Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Engineering with Computers
Published in: Engineering with Computers 4/2019

14-12-2018 | Original Article

An intelligent based-model role to simulate the factor of safe slope by support vector regression

Authors: Puteri Azura Sari, Meldi Suhatril, Normaniza Osman, M. A. Mu’azu, Hamzeh Dehghani, Yadollah Sedghi, Maryam Safa, Mahdi Hasanipanah, Karzan Wakil, Majid Khorami, Stefan Djuric

Published in: Engineering with Computers | Issue 4/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

An infrastructure development in landscape and clearing of more vegetated areas have provided huge changes in Malaysia gradually leading to slope instabilities accompanied by enormous environmental effects such as properties and destructions. Thus, prudent practices through vegetation incorporating to use slope stability is an option to the general stabilized technique. Few researches have investigated the effectiveness of vegetative coverings related to slope and soil parameters. The main goal of this study is to provide an intelligent soft computing model to predict the safety factor (FOS) of a slope using support vector regression (SVR). In the other words, SVR has investigated the surface eco-protection techniques for cohesive soil slopes in Guthrie Corridor Expressway stretch through the probabilistic models analysis to highlight the main parameters. The aforementioned analysis has been performed to predict the FOS of a slope, also the estimator’s function has been confirmed by the simulative outcome compared to artificial neural network and genetic programing resulting in a drastic accurate estimation by SVR. Using new analyzing methods like SVR are more purposeful than achieving a starting point by trial and error embedding multiple factors into one in ordinary low-technique software.
previous article Refined first-order reliability method using cross-entropy optimization method
next article A multivariate normal boundary intersection PCA-based approach to reduce dimensionality in optimization problems for LBM process

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
Literature
1.
2.
3.
Mohammadhassani M et al (2013) Application of the ANFIS model in deflection prediction of concrete deep beam. Struct Eng Mech 45(3):319–332CrossRef
4.
Erzin Y, Gul TO (2013) The use of neural networks for the prediction of the settlement of pad footings on cohesionless soils based on standard penetration test. Geomech Eng 5(6):541–564CrossRef
5.
Armaghani DJ et al (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9(1):48CrossRef
6.
Truong P, Van TT, Pinners E (2008) Vetiver system applications: a technical reference manual. Vetiver Netw Int 89:107
7.
Sarkar K, Singh T, Verma A (2012) A numerical simulation of landslide-prone slope in Himalayan region—a case study. Arab J Geosci 5(1):73–81CrossRef
8.
Behera PK et al (2017) Erratum to: Dump Slope stability analysis—a case study. J Geol Soc India 89(2):226–226CrossRef
9.
Bazzaz M et al (2015) Evaluating the performance of OBS-CO in steel frames under monotonic load. Earthq Struct 8(3):699–712CrossRef
10.
Bazzaz M et al (2015) Numerical comparison of the seismic performance of steel rings in off-centre bracing system and diagonal bracing system. Steel Compos Struct 19(4):917–937CrossRef
11.
Gray DH, Sotir RB (1996) Biotechnical and soil bioengineering slope stabilization: a practical guide for erosion control. Wiley, Hoboken
12.
Mohammadhassani M et al (2013) Applications of the ANFIS and LR in the prediction of strain in tie section of concrete deep beams. Comput Concr 12(3):243–259CrossRef
13.
Osman N, Barakbah S (2006) Parameters to predict slope stability—soil water and root profiles. Ecol Eng 28(1):90–95CrossRef
14.
Normaniza O, Faisal H, Barakbah S (2008) Engineering properties of Leucaena leucocephala for prevention of slope failure. Ecol Eng 32(3):215–221CrossRef
15.
Osman N, Abdullah MN, Abdullah CH (2011) Pull-out and tensile strength properties of two selected tropical trees. Sains Malays 40(6):577–585
16.
Osman N, Barakbah S (2011) The effect of plant succession on slope stability. Ecol Eng 37(2):139–147CrossRef
17.
Ali F (2010) Use of vegetation for slope protection: root mechanical properties of some tropical plants. Int J Phys Sci 5(5):496–506
18.
Zandi Y et al (2018) Computational investigation of the comparative analysis of cylindrical barns subjected to earthquake. Steel Compos Struct 28(4):439–447
19.
Safa M et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel–concrete composite beam’s shear strength. Steel Compos Struct Int J 21(3):679–688CrossRef
20.
Toghroli A et al (2018) Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J Intell Manuf 29 (8):1793–1801CrossRef
21.
Toghroli, A et al. (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct 17(5):623–639CrossRef
22.
23.
Mansouri I et al (2016) Strength prediction of rotary brace damper using MLR and MARS. Struct Eng Mech 60(3):471–488CrossRef
24.
Mohammadhassani M et al (2015) Fuzzy modelling approach for shear strength prediction of RC deep beams. Smart Struct Syst 16(3):497–519CrossRef
25.
Sadeghipour Chahnasir E et al (2018) Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. Smart Struct Syst 22(4):413–424
26.
Ornella L, Tapia E (2010) Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data. Comput Electr Agric 74(2):250–257CrossRef
27.
Jain P, Garibaldi JM, Hirst JD (2009) Supervised machine learning algorithms for protein structure classification. Comput Biol Chem 33(3):216–223CrossRef
28.
Mohammadhassani M et al (2013) Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams. Struct Eng Mech 46(6):853–868CrossRef
29.
Zhao H-b (2008) Slope reliability analysis using a support vector machine. Comput Geotech 35(3):459–467CrossRef
30.
Kang F, Li J (2015) Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. J Comput Civil Eng 30(3):04015040CrossRef
31.
Samui P (2008) Slope stability analysis: a support vector machine approach. Environ Geol 56(2):255CrossRef
32.
Kang F, Xu Q, Li J (2016) Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl Math Model 40(11–12):6105–6120MathSciNetCrossRef
33.
Ananthakrishnan S et al (2013) Batch-mode semi-supervised active learning for statistical machine translation. Comput Speech Lang 27(2):397–406CrossRef
34.
Ye Q, Zhang Z, Law R (2009) Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst Appl 36(3):6527–6535CrossRef
35.
Rajasekaran S, Gayathri S, Lee T-L (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35(16):1578–1587CrossRef
36.
Yang H et al (2009) Localized support vector regression for time series prediction. Neurocomputing 72(10):2659–2669CrossRef
37.
Mohammadhassani M et al (2014) An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups. Smart Struct Syst 14(5):785–809CrossRef
38.
Wei Z et al (2013) A dynamic particle filter-support vector regression method for reliability prediction. Reliab Eng Syst Saf 119:109–116CrossRef
39.
Zhang L et al (2013) Iterated time series prediction with multiple support vector regression models. Neurocomputing 99:411–422CrossRef
40.
Gordan B et al (2016) A new model for determining slope stability based on seismic motion performance. Soil Mech Found Eng 53(5):344–351CrossRef
41.
Mahdiyar A et al (2017) A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput 33(4):807–817CrossRef
42.
Greenwood JR (2006) SLIP4EX—a program for routine slope stability analysis to include the effects of vegetation, reinforcement and hydrological changes. Geotech Geol Eng 24(3):449–465CrossRef
43.
Ali N et al (2012) Soil—root interaction and effects on slope stability analysis. Electron J Geotech Eng 17:319–328
44.
Norris JE et al (2008) Slope stability and erosion control: ecotechnological solutions. Springer, BerlinCrossRef
45.
Norris J, Greenwood J (2003) In situ shear box and root pull-out apparatus for measuring the reinforcing effects of vegetation. Field Meas Geomech, Proceedings of the 6th international symposium, Oslo, Norway, pp 593-597
46.
Hengchaovanich D, Nilaweera NS (1996) An assessment of strength properties of vetiver grass roots in relation to slope stabilization. In: International conference on vetiver, Chain Kai
47.
Ke C et al (2003) Design principles and engineering samples of applying vetiver ecoengineering technology for steep slope and riverbank stabilization. In: Proc 3rd international conf. on vetiver, Guangzhou. China Agricultural Press, Beijing (Citeseer)
48.
Saifuddin M, Normaniza O (2012) Physiological and root profile studies of four legume tree species. Life Sci J 9(4):1509–1518
49.
Metadata
Title
An intelligent based-model role to simulate the factor of safe slope by support vector regression
Authors
Puteri Azura Sari
Meldi Suhatril
Normaniza Osman
M. A. Mu’azu
Hamzeh Dehghani
Yadollah Sedghi
Maryam Safa
Mahdi Hasanipanah
Karzan Wakil
Majid Khorami
Stefan Djuric
Publication date
14-12-2018
Publisher
Springer London
Published in
Engineering with Computers / Issue 4/2019
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-018-0677-4

Other articles of this Issue 4/2019

Engineering with Computers 4/2019 Go to the issue

Original Article

Modified symbiotic organisms search for structural optimization

Original Article

A novel scheduling with multi-criteria for high-performance computing systems: an improved genetic algorithm-based approach

Original Article

The local discontinuous Galerkin method for 2D nonlinear time-fractional advection–diffusion equations

Original Article

On modeling of wave propagation in a thermally affected GNP-reinforced imperfect nanocomposite shell

Original Article

Modification of rock mass rating system using soft computing techniques

Original Article

An improved pseudospectral meshless radial point interpolation (PSMRPI) method for 3D wave equation with variable coefficients