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.

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.

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.

scholarly journals Measurements of the elastic modulus of pavement subgrade layers using the SASW and FWD test methods

2015 ◽  
Vol 10 (2) ◽  
pp. 174-181 ◽  
Author(s):  
Nur Izzi Md. Yusoff ◽  
Sentot Hardwiyono ◽  
Norfarah Nadia Ismail ◽  
Mohd Raihan Taha ◽  
Sri Atmaja P. Rosyidi ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Elastic Modulus ◽  
Surface Wave ◽  
Time Lag ◽  
Test Methods ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Surface Wave Propagation ◽  
Evaluation And Monitoring ◽  
Falling Weight

In pavement management systems, deflection basin tests, such as the Falling Weight Deflectometer test, are common techniques that are widely used, while the surface wave test, i.e. the Spectral Analysis of Surface Wave test, is recently employed as an alternative technique in pavement evaluation and monitoring. In this paper, the performance of both dynamic non-destructive tests on pavement subgrade investigation is presented. Surface wave propagation between a set of receivers was transformed into the frequency domain using the Fast Fourier Transform technique and subsequently a phase spectrum was produced to measure the time lag between receivers. Using the phase difference method, an experimental dispersion curve was generated. Inversion analysis based on the 3-D stiffness matrix method was then performed to produce a shear wave velocity profile. The elastic modulus of pavement layers was calculated based on linear elastic theory. In the Falling Weight Deflectometer test, seven geophones were used to collect in situ deflection data. Based on a back-calculation procedure with the ELMOD software, the elastic modulus of each flexible pavement layer can be obtained. Both techniques are able to comprehensively investigate the elastic modulus of the subgrade layer in existing pavement non-destructively. The elastic modulus between the Spectral Analysis of Surface Wave method and the Falling Weight Deflectometer test on the subgrade layer is observed to be in a good agreement. A correlation of the elastic modulus of thesubgrade layer from both techniques is also presented.

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.

scholarly journals Voltage instability analysis based on modal analysis technique and artificial neural network

2019 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Ahmad Fateh Mohamad Nor ◽  
Marizan Sulaiman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Modal Analysis ◽  
Electric Power System ◽  
Test Power ◽  
High Tendency ◽  
Voltage Instability ◽  
Analysis Technique ◽  
Artificial Neural

<span>One of the paramount importance in the operation of electrical power system operation order to sustain the equilibrium of the system is the stability of the load buses’ voltages. If the load buses voltages are not stable, this can cause serious problem especially power system blackout. This paper presents the analysis of voltage instability in electric power system by using modal analysis technique. However, one of the challenges of modal analysis is the intencive and complex calculation procedures. In order to overcome that, this paper implements Artificial Neural Network (ANN) to improve the implementation of modal analysis technique. ANN is used to determine the participation factors obtained from the modal analysis technique. The results show that modal analysis technique is able to show which bus is close towards experiencing voltage instability. In addition, the results also show that ANN is able to predict the values of participation factors. A load bus is considered a weak bus if the bus has high tendency towards experiencing voltage instability. IEEE-14 bus test power system has been chosen as the test power system.</span>

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