Advertisement

Optimizing ANN models with PSO for predicting short building seismic response

  • Hoang Nguyen
  • Hossein MoayediEmail author
  • Loke Kok Foong
  • Husam Abdulrasool H. Al Najjar
  • Wan Amizah Wan Jusoh
  • Ahmad Safuan A. Rashid
  • Jamaloddin Jamali
Original Article
  • 50 Downloads

Abstract

The present study aimed to optimize the artificial neural network (ANN) with one of the well-established optimization algorithms called particle swarm optimization (PSO) for the problem of ground response approximation in short structures. Various studies showed that ANN-based solutions are a reliable method for complex engineering problems. Predicting the ground surface respond to seismic loading is one of the engineering problems that still has not received any ANN solution. Therefore, this paper aimed to assess the application of hybrid PSO-based ANN models to the calculation of horizontal deflection of columns in short building after being subjected to a significant seismic loading (e.g., The Chi-Chi earthquake used as one of the input databases). To prepare both of the training and testing datasets, for the ANN and PSO-ANN network models, a series of finite element (FE) modeling were performed. The used FEM simulation database consists of 8324 training datasets and 2081 testing datasets that is equal to 80% and 20% of the whole database, respectively. The input includes Chi-Chi earthquake dynamic time (s), friction angle (φ), dilation angle (ψ), unit weight (γ), soil elastic modulus (E), Poisson’s ratio (v), structure axial stiffness (EA), and bending stiffness (EI) where the output was taken horizontal deflection of the columns at their highest level (Ux). The result indicates higher reliability of the PSO-ANN model in estimating the ground response and horizontal deflection of structural columns in short structures after being subjected to earthquake loading.

Keywords

ANN Optimization PSO-ANN Earthquake Short building 

Notes

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

References

  1. 1.
    Moayedi H, Nazir R, Ghareh S, Sobhanmanesh A, Tan YC (2018) Performance analysis of piled-raft foundation system of varying pile lengths in controlling angular distortion. Soil Mech Found Eng 55:265–269Google Scholar
  2. 2.
    Kthatir A, Tehami M, Khatir S, Wahab MA (2018) Republished Paper. Multiple damage detection and localization in beam-like and complex structures using co-ordinate modal assurance criterion combined with firefly and genetic algorithms (Reprinted from Jounral of Vibroengineering 18:5063–5073 2016). J VibroEng 20:832–842Google Scholar
  3. 3.
    Dieu Tien B, Viet-Ha N, Nhat-Duc H (2018) Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and multi-layer perceptron neural network. Adv Eng Inform 38:593–604Google Scholar
  4. 4.
    Yaseen ZM, Karami H, Ehteram M, Mohd NS, Mousavi SF, Hin LS, Kisi O, Farzin S, Kim S, El-Shafie A (2018) Optimization of reservoir operation using new hybrid algorithm. KSCE J Civil Eng 22:4668–4680Google Scholar
  5. 5.
    Qin S, Zhou Y-L, Cao H, Wahab MA (2018) Model updating in complex bridge structures using kriging model ensemble with genetic algorithm. KSCE J Civil Eng 22:3567–3578Google Scholar
  6. 6.
    Jonbi J, Arini RN, Anwar B, Fulazzaky MA (2018) Effect of added the polycarboxylate ether on slump retention and compressive strength of the high-performance concrete. In: Hajek P, Han AL, Kristiawan S, Chan WT, Ismail MB, Gan BS, Sriravindrarajah R, Hidayat BA (eds) 4th international conference on rehabilitation and maintenance in civil engineeringGoogle Scholar
  7. 7.
    Nguyen H, Bui X-N (2018) Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Nat Resour Res 29:1–15Google Scholar
  8. 8.
    Nguyen H, Bui X-N, Bui H-B, Mai N-L (2018) A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine. Vietnam. Neural Comput Appl 31:1–17Google Scholar
  9. 9.
    Bui X-N, Nguyen H, Le H-A, Bui H-B, Do N-H (2019) Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. Nat Resour Res 29:1–21Google Scholar
  10. 10.
    Nguyen H, Bui X-N, Tran Q-H, Le T-Q, Do N-H (2019) Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam. SN Appl Sci, 1:125Google Scholar
  11. 11.
    Nguyen H, Bui X-N, Tran Q-H, Mai N-L (2019) A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Appl Soft Comput 77:376–386Google Scholar
  12. 12.
    Pham BT, Nguyen MD, Bui KTT, Prakash I, Chapi K, Bui DT (2019) A novel artificial intelligence approach based on multi-layer perceptron neural network and biogeography-based optimization for predicting coefficient of consolidation of soil. Catena 173:302–311Google Scholar
  13. 13.
    Nhat-Duc H, Quoc-Lam N, Dieu Tien B (2018) Image processing-based classification of asphalt pavement cracks using support vector machine optimized by artificial bee colony. J. Comput. Civ. Eng 32(5):04018037Google Scholar
  14. 14.
    Nguyen-Thoi T, Tran-Viet A, Nguyen-Minh N, Vo-Duy T, Ho-Huu V (2018) A combination of damage locating vector method (DLV) and differential evolution algorithm (DE) for structural damage assessment. Front Struct Civil Eng 12:92–108Google Scholar
  15. 15.
    Lacy SJ, Prevost JH (1987) Nonlinear seismic response analysis of earth dams. Soil Dyn Earthq Eng 6:48–63Google Scholar
  16. 16.
    Mosallanezhad M, Moayedi H (2017) Comparison analysis of bearing capacity approaches for the strip footing on layered soils. Arab J Sci Eng 42(9):3711–3722Google Scholar
  17. 17.
    Hajikhodaverdikhana P, Nazari M, Mohsenizadeh M, Shamshirband S, Chau K-W (2018) Earthquake prediction with meteorological data by particle filter-based support vector regression. Eng Appl Comput Fluid Mech 12:679–688Google Scholar
  18. 18.
    Men F (2002) Investigation of earthquake mechanisms and their impact on certain basic concepts in earthquake engineering and seismology. Earthq Eng Eng Vib 1:281–291Google Scholar
  19. 19.
    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 YorkGoogle Scholar
  20. 20.
    Gulkan P, Yazgan U (2005) Raised drift demands for framed buildings during near-field earthquakes. In: Gulkan P, Anderson JG (eds) Directions in strong motion instrumentation. Springer, Dordrecht, pp 61–81Google Scholar
  21. 21.
    Qu HL, Li RF, Hu HG, Jia HY, Zhang JJ (2016) An approach of seismic design for sheet pile retaining wall based on capacity spectrum method. Geomech Eng 11:309–323Google Scholar
  22. 22.
    Thomas S, Pillai GN, Pal K, Jagtap P (2016) Prediction of ground motion parameters using randomized ANFIS (RANFIS). Appl Soft Comput 40:624–634Google Scholar
  23. 23.
    Funck T, Dickmann T, Rihm R, Krastel S, LykkeAndersen H, Schmincke HU (1996) Reflection seismic investigations in the volcaniclastic apron of Gran Canaria and implications for its volcanic evolution. Geophys J Int 125:519–536Google Scholar
  24. 24.
    Pijush S (2010) Support vector machine for evaluating seismic liquefaction potential using standard penetration test. Disaster Adv 3:20–25Google Scholar
  25. 25.
    Latifi N, Vahedifard F, Ghazanfari E, Horpibulsuk S, Marto A, Williams J (2017) Sustainable improvement of clays using low-carbon nontraditional additive. Int J Geomech 18:04017162Google Scholar
  26. 26.
    Uncuoglu E (2015) The bearing capacity of square footings on a sand layer overlying clay. Geomech Eng 9:287–311Google Scholar
  27. 27.
    Ahmadi MM, Kouchaki BM (2016) New and simple equations for ultimate bearing capacity of strip footings on two-layered clays: numerical study. Int J Geomech 16:11Google Scholar
  28. 28.
    Gao W, Guirao JLG, Abdel-Aty M, Xi W (2019) An independent set degree condition for fractional critical deleted graphs. Discret Contin Dyn Syst-S 12:877–886Google Scholar
  29. 29.
    Gao W, Wu H, Siddiqui MK, Baig AQ (2018) Study of biological networks using graph theory. Saudi J Biol Sci 25:1212–1219Google Scholar
  30. 30.
    Gao W, Wang W, Dimitrov D, Wang Y (2018) Nano properties analysis via fourth multiplicative ABC indicator calculating. Arab J Chem 11:793–801Google Scholar
  31. 31.
    Nguyen H, Bui X-N, Bui H-B, Cuong DT (2019) Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophys 67:1–14Google Scholar
  32. 32.
    Muthusamy S, Manickam LP, Murugesan V, Muthukumaran C, Pugazhendhi A (2019) Pectin extraction from Helianthus annuus (sunflower) heads using RSM and ANN modelling by a genetic algorithm approach. Int J Biol Macromol 124:750–758Google Scholar
  33. 33.
    Safaei MR, Karimipour A, Abdollahi A, Truong Khang N (2018) The investigation of thermal radiation and free convection heat transfer mechanisms of nanofluid inside a shallow cavity by lattice Boltzmann method. Phys A-Stat Mech Appl 509:515–535Google Scholar
  34. 34.
    Karimipour A, D’Orazio A, Goodarzi M (2018) Develop the lattice Boltzmann method to simulate the slip velocity and temperature domain of buoyancy forces of FMWCNT nanoparticles in water through a micro flow imposed to the specified heat flux. Phys A-Stat Mech Appl 509:729–745Google Scholar
  35. 35.
    Goodarzi M, D’Orazio A, Keshavarzi A, Mousavi S, Karimipour A (2018) Develop the nano scale method of lattice Boltzmann to predict the fluid flow and heat transfer of air in the inclined lid driven cavity with a large heat source inside, two case studies: pure natural convection & mixed convection. Phys A-Stat Mech Appl 509:210–233Google Scholar
  36. 36.
    Alrashed AAAA, Karimipour A, Bagherzadeh SA, Safaei MR, Afrand M (2018) Electro- and thermophysical properties of water-based nanofluids containing copper ferrite nanoparticles coated with silica: experimental data, modeling through enhanced ANN and curve fitting. Int J Heat Mass Transf 127:925–935Google Scholar
  37. 37.
    Asadizadeh M, Hossaini MF (2016) Predicting rock mass deformation modulus by artificial intelligence approach based on dilatometer tests. Arab J Geosci 9:15Google Scholar
  38. 38.
    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 7:2303–2314Google Scholar
  39. 39.
    Gao W, He TY (2017) Displacement prediction in geotechnical engineering based on evolutionary neural network. Geomech Eng 13:845–860Google Scholar
  40. 40.
    Nazir R, Moayedi H, Subramaniam P, Gue S-S (2017) Application and design of transition piled embankment with surcharged prefabricated vertical drain intersection.over soft ground. Arab J Sci Eng 43(4):1573–1582Google Scholar
  41. 41.
    Nazir R, Moayedi H, Subramaniam P, Ghareh S (2017) Ground improvement using SPVD and RPE. Arab J Geosci 10:515Google Scholar
  42. 42.
    Moayedi H, Nazir R (2017) Malaysian experiences of peat stabilization, State of the Art. Geotech Geol Eng 36(1):1–11Google Scholar
  43. 43.
    Moayedi H, Mosallanezhad M, Nazir R (2017) Evaluation of maintained load test (MLT) and pile driving analyzer (PDA) in measuring bearing capacity of driven reinforced concrete piles. Soil Mech Found Eng 54:150–154Google Scholar
  44. 44.
    Moayedi H, Mosallanezhad M (2017) Uplift resistance of belled and multi-belled piles in loose sand. Measurement 109:346–353Google Scholar
  45. 45.
    Moayedi H, Mosallanezhad M (2017) Physico-chemical and shrinkage properties of highly organic soil treated with non-traditional additives. Geotech Geol Eng 35:1–11Google Scholar
  46. 46.
    Nazir R, Moayedi H, Noor RBM, Ghareh S (2016) Development of new attenuation equation for subduction mechanisms in Malaysia water. Arab J Geosci 9:741Google Scholar
  47. 47.
    Nazir R, Ghareh S, Mosallanezhad M, Moayedi H (2016) The influence of rainfall intensity on soil loss mass from cellular confined slopes. Measurement 81:13–25Google Scholar
  48. 48.
    Nazir R, Moayedi H, Mosallanezhad M, Tourtiz A (2015) Appraisal of reliable skin friction variation in a bored pile. Proc Inst Civil Eng-Geotech Eng 168:75–86Google Scholar
  49. 49.
    Moayedi H, Nazir R, Mosallanezhad M (2015) Determination of reliable stress and strain distributions along bored piles. Soil Mech Found Eng 51:285–291Google Scholar
  50. 50.
    Kassim KA, Rashid ASA, Kueh ABH, Yah CS, Siang LC, Noor NM, Moayedi H (2015) Development of rapid consolidation equipment for cohesive soil. Geotech Geol Eng 33:167–174Google Scholar
  51. 51.
    Nazir R, Moayedi H, Pratikso A, Mosallanezhad M (2014) The uplift load capacity of an enlarged base pier embedded in dry sand. Arab J Geosci 1–12Google Scholar
  52. 52.
    Moayedi H, Nazir R, Kazemian S, Huat BK (2014) Microstructure analysis of electrokinetically stabilized peat. Measurement 48:187–194Google Scholar
  53. 53.
    Moayedi H, Nazir R, Kassim KA, Huat BK (2014) Measurement of the electrokinetic properties of peats treated with chemical solutions. Measurement 49:289–295Google Scholar
  54. 54.
    Moayedi H, Mosallanezhad M, Nazir R, Kazemian S, Huat BK (2014) Peaty soil improvement by using cationic reagent grout and electrokintic method. Geotech Geol Eng 32:933–947Google Scholar
  55. 55.
    Chang MH, Kuo CP, Shau SH, Hsu RE (2011) Comparison of SPT-N-based analysis methods in evaluation of liquefaction potential during the 1999 Chi-chi earthquake in Taiwan. Comput Geotech 38:393–406Google Scholar
  56. 56.
    Gao W, Guirao JLG, Basavanagoud B, Wu J (2018) Partial multi-dividing ontology learning algorithm. Inf Sci 467:35–58MathSciNetGoogle Scholar
  57. 57.
    Gao W, Dimitrov D, Abdo H (2018) Tight independent set neighborhood union condition for fractional critical deleted graphs and ID deleted graphs. Discret Contin Dyn Syst 12(4&5):711–721Google Scholar
  58. 58.
    Asadi A, Moayedi H, Huat BBK, Parsaie A, Taha MR (2011) Artificial neural networks approach for electrochemical resistivity of highly organic soil. Int J Electrochem Sci 6:1135–1145Google Scholar
  59. 59.
    Asadi A, Moayedi H, Huat BBK, Boroujeni FZ, Parsaie A, Sojoudi S (2011) Prediction of zeta potential for tropical peat in the presence of different cations using artificial neural networks. Int J Electrochem Sci 6:1146–1158Google Scholar
  60. 60.
    Moayedi H, Raftari M, Sharifi A, Jusoh WAW, Rashid ASA (2019) Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Eng Comput 36:1–12Google Scholar
  61. 61.
    Alnaqi AA, Moayedi H, Shahsavar A, Nguyen TK (2019) Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Convers Manag 183:137–148Google Scholar
  62. 62.
    Moayedi H, Mosallanezhad M, Mehrabi M, Safuan ARA, Biswajeet P (2018) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:1–18Google Scholar
  63. 63.
    Moayedi H, Hayati S (2018) Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput Appl 31:1–17Google Scholar
  64. 64.
    Moayedi H, Hayati S (2018) Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl Soft Comput 66:208–219Google Scholar
  65. 65.
    Mosallanezhad M, Moayedi H (2017) Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci 10:10Google Scholar
  66. 66.
    Moayedi H, Armaghani DJ (2017) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34(2):347–356Google Scholar
  67. 67.
    Moayedi H, Hayati S (2018) Applicability of a CPT-based neural network solution in predicting load-settlement responses of bored pile. Int. J. Geomech 18(6):06018009Google Scholar
  68. 68.
    Moayedi H, Mosallanezhad M, Mehrabi M, Safuan ARA, Biswajeet P (2018) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:1–18Google Scholar
  69. 69.
    Moayedi H, Rezaei A (2017) An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Comput Appl 31(2):327–336Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Hoang Nguyen
    • 1
  • Hossein Moayedi
    • 2
    • 3
    Email author
  • Loke Kok Foong
    • 4
  • Husam Abdulrasool H. Al Najjar
    • 5
  • Wan Amizah Wan Jusoh
    • 6
  • Ahmad Safuan A. Rashid
    • 4
  • Jamaloddin Jamali
    • 7
  1. 1.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  2. 2.Department for Management of Science and Technology DevelopmentTon Duc Thang UniversityHo Chi Minh CityVietnam
  3. 3.Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  4. 4.Center of Tropical Geoengineering, School of Civil Engineering, Faculty of EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia
  5. 5.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and ITUniversity of Technology SydneySydneyAustralia
  6. 6.Faculty of Engineering Technology (FTK)Universiti Tun Hussein Onn Malaysia, Campus (Pagoh Branch), Higher Education Hub Pagoh, KM1, Jalan PanchorPagoh, Muar, JohorMalaysia
  7. 7.College of Engineering and TechnologyAmerican University of the Middle EastEgailaKuwait

Personalised recommendations