Modulus backcalculation methodology based on full-scale testing road and its rationality and feasibility analysis

References

• Anderson, M., 1989. A data base method for backcalculation of composite pavement layer moduli. Nondestructive Testing of Pavements and Backcalculation of Moduli, No. ASTM STP 1026, 201–216.
   Google Scholar
• Bech, N. D., and Vandenbossche, J. M., 2020. Relationship between backcalculated and estimated asphalt concrete dynamic modulus with respect to falling weight deflectometer load and temperature. Transportation Research Record: Journal of the Transportation Research Board, 2674 (9), 887–897.
   Web of Science ®Google Scholar
• Bro, R., and Smilde, A. K., 2014. Principal component analysis. Analytical Methods, 6 (9), 2812–2831.
   Web of Science ®Google Scholar
• Cao, D., et al., 2020. Effectiveness of static and dynamic backcalculation approaches for asphalt pavement. Canadian Journal of Civil Engineering, 47 (7), 846–855.
   Web of Science ®Google Scholar
• Chen, M., et al., 2020. Effect of curing on mechanical properties of cement-stabilized coral sand in marine environment. Advances in Materials Science and Engineering, 2020, 1–11.
   Web of Science ®Google Scholar
• Choi, J. W., et al., 2010. New layer-moduli back-calculation method based on the constrained extended Kalman filter. Journal of Transportation Engineering, 136 (1), 20–30.
   Google Scholar
• Ghanizadeh, A. R., Heidarabadizadeh, N., and Jalali, F., 2020. Artificial neural network back-calculation of flexible pavements with sensitivity analysis using Garson’s and connection weights algorithms. Innovative Infrastructure Solutions, 5 (2), 1–19.
   Web of Science ®Google Scholar
• Ghuzlan, K. A., Al-Mistarehi, B. W., and Al-Momani, A. S., 2020. Rutting performance of asphalt mixtures with gradations designed using Bailey and conventional Superpave methods. Construction and Building Materials, 261, 119941.
   Web of Science ®Google Scholar
• Han, C., et al., 2021. Application of a hybrid neural network structure for FWD backcalculation based on LTPP database. International Journal of Pavement Engineering, 23, 1–14.
   Web of Science ®Google Scholar
• Huang, Y., et al., 2017. Dynamic modulus test and master curve analysis of asphalt mix with trapezoid beam method. Road Materials and Pavement Design, 18 (sup3), 281–291.
   Web of Science ®Google Scholar
• Irwin, L. H., and Richter, C. A., 2005. History and development of U.S. procedures for falling weight deflectometer calibration. Transportation Research Record: Journal of the Transportation Research Board, 1905 (1), 66–72.
   Google Scholar
• Laurent-Matamoros, P., et al., 2020. Improvements to backcalculation procedure by means of structural analysis based on deflection parameters. Accelerated Pavement Testing to Transport Infrastructure Innovation, 96, 640–648.
   Google Scholar
• Le, D.-V., and Phan, C.-T., 2021. A study on artificial neural networks – genetic algorithm model and its application on back-calculation of road pavement moduli. 2020 Applying New Technology in Green Buildings (ATiGB), 53–59.
   Google Scholar
• Li, M., and Wang, H., 2017. Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parameters. International Journal of Pavement Engineering, 20 (4), 490–498.
   Web of Science ®Google Scholar
• Lytton, R. L., 1989. Backcalculation of pavement layer properties. Nondestructive Testing of Pavements and Backcalculation of Moduli, No. ASTM SP 1026, 7–32.
   Google Scholar
• Ma, X., Dong, Z., and Dong, Y., 2021. Toward asphalt pavement health monitoring with built-in sensors: a novel application to real-time modulus evaluation. IEEE Transactions on Intelligent Transportation Systems, 1–13.
   Web of Science ®Google Scholar
• M. o. H. a. U.-R. D. o. t. P. s. R. o., 2013. The code of construction project management.
   Google Scholar
• Nam, B. H., et al., 2015. Methodology to improve the AASHTO subgrade resilient modulus equation for network-level use. Journal of Transportation Engineering, 141, 12.
   Google Scholar
• Picoux, B., El Ayadi, A., and Petit, C., 2009. Dynamic response of a flexible pavement submitted by impulsive loading. Soil Dynamics and Earthquake Engineering, 29 (5), 845–854.
   Web of Science ®Google Scholar
• Plati, C., Gkyrtis, K., and Loizos, A., 2020. Integrating non-destructive testing data to produce asphalt pavement critical strains. Nondestructive Testing and Evaluation, 36, 1–25.
   Web of Science ®Google Scholar
• Pożarycki, A., Górnaś, P., and Wanatowski, D., 2017. The influence of frequency normalisation of FWD pavement measurements on backcalculated values of stiffness moduli. Road Materials and Pavement Design, 20 (1), 1–19.
   Web of Science ®Google Scholar
• Quan, W., et al., 2021. Wave propagation approach for dynamic responses of transversely isotropic viscoelastic pavement under impact load. Road Materials and Pavement Design, 23, 1–22.
   Web of Science ®Google Scholar
• Rosyidi, S. A. P., et al., 2020. Determination of deflection basin using pavement modelling computer programs and finite element method. Jurnal Teknologi, 82 (4).
   Web of Science ®Google Scholar
• Roussel, J.-M., et al., 2020. Spectral element simulation of heavy weight deflectometer test including layer interface conditions and linear viscoelastic behaviour of bituminous materials. Accelerated Pavement Testing to Transport Infrastructure Innovation, 96, 658–665.
   Google Scholar
• Santoso, J., and Surendro, K., 2019. Determining the number of hidden layers in neural network by using principal component analysis. In: Proceedings of SAI intelligent systems conference, 490–500.
   Google Scholar
• Seo, J.-W., et al., 2009. Evaluation of layer properties of flexible pavement using a pseudo-static analysis procedure of falling weight deflectometer. Construction and Building Materials, 23 (10), 3206–3213.
   Web of Science ®Google Scholar
• Sharma, S., and Das, A., 2008. Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network. Canadian Journal of Civil Engineering, 35 (1), 57–66.
   Web of Science ®Google Scholar
• Sheela, K. G., and Deepa, S. N., 2013. Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering, 2013, 1–11.
   Web of Science ®Google Scholar
• Shen, Z., Yang, H., and Zhang, S., 2021. Neural network approximation: three hidden layers are enough. Neural Networks, 141, 160–173.
   PubMed Web of Science ®Google Scholar
• Shoukry, S. N., and William, G. W., 1999. Performance evaluation of backcalculation algorithms through three-dimensional finite-element modeling of pavement structures. Transportation Research Record: Journal of the Transportation Research Board, 1655 (1), 152–160.
   Google Scholar
• Tutka, P., et al., 2021. Sensitivity analysis of determining the material parameters of an asphalt pavement to measurement errors in backcalculations. Materials, 14 (4), 873.
   PubMed Web of Science ®Google Scholar
• Vujicic, T., et al., 2016. Comparative analysis of methods for determining number of hidden neurons in artificial neural network. In: Central European conference on information and intelligent systems, 219.
   Google Scholar
• Vyas, V., Singh, A. P., and Srivastava, A., 2020. Prediction of asphalt pavement condition using FWD deflection basin parameters and artificial neural networks. Road Materials and Pavement Design, 22, 1–19.
   Web of Science ®Google Scholar
• Wang, H., et al., 2020. Prediction of airfield pavement responses from surface deflections: comparison between the traditional backcalculation approach and the ANN model. Road Materials and Pavement Design, 22, 1–16.
   Web of Science ®Google Scholar
• Yang, X., and You, Z., 2015. New predictive equations for dynamic modulus and phase angle using a nonlinear least-squares regression model. Journal of Materials in Civil Engineering, 27 (3), 04014131.
   Web of Science ®Google Scholar
• Yu, J., et al., 2018. More accurate modulus back-calculation by reducing noise information from in situ–measured asphalt pavement deflection basin using regression model. Construction and Building Materials, 158, 1026–1034.
   Web of Science ®Google Scholar
• Yu, T., et al., 2021. Study on generalized friction coefficient between base and surface layers of asphalt pavement. Journal of Engineering, Design and Technology, https://doi.org/10.1108/JEDT-04-2021-0218.
   Web of Science ®Google Scholar
• Zaabar, I., et al., 2014. Backcalculation of asphalt concrete modulus master curve from field-measured falling weight deflectometer data. Transportation Research Record: Journal of the Transportation Research Board, 2457 (1), 80–92.
   Google Scholar
• Zang, G.-S., Li, L., and Sun, L.-J., 2018. Setting method of the depth to rigid layer in modulus back-calculation. Cictp, 2017, 1135–1144.
   Google Scholar
• Zhu, Z.-h., Ye, Z.-f., and Tang, Y., 2021. Non-destructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers. Journal of Applied Poultry Research, 30, 100203.
   Web of Science ®Google Scholar
• Zihan, Z. U. A., et al., 2020. Mechanistic-based approach to utilize traffic speed deflectometer measurements in backcalculation analysis. Transportation Research Record: Journal of the Transportation Research Board, 2674 (5), 208–222.
   Web of Science ®Google Scholar