State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles-Reference-Cited by-同舟云学术

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State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles

Published:2024-04-17 Issue: Volume: Page:1-16
ISSN:1748-6025
Container-title:Geomechanics and Geoengineering
language:en
Short-container-title:Geomechanics and Geoengineering

Author:

Kumar Manish¹^ORCID,Samui Pijush²,Kumar Divesh Ranjan²,Asteris Panagiotis G.³

Affiliation:

1. Department of Civil Engineering, SRM Institute of Science and Technology (SRMIST), Tiruchirappalli, Tamilnadu, India

2. Department of Civil Engineering, NIT Patna, Bihar, India

3. Computational Mechanics Laboratory, School of Pedagogical & Technological Education, Athens, Greece

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17486025.2024.2337702

Reference51 articles.

1. Behavior of a Large Diameter Bored Pile in Drained and Undrained Conditions: Comparative Analysis

2. Experimental analysis of machine learning methods to detect Covid-19 from x-rays

3. Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

4. Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

5. Prediction of cement-based mortars compressive strength using machine learning techniques

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