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
Engineering with Computers
Erschienen in: Engineering with Computers 1/2018

11.04.2017 | Original Article

A new approach for prediction of collapse settlement of sandy gravel soils

verfasst von: Sepehr Soleimani, Pengcheng Jiao, Shabnam Rajaei, Rana Forsati

Erschienen in: Engineering with Computers | Ausgabe 1/2018

Einloggen

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

search-config
loading …

Abstract

Collapse settlement of the granular soils has become one of the main concerns of geotechnical engineers since their increased usage for constructing rockfill dams. Behavioral characterization of soil collapse settlement is a challenging task because this phenomenon is influenced by several parameters. In this study, a multi-gene genetic programming (MGGP) approach is proposed for the prediction of the collapse settlement of sandy gravel soils. The main goal is to formulate the collapse settlement and coefficient of stress release (CSR) in terms of sand content, normal stress, shear stress level, and relative density. The proposed models are compared with linear regression and ANN models. The results indicate that the MGGP models have superb accuracy and efficiency while they are simple and practical. The contribution of each parameter is evaluated through a sensitivity analysis. A parametric study is performed, and the obtained results are discussed.
Vorheriger Artikel Sustained CAD/CAE integration: integrating with successive versions of step or IGES files
Nächster Artikel Back analysis for mechanical parameters of surrounding rock for underground roadways based on new neural network

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

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

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
Literatur
1.
Penman A (1992) Rockfill for embankment dams. In: E Ovando, G Auvinet, W Paniagua, J DõÂaz (eds) Raul J Marsal Volume (eds) pp 303–327
2.
Wei K (2012) Study on collapse behaviors of coarse grained soils. Periodica Polytechnica Civil Engineering 56(2):245CrossRef
3.
Sowers G, Williams R, Wallace T (1965) Compressibility of broken rock and the settlement of rockfills. Proc. 6th ICSMFE, pp 561–565
4.
Marsal R, Arellano L, Guzmán M, Adame H (1976) Infernillo: behavior of dams built in Mexico. Instituto de Ingeniería, UNAM, Mexico
5.
Naylor D, Maranha J, Neves EMD, Pinto AV (1997) A back-analysis of Beliche Dam. Geotechnique 47(2):221–233CrossRef
6.
Barden L, McGown A, Collins K (1973) The collapse mechanism in partly saturated soil. Eng Geol 7(1):49–60CrossRef
7.
Lawton EC, Fragaszy RJ, Hardcastle JH (1991) Stress ratio effects on collapse of compacted clayey sand. J Geotechn Eng 117(5):714–730CrossRef
8.
Marsal R (1973) Mechanical properties of rockfill. In: Hirschfeld RC, Poulos SJ (eds) Embankment dam engineering, Casagrande volume. Wiley, New York, pp 109–200
9.
Alonso EE, Cardoso R (2010) Behavior of materials for earth and rockfill dams: perspective from unsaturated soil mechanics. Front Archit Civil Eng China 4(1):1–39CrossRef
10.
Mahinroosta R, Oshtaghi V (2013) Effect of saturation on the shear strength and collapse settlement of gravelly material using direct shear test apparatus. Sharif Civ Eng J 29(1):103–114
11.
Lawton EC, Fragaszy RJ, Hetherington MD (1992) Review of wetting-induced collapse in compacted soil. J Geotechn Eng 118(9):1376–1394CrossRef
12.
Marachi ND (1969) Strength and deformation characteristics of rockfill materials. University of California, Berkeley
13.
Terzaghi K (1960) Discussion of” Rockfill dams: salt springs and lower bear river concrete face dams”. Trans Am Soc Civil Eng 125(2):139–148
14.
Nobari E, Duncan JM (1972) Effect of reservoir filling on stresses and movement in earth and rockfill dams. A report of an investigation. DTIC Document
15.
Atkinson BK, Meredith PG (1987) The theory of subcritical crack growth with applications to minerals and rocks. Fract Mech Rock 2:111–166CrossRef
16.
Lama R, Vutukuri V (1978) Handbook on mechanical properties of rocks-testing techniques and results. Volume 2, vol 3. vol Monograph
17.
Oldecop LA, Alonso EE (2001) A model for rockfill compressibility. Géotechnique 51(2):127–139
18.
Oshtaghi V, Roosta M (2010) Changes in the stress and strain state in dry gravelly material due to saturation. In: 4th International Conference on Geotechnical Engineering and Soil Mechanics, Tehran, Iran, Paper No. TVTMAH
19.
Hunter G, Fell R (2002) The deformation behaviour of rockfill. School of Civil and Environmental Engineering, University of New South Wales, Sydney
20.
Alonso E (2003) Exploring the limits of unsaturated soil mechanics: The behavior of coarse granular soil and rockfill. The 11th Buchanan Lecture University of Texas A&M
21.
Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239CrossRef
22.
Alavi AH, Gandomi AH (2011) Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Comput Struct 89(23):2176–2194CrossRef
23.
Hossein Alavi A, Hossein Gandomi A, Mollahassani A, Akbar Heshmati A, Rashed A (2010) Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci 173(3):368–379CrossRef
24.
Alavi AH, Hasni H, Lajnef N, Chatti K, Faridazar F (2016) Damage detection using self-powered wireless sensor data: an evolutionary approach. Measurement 82:254–283
25.
Alavi AH, Hasni H, Lajnef N, Chatti K, Faridazar F (2016) An intelligent structural damage detection approach based on self-powered wireless sensor data. Autom Const 62:24–44CrossRef
26.
Rajasekaran S, Suresh D, Pai GV (2002) Application of sequential learning neural networks to civil engineering modeling problems. Eng Comp 18(2):138–147CrossRef
27.
Armaghani DJ, Mohamad ET, Hajihassani M, Yagiz S, Motaghedi H (2016) Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Eng Comp 32(2):189–206
28.
Gordan B, Armaghani DJ, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comp 32(1):85–97CrossRef
29.
Hasanzadehshooiili H, Mahinroosta R, Lakirouhani A, Oshtaghi V (2014) Using artificial neural network (ANN) in prediction of collapse settlements of sandy gravels. Arab J Geosci 7(6):2303–2314CrossRef
30.
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT press
31.
Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Statistics and Computing 4(2):87–112CrossRef
32.
Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput Appl 21(1):171–187CrossRef
33.
Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems. Neural Comput Appl 21(1):189–201CrossRef
34.
Gandomi AH, Sajedi S, Kiani B, Huang Q (2016) Genetic programming for experimental big data mining: A case study on concrete creep formulation. Autom Constr 70:89–97CrossRef
35.
Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. In: Proceedings of the International multiconference of engineers and computer scientists. Citeseer, pp 77–80
36.
Alonso E, Oldecop L, Rahardjo H, Toll D, Leong E (2000) Fundamentals of rockfill collapse. In: Unsaturated soils for Asia. Proceedings of the Asian Conference on Unsaturated Soils, UNSAT-Asia 2000, Singapore, 18–19 May 2000. AA Balkema, pp 3–13
37.
Basma AA, Kallas N (2004) Modeling soil collapse by artificial neural networks. Geotech Geol Eng 22(3):427–438CrossRef
38.
Searson D (2009) GPTIPS: Genetic programming and symbolic regression for MATLAB. User Guide 2010
39.
Ryan T (1997) Modern Regression Methods 474, Wiley, New York
40.
Alexander G, Alexander T (2002) Beware of Q2. J Mol Graph Model 20(4):269–276CrossRef
41.
Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27(3):302–313CrossRef
42.
Alavi AH, Gandomi AH, Sahab MG, Gandomi M (2010) Multi expression programming: a new approach to formulation of soil classification. Eng Comp 26(2):111–118CrossRef
43.
Mousavi SM, Alavi AH, Mollahasani A, Gandomi AH, Esmaeili MA (2013) Formulation of soil angle of shearing resistance using a hybrid GP and OLS method. Eng Comp 29(1):37–53CrossRef
44.
Asadzadeh M, Soroush A (2009) Direct shear testing on a rockfill material. Arabian J Sci Eng 34(2):379
45.
Naderian A, Williams D (1997) Bearing capacity of open-cut coal-mine backfill materials. Trans Instit Min Metal Sect A-Min Indust 106:A30–A33
46.
Vutikuri V, Lama R, Saluja S (1974) Handbook on mechanical properties of rocks-testing techniques and results. Clausthal, Germany, p 1
47.
Da Sun, Sheng D, Xu Y (2007) Collapse behaviour of unsaturated compacted soil with different initial densities. Can Geotech J 44(6):673–686CrossRef
48.
Fell R, Hunter G (2002) The deformation behaviour of rockfill
49.
Lawton EC, Fragaszy RJ, Hardcastle JH (1989) Collapse of compacted clayey sand. J Geotech Eng 115(9):1252–1267CrossRef
Metadaten
Titel
A new approach for prediction of collapse settlement of sandy gravel soils
verfasst von
Sepehr Soleimani
Pengcheng Jiao
Shabnam Rajaei
Rana Forsati
Publikationsdatum
11.04.2017
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 1/2018
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-017-0517-y

Weitere Artikel der Ausgabe 1/2018

Engineering with Computers 1/2018 Zur Ausgabe

Original Article

A parallel log barrier-based mesh warping algorithm for distributed memory machines

Original Article

Enhanced IGMM optimization algorithm based on vibration for numerical and engineering problems

Original Article

Self-adaptive conjugate method for a robust and efficient performance measure approach for reliability-based design optimization

Original Article

Regression-based models for the prediction of unconfined compressive strength of artificially structured soil

Original Article

Rayleigh–Stokes problem for a heated generalized second grade fluid with fractional derivatives: a stable scheme based on spectral meshless radial point interpolation

Original Article

Enriched FR conjugate search directions for robust and efficient structural reliability analysis

Neuer Inhalt

    Bildnachweise