Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2019

04.04.2017 | Original Article

An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand

verfasst von: Hossein Moayedi, Abbas Rezaei

Erschienen in: Neural Computing and Applications | Ausgabe 2/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The present study is about under-reamed pile subjected to uplift forces. They are known to be very effective especially against uplift forces. The objective is to develop a simple design formula based on an optimized artificial neural network (ANN) predictive approach model. This formula can calculate the ultimate uplift capacity of under-reamed piles (Pul) embedded in dry cohesionless soil with excellent accuracy. The new generated ANN model was developed by taking into account the key factors such as under-reamed base diameter, angle of enlarged base to the vertical axis, shaft diameter, and embedment ratio. The proposed approach shows excellent agreement with a mean absolute error (MAE) less than 0.262, which is better than previous theories.
Nächster Artikel A new algorithm of modified binary particle swarm optimization based on the Gustafson-Kessel for credit risk assessment

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Nazir R, Moayedi H, Mosallanezhad M, Tourtiz A (2015) Appraisal of reliable skin friction variation in a bored pile. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering 168(1):75–86CrossRef
2.
Mosquera ZSZ, Tsuha CDC, Schiavon JA, Thorel L (2015) Discussion of “field investigation of the axial resistance of helical piles in dense sand”. Can Geotech J 52(8):1190–1194CrossRef
3.
Milad F, Kamal T, Nader H, Erman O (2015) New method for predicting the ultimate bearing capacity of driven piles by using flap number. KSCE J Civ Eng 19(3):611–620CrossRef
4.
Arulmoli K, Martin GR, Gasparro MG, Shahrestani S, Buzzoni G (2004) Design of pile foundations for liquefaction-induced lateral spread displacements. Amer Soc Civil Engineers, New YorkCrossRef
5.
Borel S, Xv I (2001) The design of piled-raft foundations under lateral load. A a Balkema Publishers, Leiden
6.
Tomlinson M, Woodward J (2008) Pile design and construction practice “5th edition”. Taylor and Francis Group, New York
7.
Harris DE, Madabhushi GSP (2015) Uplift capacity of an under-reamed pile foundation. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering 168(6):526–538CrossRef
8.
Zhao N, Geng HX, Ma K, Li KN (2011) Elastic-plastic analysis on damaged multi-tower isolation structure with an enlarged base. In: Zhao JY (ed) Advances in civil engineering, pts 1–6. Trans Tech Publications Ltd, Stafa-Zurich, pp 2550–2554
9.
Sego DC, Biggar KW, Wong G (2003) Enlarged base (belled) piles for use in ice or ice-rich permafrost. J Cold Reg Eng 17(2):68–88CrossRef
10.
Grigoryan AA, Chinenkov YA (1990) Bearing capacity and settlements of bored piles with enlarged bases in collapsible soils. Akademiai Kiado, Budapest
11.
Bartholomew RF (1977) Enlarged base piles form an important class. Structural Engineer 55(12):A7–A7
12.
Nazir R, Moayedi H, Pratikso A, Mosallanezhad M (2015) The uplift load capacity of an enlarged base pier embedded in dry sand. Arab J Geosci 8(9):7285–7296CrossRef
13.
Nazir R, Chuan HS, Niroumand H, Kassim KA (2014) Performance of single vertical helical anchor embedded in dry sand. Measurement 49:42–51CrossRef
14.
Sakr M (2015) Relationship between installation torque and axial capacities of helical piles in cohesionless soils. J Perform Constr Facil 29(6):11CrossRef
15.
Tsuha CDC, Aoki N (2010) Relationship between installation torque and uplift capacity of deep helical piles in sand. Can Geotech J 47(6):635–647CrossRef
16.
Briançon L, Simon B (2011) Performance of pile-supported embankment over soft soil: full-scale experiment. J Geotech Geoenviron 138(4):551–561CrossRef
17.
Honda T, Hirai Y, Sato E (2011) Uplift capacity of belled and multi-belled piles in dense sand. Soils Found 51(3):483–496CrossRef
18.
Chae D, Cho W, Na HY (2012) Uplift capacity of belled pile in weathered sandstones. International Journal of Offshore and Polar Engineering 22(4):297–305
19.
Yao WJ, Chen SP (2014) Elastic-plastic analytical solutions of deformation of uplift belled pile. Teh Vjesn 21(6):1201–1211
20.
Lin JG, Hsu SY, Lin SS (2015) The new method to evaluate the uplift capacity of belled piles in sandy soil. J Mar Sci Technol-Taiwan 23(4):523–533
21.
Ahmadi H, Hajialilue-Bonab M (2012) Experimental and analytical investigations on bearing capacity of strip footing in reinforced sand backfills and flexible retaining wall. Acta Geotech 7(4):357–373CrossRef
22.
Horpibulsuk S, Niramitkornburee A (2010) Pullout resistance of bearing reinforcement embedded in sand. Soils Found 50(2):215–226CrossRef
23.
El Sawwaf M, Nazir A (2006) The effect of soil reinforcement on pullout resistance of an existing vertical anchor plate in sand. Comput Geotech 33(3):167–176CrossRef
24.
Tafreshi SNM, Javadi S, Dawson AR (2014) Influence of geocell reinforcement on uplift response of belled piles. Acta Geotech 9(3):513–528CrossRef
25.
Suman S, Das SK, Mohanty R (2016) Prediction of friction capacity of driven piles in clay using artificial intelligence techniques. Int J Geotech Eng 10(5):469–475CrossRef
26.
Momeni E, Nazir R, Armaghani DJ, Maizir H (2015) Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Sci Res J 19(1):85–93CrossRef
27.
Shahin MA (2016) State-of-the-art review of some artificial intelligence applications in pile foundations. Geosci Front 7(1):33–44CrossRef
28.
Shahin MA (2014) Artificial intelligence for modeling load-settlement response of axially loaded bored piles. Crc Press-Taylor & Francis Group, Boca RatonCrossRef
29.
Kordjazi A, Nejad FP, Jaksa MB (2014) Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data. Comput Geotech 55:91–102CrossRef
30.
Baziar MH, Kashkooli A, Saeedi-Azizkandi A (2012) Prediction of pile shaft resistance using cone penetration tests (CPTs). Comput Geotech 45:74–82CrossRef
31.
Alkroosh I, Nikraz H (2014) Predicting pile dynamic capacity via application of an evolutionary algorithm. Soils Found 54(2):233–242CrossRef
32.
Zhang CH, Yang SQ, Zhang JG, Xie JH, Zhang JL (2010) The numerical simulation of low strain dynamic response for foundation piles and the application for defect diagnosis by artificial neural networks. Science Press Beijing, Beijing
33.
Tarawneh B, Imam R (2014) Regression versus artificial neural networks: predicting pile setup from empirical data. KSCE J Civ Eng 18(4):1018–1027CrossRef
34.
Hanna AM, Morcous G, Helmy M (2004) Efficiency of pile groups installed in cohesionless soil using artificial neural networks. Can Geotech J 41(6):1241–1249CrossRef
35.
Nejad FP, Jaksa MB, Kakhi M, McCabe BA (2009) Prediction of pile settlement using artificial neural networks based on standard penetration test data. Comput Geotech 36(7):1125–1133CrossRef
36.
Liang BL, Gao Y, Zhu L, Lv GB, Li X (2005) Prediction of pile displacement using PSO algorithm and artificial neural networks. China Univ Geosciences Press, Wuhan
37.
Rahman M, Wang J, Deng W, Carter J (2001) A neural network model for the uplift capacity of suction caissons. Comput Geotech 28(4):269–287CrossRef
38.
Ardalan H, Eslami A, Nariman-Zadeh N (2009) Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms. Comput Geotech 36(4):616–625CrossRef
39.
Alavi AH, Aminian P, Gandomi AH, Esmaeili MA (2011) Genetic-based modeling of uplift capacity of suction caissons. Expert Syst Appl 38(10):12608–12618CrossRef
40.
Cheng M-Y, Cao M-T, Tran D-H (2014) A hybrid fuzzy inference model based on RBFNN and artificial bee colony for predicting the uplift capacity of suction caissons. Autom Constr 41:60–69CrossRef
41.
Hagan M, Demuth H, Beale M (1996) Neural network design. PWS Pub, Boston ISBN 7-111-10841-8
42.
Fierro R, Lewis F (1999) Multilayer feedforward networks are universal approximators. IEEE Trans Syst Man, Cybern 29(6):649–654CrossRef
Metadaten
Titel
An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand
verfasst von
Hossein Moayedi
Abbas Rezaei
Publikationsdatum
04.04.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2990-z

Weitere Artikel der Ausgabe 2/2019

Neural Computing and Applications 2/2019 Zur Ausgabe

Original Article

An efficient traffic sign recognition based on graph embedding features

Original Article

Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling

Original Article

Object detection using hybridization of static and dynamic feature spaces and its exploitation by ensemble classification

Original Article

Shallow and wide fractional max-pooling network for image classification

Original Article

Optimal position and rating of DG in distribution networks by ABC–CS from load flow solutions illustrated by fuzzy-PSO

Original Article

Combined heat and power economic dispatch problem solution by implementation of whale optimization method