Predicting Loading–Unloading Pile Static Load Test Curves by Using Artificial Neural Networks

Author:

Alzo’ubi A. K.ORCID,Ibrahim Farid

Funder

Abu Dhabi University

Publisher

Springer Science and Business Media LLC

Subject

Geology,Soil Science,Geotechnical Engineering and Engineering Geology,Architecture

Reference28 articles.

1. Abu Kiefa MA (1998) General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron Eng ASCE 124(12):1177–1185

2. Alzo’ubi AK, Ati M, Ibrahim F (2015) Smart framework for predicting drilled shaft capacity based on data mining techniques and GIS data. In: Manzanal D, Sfriso AO (eds) From fundamentals to applications in geotechnics, The Pan American conference on soil mechanics and geotechnical engineering, 15th PCSMGE/8th SCRM/6th IS-BA 2015, 15–18 November, 1909–1915

3. ASTM D1586-11 (2011) Standard test method for standard penetration test (SPT) and split-barrel sampling of soils. ASTM International, West Conshohocken. https://doi.org/10.1520/d1586-11 , www.astm.org

4. Benali A, Ammar N (2011) Prediction of the pile capacity in purely coherent soils using the approach of the artificial neural networks. In: International seminar, innovation and valorization in civil engineering and construction materials, No.: 5O-239, University of sciences and technology, Algiers, Algeria

5. BS 8004:2015. BSI Standards Publication Code of practice for foundations BS 8004:2015 BRITISH STANDARD

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