DATA DIVISION FOR DEVELOPING NEURAL NETWORKS APPLIED TO GEOTECHNICAL ENGINEERING
By Mohamed A. Shahin, Holger R. Maier, Mark B. Jaksa. 2004
340 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/969bae558fa9a8d2c1daa7c8575852eaf82815f6
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In Civil and Structural Engineering
In Safety, Risk, Reliability and Quality
Citations in the corpus
- Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data (2018, methodology): “To avoid overfiting, It is recommended that the available database should be classified into three sets: (1) training, (2) validation, and (3) test subsets (Alavi and Sadrossadat 2016; Sadrossadat et al. 2016a, b; Shahin et al. 2004; Ziaee et al. 2015).”
- Predicting pile dynamic capacity via application of an evolutionary algorithm (2014, methodology): “In this work, the data was randomly divided into two statistically consistent sets, as recommended by Master (1993) and detailed by Shahin et al. (2004).”