Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN
Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper intr...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/515467 |
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| author | Ali Reza Ghanizadeh Mansour Fakhri |
| author_facet | Ali Reza Ghanizadeh Mansour Fakhri |
| author_sort | Ali Reza Ghanizadeh |
| collection | DOAJ |
| description | Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000) four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN) are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation. |
| format | Article |
| id | doaj-art-7febe1b8fdef408ca6853179fcae9333 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-7febe1b8fdef408ca6853179fcae93332025-02-03T01:23:29ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/515467515467Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANNAli Reza Ghanizadeh0Mansour Fakhri1Department of Civil Engineering, K.N.Toosi University of Technology, No. 1346, Vali Asr Street, Mirdamad Intersection, Tehran 19967-15433, IranDepartment of Civil Engineering, K.N.Toosi University of Technology, No. 1346, Vali Asr Street, Mirdamad Intersection, Tehran 19967-15433, IranFatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000) four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN) are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation.http://dx.doi.org/10.1155/2014/515467 |
| spellingShingle | Ali Reza Ghanizadeh Mansour Fakhri Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN The Scientific World Journal |
| title | Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN |
| title_full | Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN |
| title_fullStr | Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN |
| title_full_unstemmed | Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN |
| title_short | Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN |
| title_sort | prediction of frequency for simulation of asphalt mix fatigue tests using mars and ann |
| url | http://dx.doi.org/10.1155/2014/515467 |
| work_keys_str_mv | AT alirezaghanizadeh predictionoffrequencyforsimulationofasphaltmixfatiguetestsusingmarsandann AT mansourfakhri predictionoffrequencyforsimulationofasphaltmixfatiguetestsusingmarsandann |