Applicability of a CPT-Based Neural Network Solution in Predicting Load-Settlement Responses of Bored Pile
Author:
Affiliation:
1. Assistant Professor, Dept. of Civil Engineering, Kermanshah Univ. of Technology, Kermanshah 67178, Iran (corresponding author)..
2. Assistant Professor, Dept. of Mechanical Engineering, Kermanshah Univ. of Technology, Kermanshah 67178, Iran.
Publisher
American Society of Civil Engineers (ASCE)
Subject
Soil Science
Link
http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29GM.1943-5622.0001125
Reference33 articles.
1. Regressive approach for predicting bearing capacity of bored piles from cone penetration test data
2. Simulating pile load-settlement behavior from CPT data using intelligent computing
3. Alsamman O. M. and Long J. H. (1993). “Prediction of drilled shafts axial capacities using CPT results.” Proc. 3rd Int. Conf. on Case Histories in Geotechnical Engineering Missouri Univ. of Science and Technology Scholars’ Mine Rolla MO.
4. Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms
5. Prediction of pile shaft resistance using cone penetration tests (CPTs)
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