ◽  
The Neural Networks ◽  
Elastic Layers ◽  
Falling Weight ◽  
Moduli Of Elasticity

A neural-network-based backcalculation procedure is developed for multilayer composite pavement systems. The constructed layers are modeled as compressible elastic layers, whereas the subgrade is modeled as a Winkler foundation. The neural networks are trained to find moduli of elasticity of the constructed layers and a coefficient of subgrade reaction to accurately match a measured deflection profile. The method was verified by theoretically generated deflection profiles and falling weight deflectometer data measurements conducted at Edmonton Municipal Airport, Canada. For the theoretical deflection basins, the results of backcalculation were compared with actual elastic parameters, and excellent agreement was observed. The results of backcalculation using field test data were compared with the results obtained using WESDEF. Similar trends were observed for elastic parameters of all the pavement layers. The backcalculation procedure is implemented in a computer program called DIPLOBACK.

Effect of FWD Testing Position on Modulus of Subgrade Reaction

2014 ◽  
Vol 518 ◽  
pp. 53-59 ◽  
Author(s):  
S.N. Shoukry ◽  
G.W. William ◽  
M.Y. Riad ◽  
J.C. Prucz
Keyword(s):  
Temperature Gradient ◽  
Early Morning ◽  
Slab Thickness ◽  
Positive Temperature ◽  
Falling Weight Deflectometer ◽  
Subgrade Reaction ◽  
K Value ◽  
Modulus Of Subgrade Reaction ◽  
Late Evening ◽  
Falling Weight

This paper discusses the variation of the Modulus of subgrade reaction (k) backcalculated from slab deflection basins, interactive with the location of the Falling Weight Deflectometer (FWD) load pulse, and curling of slabs due to daily temperature variations. The k-value was calculated following the AASHTO design guides procedures, while deflection basins were recorded at an interval of 3 to 4 hours along the day on an instrumented concrete pavement test section in West Virginia. The state of deformation of the slabs are continuously monitored, through dowel bar bending measurements and records of the temperature gradient profiles through the slab thickness, as well as joint openings every 20 minutes. The results indicated that the backcalculated k-values are greatly affected by the positive temperature gradient, and the least variation in (k) was found in the slab center. In order to minimize errors in back-calculations of k-values, it is recommended to perform the FWD test for recording deflection basins in the interior of the slab during late evening or in the early morning.

scholarly journals Calculation of Effective Structural Number Using Simple Neural Network for Some Road Links in Indonesia

2018 ◽  
Vol 14 (2) ◽  
pp. 45
Author(s):  
Siegfried Syafier
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Falling Weight Deflectometer ◽  
Acceptable Error ◽  
Pavement Maintenance ◽  
The Neural Network ◽  
Network Method ◽  
Simple Neural Network ◽  
Falling Weight ◽  
Layer Composition

In the pavement maintenance system, the parameter of effective structural number (SNeff) would be a considered factor in deciding whether a road link would be repaired or not. To calculate this parameter, it is required the testing of Falling Weight Deflectometer (FWD) and information of layer composition and thicknesses. The combination of these information and using the method of AASHTO’93, it can be calculated the SNeff. These two information generally would be gained through the testings of core drill and test pit which would take time and cost. To overcome these problems, the neural network method or precisely the artificial neural network is developed for analysis of pavement structure. From the analysis, it can be said that the neural network of single perceptron can be used for predicting the SNeff with an acceptable error. In general the value of SNeff obtained from neural network calculation is lower than that of AASHTO’93. In this paper it is also recommended to develop the neural network using multi layer perceptron for the use on pavement system analysis that might be decreasing the error.

Dynamic Viscoelastic Analysis of Falling Weight Deflectometer Deflections for Rigid and Flexible Pavements

2015 ◽  
Vol 2525 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Lev Khazanovich ◽  
Abbas Booshehrian
Keyword(s):  
Flexible Pavements ◽  
Viscoelastic Plate ◽  
Hankel Transform ◽  
Winkler Foundation ◽  
Falling Weight Deflectometer ◽  
Response Data ◽  
Proposed Model ◽  
Viscoelastic Effects ◽  
Falling Weight ◽  
Measured Response

This paper investigates the ability of a generalized Westergaard model consisting of a viscoelastic plate on a viscoelastic Winkler foundation to describe deflections of both rigid and flexible pavements under dynamic loading. The pavement response to falling weight deflectometer (FWD) loading was simulated, and a semianalytical solution involving the use of a Hankel transform in space and a finite difference method in time was developed. The obtained solution was used to interpret FWD data collected at the Minnesota Road Research Facility. It was shown that a good match between the simulated and measured response data could be obtained for both rigid and flexible pavements if inertia and viscoelastic effects were accounted for. The proposed model is thus an attractive tool for analysis of pavement–vehicle interaction.

Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network

2008 ◽  
Vol 35 (1) ◽  
pp. 57-66 ◽  
Author(s):  
Sunil Sharma ◽  
Animesh Das
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Falling Weight Deflectometer ◽  
Rigid Layer ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Equidistant Points ◽  
Falling Weight

Efforts have been made in this paper to backcalculate the in situ elastic moduli of asphalt pavement from synthetically derived falling weight deflectometer (FWD) deflections at seven equidistant points. An artificial neural network (ANN) is used as a tool for backcalculation in this work. The ANN is observed to backcalculate layer moduli, both from normal as well as noisy deflection basins, with better accuracy compared with other software, namely, EVERCALC and ExPaS. EVERCALC is a backcalculation software downloaded from the Internet and ExPaS is a backcalculation algorithm developed in-house, based on a “search and expand” approach. Work have been extended further to develop ANN models that can predict a possible rigid layer at the bottom of the pavement and can directly predict the remaining life of the pavement without backcalculating the layer moduli. Finally, a reliability analysis is performed to quantify the performance of backcalculation using an ANN.

Prediction of Layer Moduli from Falling Weight Deflectometer and Surface Wave Measurements Using Artificial Neural Network

1998 ◽  
Vol 1639 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Yongon Kim ◽  
Y. Richard Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surface Wave ◽  
Stress Wave ◽  
Dispersion Analysis ◽  
Forward Model ◽  
Falling Weight Deflectometer ◽  
Phase Velocities ◽  
Analysis Technique ◽  
Falling Weight

A new algorithm for predicting layer moduli using measurements from both falling weight deflectometer (FWD) and surface wave tests is presented. This algorithm employs numerical solutions of a multilayered half-space based on Hankel transforms as a forward model and an artificial neural network (ANN) for the inversion process. Phase velocities for frequencies ranging from 10 Hz to 10,000 Hz are calculated using the forward model for varying pavement structures with a range of layer moduli and thicknesses. These phase velocities, along with the layer moduli and thicknesses, are used to train an ANN to backcalculate layer moduli from dispersion curves (i.e., phase velocity versus frequency curves) constructed from the FWD and stress wave test data. To account for the effect of bedrock on the moduli prediction, another network is trained with layer thicknesses and phase velocities for predicting the depth to bedrock. Combining this network with the network for the moduli prediction results in a sequential dispersion analysis technique in which the depth to bedrock predicted from the first network becomes an input to the second network for predicting layer moduli. FWD and stress wave test measurements from an intact pavement and an asphalt overlay over cracked asphalt layer are processed using the sequential dispersion analysis technique and MODULUS 5.0 backcalculation program. Comparison of the results indicates that the dispersion analysis technique yields less variable subgrade moduli and is more sensitive to changes in the asphalt surface layer, because the high-frequency data from the stress wave test is incorporated.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

scholarly journals An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

2021 ◽  
Vol 13 (11) ◽  
pp. 6194
Author(s):  
Selma Tchoketch_Kebir ◽  
Nawal Cheggaga ◽  
Adrian Ilinca ◽  
Sabri Boulouma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Modeling Process ◽  
Photovoltaic Arrays ◽  
Mode Of Operation ◽  
Different Types ◽  
The Neural Networks ◽  
Operational Modes ◽  
Types Of Faults ◽  
Electrical Data

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

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