All Issue

2023 Vol.65, Issue 3 Preview Page
Evaluation of Maximum Dry Unit Weight Prediction Model Using Deep Neural Network Based on Particle Size Analysis

입도분석에 기반한 Deep Neural Network를 이용한 최대 건조 단위중량 예측 모델 평가

Myeong Hwan Kima†
김 명환a†
aPh. D., Department of Agricultural and Rural Engineering, Chungbuk National University
a충북대학교 농업농촌공학과 박사과정
† Corresponding author Tel.: 010-7350-2582, E-mail: mhkim3306@naver.com
2023. pp. 15-28
PDF XML Citation
Abstract

The compaction properties of the soil change depending on the physical properties, and are also affected by crushing of the particles. Since the particle size distribution of soil affects the engineering properties of the soil, it is necessary to analyze the material properties to understand the compaction characteristics. In this study, the size of each sieve was classified into four in the particle size analysis as a material property, and the compaction characteristics were evaluated by multiple regression and maximum dry unit weight. As a result of maximum dry unit weight prediction, multiple regression analysis showed R2 of 0.70 or more, and DNN analysis showed R2 of 0.80 or more. The reliability of the prediction result analyzed by DNN was evaluated higher than that of multiple regression, and the analysis result of DNN-T showed improved prediction results by 1.87% than DNN. The prediction of maximum dry unit weight using particle size distribution seems to be applied to evaluate the compacting state by identifying the material characteristics of roads and embankments. In addition, the particle size distribution can be used as a parameter for predicting maximum dry unit weight, and it is expected to be of great help in terms of time and cost of applying it to the compaction state evaluation.

Keywords
Maximum dry unit weight
multiple regression
deep neural network
References

REFERENCES

1
M. H. Alawi and M. I. Rajab, Road Materials and Pavement Design, Prediction of California bearing ratio of subbase layer using multiple linear regression models, 14(1); 211-219 (2013)10.1080/14680629.2012.757557
2
J. W. Baek and K. Chung, IEEE Access, Context deep neural network model for predicting depression risk using multiple regression, 8; 18171-18181 (2020)10.1109/access.2020.2968393
3
S. H. Baek, S. W. Yun, J. C. Park and C. Yu, Journal of Agriculture & Life Science, Characteristic of compaction and permeability of soils with the variation of particle distribution and compaction energy, 41(2); 45-50 (2007)
4
Y. Bengio, A. Courville and P. Vincent, IEEE Transactions on Patter Analysis and Machine Intelligence, Representation learning: a review and new perspectives, 35(8); 1798-1828 (2014)10.48550/arXiv.1206.5538
5
A. Bera and A. Ghosh, International Journal of Geotechnical Engineering, Regression model for prediction of optimum moisture content and maximum dry unit weight of fine grained soil, 5; 297-305 (2011)10.3328/ijge.2011.05.03.297-305
6
M. L. Chen, G. J. Wu, B. R. Gan, W. H. Jiang and J. W. Zhou, Advances in Materials Science and Engineering, Physical and compaction properties of granular materials with artificial grading behind the particle size distributions; 1-20, Volume 2018, Article ID 8093571 (2018)10.1155/2018/8093571
7
A. Chinkulkijniwat, E. Man-Koksung, A. Uchaipichat and S. Horpibulsuk, Journal of ASTM International, Compaction characteristics of non-gravel and gravelly soils using a small compaction apparatus, 7(7); 1-15 (2010)
8
S. H. Cho, H. S. Nam, K. J. Ryu and D. K. Ryoo, Journal of Navigation and Port Research, A comparative analysis of the forecasting performance of coal and iron ore in Gwangyang Port using stepwise regression and artificial neural network model, 44(3); 187-194, (in Korean) (2020)10.5394/KINPR.2020.44.3.187
9
Y. Ding, Y. Rao, A. K. Sarmah, X. Huang, B. Pan and D. Liu, International Journal of Geomechanics, Prediction and evaluation of grain size-dependent maximum dry density for gravelly soil, 20(9); 04020153 (2020)10.1061/(asce)gm.1943-5622.0001775
10
A. A. Elgibaly and A. M. Elkamel, Fluid Phase Equilibria, A new correlation for predicting hydrate formation conditions for various gas mixtures and inhibitors, 152; 23-42 (1998)10.1016/s0378-3812(98)00368-9
11
D. Groupe, 2nd ed.. Principle of artificial neural networks, Singapore. World Scientific Publishing Co. (2007)
12
Y. Gurtug and A. Sridharan, Soil and Foundation, Compaction behaviour and prediction of its characteristics of fine grained soils with particular reference to compaction energy, 44(5); 27-36 (2004)10.3208/sandf.44.5_27
13
L. Hertäg and H. Sprekeler, Elife, Learning prediction error neurons in a canonical interneuron circuit, 9; e57541 (2020)
14
A. W. Johnson and J. R. Sallberg, Highway Research Board Bulletin, Factors that influence field compaction of soils, 272; 206 (1960)
15
J. Keijser and H. Sprekeler, PLoS Computational Biology, Optimizing interneuron circuits for compartment-specific feedback inhibition, 18(4); e1009933 (2022)10.1371/journal.pcbi.1009933
16
K. Kim, J. Jeon and H. Choi, Journal of the Korean Data Analysis Society, A comparison study of correlation modeling, 11(6); 3319-3329, (in Korean) (2019)
17
M. H. Kim, C. M. Song and S. E. Eom, KSAE Annual ConferenceEvaluation of permeability coefficient of agriculture reservoirs using deep neural network, Korea, Oct 13-14, 2022, Interburgo Hotel, Daegu. (2022)
18
W. Y. Kim, N. R. Kim and J. Y. Lim, J. Korean Soc. Hazard Mitig, A study on the experiment for classification of construction material properties of reservoir, 17(2); 219-230 (2017)10.9798/kosham.2017.17.2.219
19
Y. G. Kim, H. J. Park, S. G. Lee and E. Y. Cha, Annual Conference of Korea Information Processing Society, Correlation analysis and optimization between parameters using with deep learning, 22(2); 1285-1288, Oct. 28 (2015)
20
Y. S. Kim, S. B. Lee and S. G. Jung, Journal of the Korean Geotechnical Society, The engineering characteristics of weathered sedimentary rock soils - In Taegu and Kyungpook areas -, 11(4); 63-74 (1995)
21
C. W. Lee, S. Y. Park, H. M. Oh and Y. S. Kim, Journal of the Korean Society of Agricultural Engineers, Numerical simulation on seepage and seismic behaviors of poorly-compacted raised reservoir levee, 57(4); 85-99 (2015)10.5389/ksae.2015.57.4.085
22
J. H. Lee, B. S. Hwang, D. H. Chae and W. J. Cho, Journal of the Korean Geo-Environmental Society, Evaluations on the compaction energy effects on the soil compaction at sub-zero temperature, 16(8); 13-18, (in Korean) (2015)10.14481/jkges.2015.16.8.13
23
G. Lumay and N. Vandewalle, Physical Review E, Compaction of anisotropic granular materials: experiments and simulations, 70(1), 051315-1-5 (2004)10.1103/physreve.70.051314
24
R. H. Myers, 2nd ed.. Classical and modern regression with applications, Boston, Mass. Duxbury Press. (1990)
25
S. K. Noh, P. W. Chang and Y. H. Son, Journal of the Korean Society of Agricultural Engineers, Analysis of effect of compaction energy using fragmentation fractal, 48(6); 59-67 (2006)10.5389/ksae.2006.48.6.059
26
Z. Y. Ma, F. N. Dang and H. J. Liao, Granular Matter, Numerical study of the dynamic compaction of gravel soil ground using the discrete element method, 16(6); 881-889 (2014)10.1007/s10035-014-0529-x
27
N. S. Pandian, T. S. Nagaraj and M. Manoj, Geotechnique, Reexamination of compaction characteristics of fine grained soils, 47(2); 363-366 (1997)10.1680/geot.1997.47.2.363
28
H. I. Park, Marine Georesources and Geotechnology, Development of neural network model to estimate the permeability coefficient of soils, 29; 267-278 (2011)10.1080/1064119x.2011.554963
29
H. M. Park, H. S. Park and S. W. Park, Journal of the Korean Geotechnical Society, Mechanical evaluation of compacted granular materials considering particle size distribution, 32(1); 45-53 (2016)10.7843/kgs.2016.32.1.45
30
J. H. Park, Master’s thesis, A study on evaluation of compaction ratio using 10% air voids line; 36-51, (in Korean), Keimyung University. (2010)
31
J. M. Park, Y. B. Han and Y. S. Jang, Journal of Korean Society of Coastal and Ocean Engineers, Estimation of ultimate bearing capacity of gravel compaction piles using nonlinear regression analysis, 25(2); 112-121 (2013)10.9765/kscoe.2013.25.2.112
32
C. S. Park. and J. H. Kim, Journal of the Korean Geo-Environmental Society, Correlation between physical and compaction characteristics of various soils, 18(1); 23-29 (2017)10.14481/jkges.2017.18.1.23
33
E. Perfect and B. D. Kay, Soil and Tillage Research, Applications of fractals in soil and tillage research: A review, 36(1-2); 1-20 (1995)10.1016/0167-1987(96)81397-3
34
G. W. Ring III, J. R. Sallberg and W. H. Collins, Highway Research Board Bulletin, Correlation of compaction and classification test data, 325; 577-587 (1962)
35
A. Sezer, A. B. Göktepe and S. Altun, AIAI-2009 Workshops Proceedings, Estimation of the permeability of granular soils using neuro-fuzzy system (2009)
36
C. Yu, W. S. Kim, K. J. Kang, S. Y. Park and S. Y. Na, KSAE Annual ConferenceA risk evaluation method of agricultural infrastructures with climate change using hybridized fuzzy logic and neural network theory, Korea, Oct 13-14, Interburgo Hotel, Daegu. (2002)
37
T. Y. Yun, International Journal of Highway Engineering, Applicability evaluation of DEM with spherical particles for packing degree estimation, 20(6); 21-30, (in Korean) (2018)10.7855/ijhe.2018.20.6.021
Information
  • Publisher :The Korean Society of Agricultural Engineers
  • Publisher(Ko) :한국농공학회
  • Journal Title :Journal of Korean Society of Agricultural Engineers
  • Journal Title(Ko) :한국농공학회논문집
  • Volume : 65
  • No :3
  • Pages :15-28
  • Received Date : 2023-01-26
  • Revised Date : 2023-03-14
  • Accepted Date : 2023-03-17
  • DOI :https://doi.org/10.5389/KSAE.2023.65.3.015
Journal Informaiton Journal of Korean Society of Agricultural Engineers Journal of Korean Society of Agricultural Engineers
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • crossref funder-registry
  • crosscheck
  • orcid
  • open access
Journal Informaiton Journal Informaiton - close