Evaluating characteristics of particles’ surface micro-texture of granular materials based on the spectral analysis method

References

• Alabbasi, Y. and Hussein, M., 2019. Geomechanical modelling of railroad ballast: a review. Archives of Computational Methods in Engineering, 12, 1–19.
   Google Scholar
• Alvarez, A.E., Mora, J.C., and Espinosa, L.V., 2018. Quantification of stone-on-stone contact in permeable friction course mixtures based on image analysis. Materials and Design, 165, 462–471.
   Google Scholar
• ASTM D3319-11. 2017. Standard practice for the accelerated polishing of aggregates using the British wheel. West Conshohocken, PA: ASTM International.
   Google Scholar
• ASTM G 133-05. 2016. Standard test method for linearly reciprocating ball-on-flat sliding wear. West Conshohocken, PA: ASTM International.
   Google Scholar
• Attia, M. and Abdelrahman, M., 2010. Variability in resilient modulus of reclaimed asphalt pavement as base layer and its impact on flexible pavement performance. Transportation Research Record, 2167, 18–29.
   Google Scholar
• Bi, J., et al., 2018. Fractal dimensions of granular materials based on grading curves. Journal of Materials in Civil Engineering, 30 (6), 04018083.1–11.
   Web of Science ®Google Scholar
• Byun, Y., et al., 2020. Aggregate properties affecting shear strength and permanent deformation characteristics of unbound–base course materials. Journal of Materials in Civil Engineering, 32 (1), 04019332.
   Web of Science ®Google Scholar
• Celma, C.C., et al., 2017. Contact-induced deformation and damage of rocks used in pavement materials. Materials and Design, 133, 255–265.
   Web of Science ®Google Scholar
• Chen, D., 2015. Study on image-based texture analysis method and prediction of skid-resistance & tire/pavement noise reduction of HMA. PhD thesis, Chang’an University.
   Google Scholar
• Chen, D., et al., 2019. Prediction of asphalt mixture surface texture level and its distributions using mixture design parameters. International Journal of Pavement Engineering, 20 (5), 557–565.
   Web of Science ®Google Scholar
• Chen, D., et al., 2020a. Aggregate micro tribological properties of sponge city permeable pavement base layer under vehicle loading. Construction and Building Materials, 261, 120424.
   Web of Science ®Google Scholar
• Chen, D., et al., 2020b. Prediction of tire–pavement friction based on asphalt mixture surface texture level and its distributions. Road Materials and Pavement Design, 21 (6), 1545–1564.
   Web of Science ®Google Scholar
• Chen, D., 2020. Evaluating asphalt pavement surface texture using 3D digital imaging. International Journal of Pavement Engineering, 21 (4), 416–427.
   Web of Science ®Google Scholar
• FHWA-HIF-11-030. 2011. Aggregate image measurement system 2 (AMIS2) final report, Highways for LIFE technology partnership program federal highway administration, 2011.11.
   Google Scholar
• Gencel, O., et al., 2012. The relations between mechanical strengths and abrasive wear of concrete incorporating the hematite aggregate. International Journal of Pavement Engineering, 13, 235–243.
   Web of Science ®Google Scholar
• Huyan, J., et al., 2020. Image-based coarse-aggregate angularity analysis and evaluation. Journal of Materials in Civil Engineering, 32 (6), 1–23.
   Web of Science ®Google Scholar
• ISO. Standard No. 13473-4. 2008. Characterization of pavement texture by use of surface profiles – part 4: Spectral analysis of texture profiles. International Organization for Standardization.
   Google Scholar
• Iwasaki, A., 2020. Deriving the variance of the discrete Fourier transform test using Parseval's theorem. IEEE Transactions on Information Theory, 66, 1164–1170.
   Web of Science ®Google Scholar
• Jeong, H.Y., et al., 2021. A research for imputation method of photovoltaic power missing data to apply time series models. Journal of Korea Multimedia Society, 24 (9), 1251–1260.
   Google Scholar
• Joseph, K.A.B., Julius, J.K., and Gculisile, M.M., 2013. Three-dimensional laser scanning technique to quantify aggregate and ballast shape properties. Construction and Building Materials, 43, 389–398.
   Web of Science ®Google Scholar
• Kouchaki, S., et al., 2017. Evaluation of aggregates surface micro-texture using spectral analysis. Construction and Building Materials, 456, 944–955.
   Google Scholar
• Lai, J.Z., et al., 2018. Properties and modeling of ultra-high-performance concrete subjected to multiple bullet impacts. Journal of Materials in Civil Engineering, 30, 04018256.
   Web of Science ®Google Scholar
• Li, Q., et al., 2019. 3D characterization of aggregates for pavement skid resistance. Journal of Transportation Engineering Part: B-Pavements, 145, 432–441.
   Web of Science ®Google Scholar
• Li, N., et al., 2020. Numerical simulation of dynamic repetitive load test of unbound aggregate using precision unbound material analyzer. Road Materials and Pavement Design, 21 (6), 1675–1693.
   Web of Science ®Google Scholar
• Li, S.M., Wang, J.R., and Li, X.L., 2018. Theoretical analysis of adaptive harmonic window and its application in frequency extraction of vibration signal. Journal of Central South University, 25, 241–250.
   Web of Science ®Google Scholar
• Liu, Z.H., et al., 2020. Acoustic emission source localization with generalized regression neural network based on time difference mapping method. Experimental Mechanics, 60, 679–694.
   Web of Science ®Google Scholar
• Liu, F.Y., et al., 2022. Microstructure and tribological property of a MXene derived from Ti3AlC2. Materials Research Express, 9 (2), 1–19.
   Web of Science ®Google Scholar
• Nusit, K., et al., 2017. Advanced characteristics of cement-treated materials with respect to strength performance and damage evolution. Journal of Materials in Civil Engineering, 29 (4), 04016255.1–11.
   Web of Science ®Google Scholar
• Paul, D.K. and Gnanendran, C. T., 2016. Characterization of lightly stabilized granular base materials using monotonic and cyclic load flexural testing. Journal of Materials in Civil Engineering, 28 (1), 04015074.1–19.
   Web of Science ®Google Scholar
• Putman, J.A., 2007. Signal processing techniques. Available from: https://www.eeginfo.com/research/researchpapers/Signal_Processing.pdf [Accessed 21 December 2016].
   Google Scholar
• Romero-Fiances, I., et al., 2021. Impact of duration and missing data on the long-term photovoltaic degradation rate estimation. Renewable Energy, 181 (1), 738–748.
   Google Scholar
• Ru, H.W., Zhang, W.X., and Wu, L.L., 2020. A survey of white light interference vertical scan measurement algorithms. Laser and Infrared, 50 (8), 899–906.
   Google Scholar
• Sun, Z.Y., et al., 2020. Quantitative evaluation for shape characteristics of aggregate particles based on 3D point cloud data. Construction and Building Materials, 263 (1), 1–19.
   Google Scholar
• Szerakowska, S., et al., 2018. Spectral method as a tool to examine microtextures of quartz sand-sized grains. Micron, 110, 36–45.
   PubMed Web of Science ®Google Scholar
• Wang, L., et al., 2005. Unified method to quantify aggregate shape angularity and texture using Fourier analysis. Journal of Materials in Civil Engineering, 17 (5), 498–504.
   Web of Science ®Google Scholar
• Wang, R., et al., 2019. Deformation of granular material under continuous rotation of stress principal axes. International Journal of Geomechanics, 19 (4), 04019017.1–19.
   Web of Science ®Google Scholar
• Wei, L., Fwa, T.F., and Zhe, Z., 2005. Wavelet analysis and interpretation of road roughness. Journal of Transportation Engineering, 131 (2), 120–130.
   Web of Science ®Google Scholar
• Wu, J.F., et al., 2018. Simulation on the micro-deval test for the aggregate wear properties measurement. Construction and Building Materials, 180, 445–454.
   Web of Science ®Google Scholar
• Yan, L., et al., 2020. Analysis of stress fields at line contact with consideration of surface topographies. Surface Technology, 50 (12), 294–302.
   Google Scholar
• Yang, X., Chen, S., and You, Z., 2017. 3D voxel-based approach to quantify aggregate angularity and surface texture. Journal of Materials in Civil Engineering, 29, 04017031.
   Web of Science ®Google Scholar
• Yang, H.W., Lourenço, S.D., and Baudet, B.A., 2022. 3D fractal analysis of multi-scale morphology of sand particles with μCT and interferometer. Géotechnique, 72, 20–23.
   Web of Science ®Google Scholar
• Ye, Q.L., et al., 2020. Parallel implementation of empirical mode decomposition for nearly bandlimited signals via polyphase representation. Signal, Image and Video Processing, 14, 225–253.
   Web of Science ®Google Scholar
• Yin, Z., et al., 2017. Modeling mechanical behavior of very coarse granular materials. Journal of Engineering Mechanics, 143 (1), C4016006.1–11.
   Web of Science ®Google Scholar
• Zhang, D.F, 2017. Matlab R2017a artificial intelligence algorithm. Beijing: Electronic Industry Press.
   Google Scholar