Open Access
Issue |
BIO Web Conf.
Volume 151, 2025
International Conference “Mountains: Biodiversity, Landscapes and Cultures” (MBLC-2024)
|
|
---|---|---|
Article Number | 04005 | |
Number of page(s) | 8 | |
Section | Health and Biochemistry | |
DOI | https://doi.org/10.1051/bioconf/202515104005 | |
Published online | 21 January 2025 |
- P. Thanh-Tung et al., “RESILIENT MODULUS OF SOIL USING FOR SUBGRADE OF PAVEMENT: A CASE STUDY IN VIETNAM,” Acta Geodynamica et Geomaterialia, vol. 20, no. 3, 2023. [Google Scholar]
- M. A. Benbouras, L. Sadoudi, and A. Leghouchi, “Prediction of the Resilient Modulus of Subgrade Soil Using Machine-Learning Techniques,” Urbanism. Arhitectura. Constr, vol. 16, pp. 1-14, 2025. [Google Scholar]
- M. Bouatia, “Analysis of Flexible Pavement Structural Behavior Considering Decreased Subgrade Resilient Modulus,” in MATEC Web of Conferences, 2024, vol. 394: EDP Sciences, p. 01004. [CrossRef] [EDP Sciences] [Google Scholar]
- Z. Han and S. K. Vanapalli, “Relationship between resilient modulus and suction for compacted subgrade soils,” Engineering geology, vol. 211, pp. 85-97, 2016. [CrossRef] [Google Scholar]
- D. Andrei, M. W. Witczak, C. W. Schwartz, and J. Uzan, “Harmonized resilient modulus test method for unbound pavement materials,” Transportation Research Record, vol. 1874, no. 1, pp. 29-37, 2004, doi: https://doi.org/10.3141/1874-04. [CrossRef] [Google Scholar]
- D. Kim and J. R. Kim, “Resilient behavior of compacted subgrade soils under the repeated triaxial test,” Construction and Building Materials, vol. 21, no. 7, pp. 1470-1479, 2007, doi: https://doi.org/10.1016/j.conbuildmat.2006.07.006. [CrossRef] [Google Scholar]
- M. Mazari, E. Navarro, I. Abdallah, and S. Nazarian, “Comparison of numerical and experimental responses of pavement systems using various resilient modulus models,” Soils and Foundations, vol. 54, no. 1, pp. 36-44, 2014, doi: https://doi.org/10.1016/j.sandf.2013.12.004. [CrossRef] [Google Scholar]
- U. Asif, M. F. Javed, M. Abuhussain, M. Ali, W. A. Khan, and A. Mohamed, “Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners,” Case Studies in Construction Materials, vol. 20, p. e03135, 2024, doi: https://doi.org/10.1016/j.cscm.2024.e03135. [CrossRef] [Google Scholar]
- Y. Luo, X. Liu, F. Chen, H. Zhang, and X. Xiao, “Numerical simulation on crack– inclusion interaction for rib-to-deck welded joints in orthotropic steel deck,” Metals, vol. 13, no. 8, p. 1402, 2023, doi: https://doi.org/10.3390/met13081402. [CrossRef] [Google Scholar]
- R. Alyousef, M. Khan, K. Arif, M. Fawad, A. M. Hassan, and N. A. Ghamry, “Forecasting the strength characteristics of concrete incorporating waste foundry sand using advance machine algorithms including deep learning,” Case Studies in Construction Materials, vol. 19, p. e02459, 2023, doi: https://doi.org/10.1016/j.cscm.2023.e02459. [CrossRef] [Google Scholar]
- U. Asif, S. A. Memon, M. F. Javed, and J. Kim, “Predictive Modeling and Experimental Validation for Assessing the Mechanical Properties of Cementitious Composites Made with Silica Fume and Ground Granulated Blast Furnace Slag,” Buildings, vol. 14, no. 4, p. 1091, 2024, doi: https://doi.org/10.3390/buildings14041091. [CrossRef] [Google Scholar]
- Y. Hu, A. A. Alaskar, F. Althoey, M. A. Abuhussain, G. Alfalah, and F. Farooq, “Strength evaluation sustainable concrete with waste ingredients at elevated temperature by employing interpretable algorithms: Optimization and hyper tuning,” Materials Today Communications, vol. 36, p. 106467, 2023, doi: https://doi.org/10.1016/j.mtcomm.2023.106467. [CrossRef] [Google Scholar]
- X. Zhang, S. Wang, H. Liu, J. Cui, C. Liu, and X. Meng, “Assessing the impact of inertial load on the buckling behavior of piles with large slenderness ratios in liquefiable deposits,” Soil Dynamics and Earthquake Engineering, vol. 176, p. 108322, 2024, doi: https://doi.org/10.1016/j.soildyn.2023.108322. [CrossRef] [Google Scholar]
- D. Han et al., “LMCA: a lightweight anomaly network traffic detection model integrating adjusted mobilenet and coordinate attention mechanism for IoT,” Telecommunication Systems, vol. 84, no. 4, pp. 549-564, 2023, doi: https://doi.org/10.1016/j.enggeo.2016.06.020. [CrossRef] [Google Scholar]
- Y. Zheng, Y. Wang, and J. Liu, “Research on structure optimization and motion characteristics of wearable medical robotics based on improved particle swarm optimization algorithm,” Future Generation Computer Systems, vol. 129, pp. 187-198, 2022, doi: https://doi.org/10.1016/j.future.2021.11.021. [CrossRef] [Google Scholar]
- L. Wang, W. Zhang, and F. Chen, “Bayesian approach for predicting soil-water characteristic curve from particle-size distribution data,” Energies, vol. 12, no. 15, p. 2992, 2019, doi: https://doi.org/10.3390/en12152992. [CrossRef] [Google Scholar]
- N. Kardani, A. Zhou, S.-L. Shen, and M. Nazem, “Estimating unconfined compressive strength of unsaturated cemented soils using alternative evolutionary approaches,” Transportation Geotechnics, vol. 29, p. 100591, 2021, doi: https://doi.org/10.1016/j.trgeo.2021.100591. [CrossRef] [Google Scholar]
- S. K. Sinha and M. C. Wang, “Artificial neural network prediction models for soil compaction and permeability,” Geotechnical and Geological Engineering, vol. 26, pp. 47-64, 2008. [CrossRef] [Google Scholar]
- R. Morgan, D. Morgan, and H. Finney, “A predictive model for the assessment of soil erosion risk,” Journal of agricultural engineering research, vol. 30, pp. 245-253, 1984, doi: https://doi.org/10.1016/S0021-8634(84)80025-6. [CrossRef] [Google Scholar]
- S. K. Das, P. Samui, and A. K. Sabat, “Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil,” Geotechnical and Geological Engineering, vol. 29, pp. 329-342, 2011, doi: https://doi.org/10.1007/s10706-010-9379-4 [CrossRef] [Google Scholar]
- A. Ghorbani and H. Hasanzadehshooiili, “Prediction of UCS and CBR of microsilica- lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing,” Soils and foundations, vol. 58, no. 1, pp. 34-49, 2018, doi: https://doi.org/10.1016/j.sandf.2017.11.002. [CrossRef] [Google Scholar]
- W.-l. Zou, Z. Han, L.-q. Ding, and X.-q. Wang, “Predicting resilient modulus of compacted subgrade soils under influences of freeze–thaw cycles and moisture using gene expression programming and artificial neural network approaches,” Transportation Geotechnics, vol. 28, p. 100520, 2021, doi: https://doi.org/10.1016/j.trgeo.2021.100520. [CrossRef] [Google Scholar]
- Y. Ren, L. Zhang, and P. N. Suganthan, “Ensemble classification and regression-recent developments, applications and future directions,” IEEE Computational intelligence magazine, vol. 11, no. 1, pp. 41-53, 2016, doi: https://doi.org/10.1109/MCI.2015.2471235. [CrossRef] [Google Scholar]
- L.-q. Ding, Z. Han, W.-l. Zou, and X.-q. Wang, “Characterizing hydro-mechanical behaviours of compacted subgrade soils considering effects of freeze-thaw cycles,” Transportation Geotechnics, vol. 24, p. 100392, 2020, doi: https://doi.org/10.1016/j.trgeo.2020.100392. [CrossRef] [Google Scholar]
- M. T. Rahman, “Evaluation of moisture, suction effects and durability performance of lime stabilized clayey subgrade soils,” 2014. [Google Scholar]
- B. Ghorbani, A. Arulrajah, G. Narsilio, S. Horpibulsuk, and M. W. Bo, “Development of genetic-based models for predicting the resilient modulus of cohesive pavement subgrade soils,” Soils and Foundations, vol. 60, no. 2, pp. 398-412, 2020, doi: https://doi.org/10.1016/j.sandf.2020.02.010. [CrossRef] [Google Scholar]
- P. R. Oskooei, A. Mohammadinia, A. Arulrajah, and S. Horpibulsuk, “Application of artificial neural network models for predicting the resilient modulus of recycled aggregates,” International Journal of Pavement Engineering, vol. 23, no. 4, pp. 1121-1133, 2022. [CrossRef] [Google Scholar]
- N. Kardani, M. Aminpour, M. N. A. Raja, G. Kumar, A. Bardhan, and M. Nazem, “Prediction of the resilient modulus of compacted subgrade soils using ensemble machine learning methods,” Transportation Geotechnics, vol. 36, p. 100827, 2022, doi: https://doi.org/10.1016/j.trgeo.2022.100827. [CrossRef] [Google Scholar]
- A. Alaskar et al., “Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature,” Case Studies in Construction Materials, vol. 18, p. e02199, 2023. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.