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Discrete Aggregate Mass Calculation Method for Visual Detection of Aggregate Gradation and Elongated and Flat Aggregate Contents

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Trends on Construction in the Digital Era (ISIC 2022)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 306))

Abstract

Aggregate gradation and elongated and flat aggregate contents strongly affect the performance of asphalt mixtures. During the visual detection of these two indexes, the morphology in a single view is typically used for mass calculation. However, it has a significant error and affects the detection accuracy. Therefore, in this study, the morphologies of an aggregate from multiple views were collected during falling. Size features were also extracted for mass calculations. Two mass calculation methods, the multi-view equivalent volume models (MEVMs) and ensemble regression learning model (ERLM), were proposed in this study. MEVMs were constructed using multi-view shape features. The relationships between pixel volumes of MEVMs and actual aggregate mass were established through the least square method for mass calculations. Correlation analyses of multi-view size features were conducted and weakly correlated features were eliminated. The ERLM was combined with the K-nearest neighbor algorithm, multi-layer perceptron neural network, support vector regression algorithm, and ensemble decision tree using an adaptive weight assignment algorithm. The ERLM was trained with processed multi-view features for mass calculations. Finally, the feasibility of MEVMs and ERLM were verified through mass calculations of aggregates with different particle sizes and shapes. Both methods showed significantly improved correlation and accuracy, with the ERLM showing stronger generalization ability in particle size and shape scales than that of MEVMs. Therefore, the ERLM could be effectively applied for the visual detection of aggregate gradation and elongated and flat aggregate contents. The application of the proposed methods was verified in practical road engineering.

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References

  1. Feng, W., Yue, X., Peide, C., Tao, M., Dongliang, K.: Effect of aggregate morphologies and compaction methods on the skeleton structures in asphalt mixtures. Constr. Build. Mater. 263, 120220 (2020)

    Article  Google Scholar 

  2. Stempkowska, A., Gawenda, T., Ostrowski, K.A., Saramak, D., Surowiak, A.: Impact of the geometrical parameters of dolomite coarse aggregate on the thermal and mechanic properties of preplaced aggregate concrete. Materials 13(19), 4358 (2020)

    Article  Google Scholar 

  3. Fang, M., Park, D., Singuranayo, J., Chen, H., Li, Y.: Aggregate gradation theory, design and its impact on asphalt pavement performance: a review. Int. J. Pavement Eng. 20(12), 1–17 (2018)

    Google Scholar 

  4. Gong, F., Zhou, X., You, Z., Liu, Y., Chen, S.: Using discrete element models to track movement of coarse aggregates during compaction of asphalt mixture. Constr. Build. Mater. 189, 338–351 (2018)

    Article  Google Scholar 

  5. Zhang, J., Li, X., Ma, W., Pei, J.: Characterizing heterogeneity of asphalt mixture based on aggregate particles movements. Iran. J. Sci. Technol. Trans. Civ. Eng. 43(1), 81–91 (2018). https://doi.org/10.1007/s40996-018-0125-0

    Article  Google Scholar 

  6. Burak, S., Amir, O., Ali, T.: Effect of aggregate shape on the surface properties of flexible pavement. KSCE J. Civ. Eng. 18(5), 1364–1371 (2014). https://doi.org/10.1007/s12205-014-0516-0

    Article  Google Scholar 

  7. Lucas, J., Jorge, L., Babadopulos, L., Soares, J.: Effect of aggregate shape properties and binder’s adhesiveness to aggregate on results of compression and tension/compression tests on hot mix asphalt. Mater. Struct. 53(2), 1–15 (2020)

    Google Scholar 

  8. Wang, S., Miao, Y., Wang, L.: Investigation of the force evolution in aggregate blend compaction process and the effect of elongated and flat particles using DEM. Constr. Build. Mater. 258, 119674 (2020)

    Article  Google Scholar 

  9. Aïssoun, B.M., Hwang, S.-D., Khayat, K.H.: Influence of aggregate characteristics on workability of superworkable concrete. Mater. Struct. 49(1–2), 597–609 (2015). https://doi.org/10.1617/s11527-015-0522-9

    Article  Google Scholar 

  10. Fangyuan, G., Yu, L., Xiaodong, Z., Zhanping, Y.: Lab assessment and discrete element modeling of asphalt mixture during compaction with elongated and flat coarse aggregates. Constr. Build. Mater. 182, 573–579 (2018)

    Article  Google Scholar 

  11. Liu, L., Shen, D., Chen, H., Xu, W.: Aggregate shape effect on the diffusivity of mortar: a 3D numerical investigation by random packing models of ellipsoidal particles and of convex polyhedral particles. Comput. Struct. 144, 40–51 (2014)

    Article  Google Scholar 

  12. JTG F40–2004, Technical specifications for construction of highway asphalt pavements. China Communications Press, Beijing (2004)

    Google Scholar 

  13. Mahdi, H., Rassoul, A., Behzad, N., Pooyan, R.: An industrial image processing-based approach for estimation of iron ore green pellet size distribution. Powder Technol. 303, 260–268 (2016)

    Article  Google Scholar 

  14. Bonadonna, C., Bagheri, G.H., Manzella, I., Vonlanthen, P.: On the characterization of size and shape of irregular particles. Powder Technol. Int. J. Sci. Technol. Wet Dry Part. Syst. 270, 141–153 (2015)

    Google Scholar 

  15. Chen, A., Chen, B., Feng, C.: Image analysis algorithm and verification for on-line molecular sieve size and shape inspection. Adv. Powder Technol. 25, 508–513 (2014)

    Article  Google Scholar 

  16. Masad, E., Button, J., Papagiannakis, T.: Fine-aggregate angularity: automated image analysis approach. Transp. Res. Rec. J. Transp. Res. Board 1721, 66–72 (2000)

    Article  Google Scholar 

  17. Masad, E., Button, J.: Unified imaging approach for measuring aggregate angularity and texture. Comput.-Aided Civil Infrastruct. Eng. 15, 273–280 (2000)

    Article  Google Scholar 

  18. Al-Rousan, T., Masad, E., Tutumluer, E., Pan, T.: Evaluation of image analysis techniques for quantifying aggregate shape characteristics. Constr. Build. Mater. 21, 978–990 (2007)

    Article  Google Scholar 

  19. Bessa, I., Branco, V., Soares, J.: Evaluation of different digital image processing software for aggregates and hot mix asphalt characterizations. Constr. Build. Mater. 37(3), 370–378 (2012)

    Article  Google Scholar 

  20. Chao, X., Huining, X., Yiqiu, T., Xueyan, L., Changhong, Z., Tom, S.: Gradation measurement of asphalt mixture by X-Ray CT images and digital image processing methods. Measurement 132, 377–386 (2019)

    Article  Google Scholar 

  21. Liwan, S., Duanyi, W., Changning, J., Ben, L., Hehao, L.: Measurement of coarse aggregates movement characteristics within asphalt mixture using digital image processing methods. Measurement 163, 107948 (2020)

    Article  Google Scholar 

  22. Prudencio, L., de Oliveira, A., Weidmann, D., Damo, G.: Particle shape analysis of fine aggregate using a simplified digital image processing method. Mag. Concr. Res. 65(1), 27–36 (2013)

    Article  Google Scholar 

  23. Hamzeloo, E., Massinaei, M., Mehrshad, N.: Estimation of particle size distribution on an industrial conveyor belt using image analysis and neural networks. Powder Technol. 261, 185–190 (2014)

    Article  Google Scholar 

  24. Damadipour, M., Nazarpour, M., Alami, M.T.: Evaluation of particle size distribution using an efficient approach based on image processing techniques. Iranian J. Sci. Technol. Trans. Civ. Eng. 43(1), 429–441 (2018). https://doi.org/10.1007/s40996-018-0175-3

    Article  Google Scholar 

  25. Pei, L., et al.: Pavement aggregate shape classification based on extreme gradient boosting - sciencedirect. Constr. Build. Mater. 256, 119356 (2020)

    Article  Google Scholar 

  26. Ying, G., et al.: Variability evaluation of gradation for asphalt mixture in asphalt pavement construction. Autom. Constr. 128, 103742 (2021)

    Article  Google Scholar 

  27. Liao, C., Tarng, Y.: On-line automatic optical inspection system for coarse particle size distribution. Powder Technol. 189, 508–513 (2009)

    Article  Google Scholar 

  28. Yang, J., Chen, S.: An online detection system for aggregate sizes and shapes based on digital image processing. Mineral. Petrol. 111(1), 135–144 (2016). https://doi.org/10.1007/s00710-016-0458-y

    Article  Google Scholar 

  29. Jianhong, Y., Huaiying, F.: Research into different methods for measuring the particle-size distribution of aggregates: an experimental comparison. Constr. Build. Mater. 221, 469–478 (2019)

    Article  Google Scholar 

  30. Rongji, C., Yulong, Z., Ying, G., Xiaoming, H., Lili, Z.: Effects of flow rates and layer thicknesses for aggregate conveying process on the prediction accuracy of aggregate gradation by image segmentation based on machine vision. Constr. Build. Mater. 222, 566–578 (2019)

    Article  Google Scholar 

  31. Morteza, V., Majid, S., Ali, R.: Introducing a simple method to determine aggregate gradation of hot mix asphalt using image processing. Int. J. Pavement Eng. 15(2), 142–150 (2014)

    Article  Google Scholar 

  32. Qinglin, G., Yanshan, B., Lili, L., Yubo, J., Jinglin, T., Chengxiu, X.: Stereological estimation of aggregate gradation using digital image of asphalt mixture. Constr. Build. Mater. 94, 458–466 (2015)

    Article  Google Scholar 

  33. Fan, W., Chen, Z., Luo, Z., Guo, B.: An aggregate gradation detection method based on multi-view information fusion. Powder Technol. 388, 7–16 (2021)

    Article  Google Scholar 

  34. Fan, W., Chen, Z., Luo, Z., Guo, B.: A detection method of elongated and flat aggregate particles based on multi-view shape features with a single camera. Adv. Powder Technol. 32(11), 4004–4016 (2021)

    Article  Google Scholar 

  35. Li, W., Sha, A., Sun, Z., Yuan, M., Ren, B.: Mineral mixture gradation on-line detection technology based on optoelectronics imaging. Chin. J. Highway Transp. 26(1), 38–43 (2013)

    Google Scholar 

  36. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    Article  MATH  Google Scholar 

  37. Pei, L., Sun, Z., Hu, Y., Li, W., Gao, Y., Hao, X.: Neural network model for road aggregate size calculation based on multiple features. J. S. Chin. Univ. Technol. (Nat. Sci.) 48(6), 77–86 (2020)

    Google Scholar 

  38. Jiateng, Y., Dewang, C., Yidong, L.: Smart train operation algorithms based on expert knowledge and ensemble CART for the electric locomotive. Knowl.-Based Syst. 92(15), 78–91 (2016)

    Google Scholar 

Download references

Acknowledgements

This study was financially supported by National Key Research and Development Program of China (No. 2021YFB2600602), National Key Research and Development Program of China (No. 2021YFB2600600), and National Natural Science Foundation of China (No. 51878168). These financial supports are gratefully acknowledged.

Author information

Authors and Affiliations

  1. School of Transportation, Southeast University, Nanjing, 211189, China

    Zeqi Chen, Ying Gao, Siyu Chen, Tao Ma & Xiaoming Huang

  2. Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi ’an, 710064, China

    Jiupeng Zhang

Authors
  1. Zeqi Chen
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  2. Ying Gao
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  3. Jiupeng Zhang
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  4. Siyu Chen
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  5. Tao Ma
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  6. Xiaoming Huang
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Corresponding author

Correspondence to Ying Gao .

Editor information

Editors and Affiliations

  1. ISISE, University of Minho, Guimaraes, Portugal

    António Gomes Correia

  2. ISISE, University of Minho, Guimaraes, Portugal

    Miguel Azenha

  3. School of Architecture, University of Minho, Guimarães, Portugal

    Paulo J. S. Cruz

  4. ALGORITMI Research Center / LASI, University of Minho, Braga, Portugal

    Paulo Novais

  5. CTAC, University of Minho, Guimarães, Portugal

    Paulo Pereira

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Chen, Z., Gao, Y., Zhang, J., Chen, S., Ma, T., Huang, X. (2023). Discrete Aggregate Mass Calculation Method for Visual Detection of Aggregate Gradation and Elongated and Flat Aggregate Contents. In: Gomes Correia, A., Azenha, M., Cruz, P.J.S., Novais, P., Pereira, P. (eds) Trends on Construction in the Digital Era. ISIC 2022. Lecture Notes in Civil Engineering, vol 306. Springer, Cham. https://doi.org/10.1007/978-3-031-20241-4_27

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  • DOI: https://doi.org/10.1007/978-3-031-20241-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20240-7

  • Online ISBN: 978-3-031-20241-4

  • eBook Packages: EngineeringEngineering (R0)

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