Field Study on Unconfined Compressive Strength and Drilling Data of DSM Columns: A Machine Learning Approach-Reference-Cited by-同舟云学术

Field Study on Unconfined Compressive Strength and Drilling Data of DSM Columns: A Machine Learning Approach

Published:2024-06-13 Issue: Volume:176 Page:404-416
ISSN:
Container-title:International Conference on Transportation and Development 2024
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Short-container-title:

Author:

Alavi Seyed Meisam¹¹¹¹,Shakeri Talarposhti Sajjad¹¹¹¹,Khodaei Ardabili Ahmad Ali¹¹¹¹,Aghamolaei Milad¹¹¹¹

Affiliation:

1. Baspar Pey Iranian Company, Tehran, Iran.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485514.036

Reference29 articles.

1. Anitescu C. Atroshchenko E. Alajlan N. and Rabczuk T. 2019. “Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems." 59 (1). https://doi.org/10.32604/cmc.2019.06641.

2. Bruce, M. E. C., Berg, R. R., Filz, G. M., Terashi, M., Yang, D. S., Collin, J. G., and Geotechnica, S. 2013. Federal Highway Administration design manual: Deep mixing for embankment and foundation support. United States. Federal Highway Administation. Offices of Research & Development.

3. Cakiroglu C. Islam K. Bekdaş G. Kim S. and Geem Z. W. 2022. “Interpretable machine learning algorithms to predict the axial capacity of FRP-reinforced concrete columns." 15 (8) 2742.

4. Chen J.-J. Zhang L. Zhang J.-F. Zhu Y.-F. and Wang J.-H. 2013. “Field tests modification and application of deep soil mixing method in soft clay.” 139 (1) 24–34.

5. Chen, T., and Guestrin, C. 2016. “Xgboost: A scalable tree boosting system.” In Proc., Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.