Effective Layer Temperature Prediction Model and Temperature Correction via Falling Weight Deflectometer Deflections

2001 ◽  
Vol 1764 (1) ◽  
pp. 97-111 ◽  
Author(s):  
Dong-Yeob Park ◽  
Neeraj Buch ◽  
Karim Chatti
Keyword(s):  
Prediction Model ◽  
Temperature Correction ◽  
Falling Weight Deflectometer ◽  
Temperature Prediction ◽  
Falling Weight ◽  
Effective Layer

Temperature Correction on Falling Weight Deflectometer Measurements

2000 ◽  
Vol 1716 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Dar-Hao Chen ◽  
John Bilyeu ◽  
Huang-Hsiung Lin ◽  
Mike Murphy
Keyword(s):  
Close Agreement ◽  
Temperature Correction ◽  
Falling Weight Deflectometer ◽  
Temperature Dependent ◽  
Different Seasons ◽  
Falling Weight ◽  
Correction Equations

Repeated falling weight deflectometer (FWD) tests were conducted at three sites. The tests were conducted at regular intervals for 2 to 3 consecutive days per location, and also done during different seasons in order that the widest possible range of temperatures could be obtained. The influence of cracks on temperature correction was also investigated. Temperature correction equations for deflection and moduli were developed so that users could be allowed to input their own reference temperatures. For all test pads, only the W1 and W2 deflections were found to be significantly affected by temperature. Comparisons with other reported temperature correction equations showed close agreement for deflection, but not for moduli. Tests were also run on cracked locations. Temperature did not affect the response of the cracked pavement as much as it did the intact pavement. Due to the different temperature-dependent characteristics of intact and cracked locations, the equations developed from the intact locations may not be used on cracked locations.

scholarly journals Temperature Correction of Deflections and Backcalculated Elasticity Moduli Determined from Falling Weight Deflectometer Measurements on Asphalt Pavements

2022 ◽  
Vol 24 (1) ◽  
pp. D1-D8
Author(s):  
Ilja Březina ◽  
Ondřej Machel ◽  
Tomáš Zavřel
Keyword(s):  
Bearing Capacity ◽  
Thermal Gradient ◽  
Temperature Correction ◽  
Asphalt Pavements ◽  
Falling Weight Deflectometer ◽  
Elasticity Moduli ◽  
Similar Comparison ◽  
Pavement Temperature ◽  
Falling Weight

The evaluation of the bearing capacity of asphalt pavements is usually performed by analysing the deflections measured by a Falling Weight Deflectometer (FWD). The deflection changes with the pavement temperature. In evaluation is necessary to consider the thermal gradient of pavement and perform the temperature correction. The article contains an analysis of effects of the pavement temperature on FWD results on the long-term monitored sections. The temperature correction was performed on measured deflections or back-calculated elasticity moduli. The moduli recalculated to the temperature of 20 °C according to both procedures were similar. Comparison of moduli determined by recalculation to moduli backcalculated from the deflection bowls measured at the temperature of 20 °C, has proven smaller differences for the moduli determined from the deflection bowl corrected to the temperature of 20 °C.

scholarly journals The Application of Frequency-Temperature Superposition Principle for Back-Calculation of Falling Weight Deflectometer

2019 ◽  
Vol 10 (1) ◽  
pp. 132
Author(s):  
Jung-Chun Lai ◽  
Jung Liu ◽  
Chien-Wei Huang
Keyword(s):  
Evaluation Model ◽  
Superposition Principle ◽  
Test Site ◽  
Temperature Correction ◽  
Falling Weight Deflectometer ◽  
Temperature Superposition ◽  
Back Calculation ◽  
Falling Weight ◽  
Frequency Temperature

The falling weight deflectometer (FWD) is a widely used nondestructive test (NDT) device in pavement infrastructure. A FWD test measures the surface deflections subjected to an applied impact loading and the modulus of pavement layers can be determined by back-calculating the measured deflections. However, the modulus of asphalt layers is significantly influenced by temperature; hence, the temperature correction must be considered in back-calculation to evaluate the moduli of asphalt layers at a reference temperature. In addition, the in situ temperature at various pavement depths is difficult to measure. A model for evaluating the temperature at various depths must be established to estimate the in situ temperature of asphalt layers. This study collected the temperature data from a FWD test site to establish a temperature-evaluation model for various depths. The cored specimens from the test site were obtained to conduct dynamic modulus tests for asphalt layers. The FWD tests were applied at the FWD test site and the back-calculation was performed with temperature correction using the frequency-temperature superposition principle. The back-calculated moduli of asphalt layers were compared with the master curve of dynamic modulus to verify the application of the frequency-temperature superposition principle for FWD back-calculation. The results show that the proposed temperature-evaluation model can effectively evaluate the temperature at various depths of pavement. Moreover, the frequency-temperature superposition principle can be effectively employed to conduct temperature correction for FWD back-calculation.

Cutting temperature prediction model and parametric optimization in turning aluminium matrix composite

2020 ◽  
Author(s):  
Diptikanta Das ◽  
Kumar Shantanu Prasad ◽  
Harsh Vardhan Khatri ◽  
Rajesh Kumar Mandal ◽  
Chandrika Samal ◽  
...  
Keyword(s):  
Prediction Model ◽  
Matrix Composite ◽  
Parametric Optimization ◽  
Cutting Temperature ◽  
Aluminium Matrix ◽  
Temperature Prediction ◽  
Aluminium Matrix Composite

scholarly journals A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4606
Author(s):  
Sunguk Hong ◽  
Cheoljeong Park ◽  
Seongjin Cho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Thermal Environment ◽  
Management Plan ◽  
Temperature Prediction ◽  
Railway Systems ◽  
Train Speed ◽  
Speed Restrictions ◽  
Entire Network

Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

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