Structural Optimization of Concrete Volume for Machine Foundation Using Genetic Algorithms

Document Type : Regular Article

Authors

1 D.Sc. Student, Mechanical Engineering Postgraduate Program (PPGEM), State University of Rio de Janeiro (UERJ), Rio de Janeiro/RJ, Brazil

2 Professor, Mechanical Engineering Postgraduate Program (PPGEM), State University of Rio de Janeiro (UERJ), Rio de Janeiro/RJ, Brazil

3 Professor, Civil Engineering Postgraduate Program (PGECIV), State University of Rio de Janeiro (UERJ), Rio de Janeiro/RJ, Brazil

Abstract

This research work aims to optimize a concrete foundation designed to support a high-capacity motor-driven compressor. The structure has plane dimensions of approximately 15 m × 11 m and a height of 1.5 m. The concrete block is to be supported by 20 concrete piles approximately 8.5 m in length and 0.5 m in diameter. The investigated structural system is subjected to deterministic dynamic loadings due to the nature of the equipment supported by the concrete foundation. The main objective of the optimization is to reduce the structural volume through the analysis of its dynamic response, in order to minimize the cost of the concrete volume. In this research work, Genetic Algorithms (GAs) are used through an appropriate interface between ANSYS and MATLAB software. The results of this study show that through the GAs it is possible to achieve a considerable volume reduction with respect to the original volume of concrete used in the design of the foundations structural system.

Highlights

Google Scholar

Keywords

Main Subjects

References

[1]     Lopes MA. Structural optimization of a concrete foundation subjected to dynamic loadings from a high capacity motor-driven compressor. Master’s dissertation (Mechanical Engineering Master’s Degree) – Mechanical Engineering Postgraduate Program (PPGEM), State Univer. 2017.
[2]     Lopes MA, Soeiro FJCP, Burgos RB. Structural optimization of a concrete foundation subjected to dynamic loadings from a motor-compressor group. XXXVIII Iber Lat Am Congr Comput Methods Eng (CILAMCE), Florianopolis/SC, Brazil 2017.
[3]     Lopes DMC. Numerical analysis and study of dynamic response of structural systems for machinery foundations. 1f. Master’s dissertation (Civil Engineering Master’s Degree) - Civil Engineering Postgraduate Program (PGECIV), State University of Rio de Janeiro (UERJ), R. 2016.
[4]     Holland J. An introduction with application to biology, control and artificial intelligence Adaptation in Natural and Artificial System 1975.
[5]     Adeli H, Cheng N. Augmented Lagrangian Genetic Algorithm for Structural Optimization. J Aerosp Eng 1994;7:104–18. doi:10.1061/(ASCE)0893-1321(1994)7:1(104).
[6]     Tan LP, Lotfi A, Lai E, Hull JB. Soft computing applications in dynamic model identification of polymer extrusion process. Appl Soft Comput 2004;4:345–55. doi:10.1016/j.asoc.2003.10.004.
[7]     Grierson DE, Hajela P. Emergent computing methods in engineering design: applications of genetic algorithms and neural networks. vol. 149. Springer Science & Business Media; 1996.
[8]     Prendes Gero MB, Bello García A, del Coz Díaz JJ. A modified elitist genetic algorithm applied to the design optimization of complex steel structures. J Constr Steel Res 2005;61:265–80. doi:10.1016/j.jcsr.2004.07.007.
[9]     Tayşi N, Göğüş MT, Özakça M. Optimization of arches using genetic algorithm. Comput Optim Appl 2008;41:377–94. doi:10.1007/s10589-007-9111-3.
[10]    Vasudev KL, Sharma R, Bhattacharyya SK. Multi-objective shape optimization of submarine hull using genetic algorithm integrated with computational fluid dynamics. Proc Inst Mech Eng Part M J Eng Marit Environ 2019;233:55–66. doi:10.1177/1475090217714649.
[11]    Rajeev S, Krishnamoorthy CS. Discrete Optimization of Structures Using Genetic Algorithms. J Struct Eng 1992;118:1233–50. doi:10.1061/(ASCE)0733-9445(1992)118:5(1233).
[12]    Hajela P, Lee E. Genetic algorithms in truss topological optimization. Int J Solids Struct 1995;32:3341–57. doi:10.1016/0020-7683(94)00306-H.
[13]    Camp C, Pezeshk S, Cao G. Optimized Design of Two-Dimensional Structures Using a Genetic Algorithm. J Struct Eng 1998;124:551–9. doi:10.1061/(ASCE)0733-9445(1998)124:5(551).
[14]    Hosseini G. Capacity Prediction of RC Beams Strengthened with FRP by Artificial Neural Networks Based on Genetic Algorithm. J Soft Comput Civ Eng 2017;1:93–8.
[15]    Olivier LT, Jimoh AA, Adedeji AA. Optimisation of Recycled Thermoplastic Plate (Tile). J Soft Comput Civ Eng 2017;1:19–34.
[16]    Karanjule DB, Bhamare SS, Rao TH. Process Parameter Optimization for Minimizing Springback in Cold Drawing Process of Seamless Tubes using Advanced Optimization Algorithms. J Soft Comput Civ Eng 2018;2:72–90.
[17]    American Concrete Institute. “ACI 351 – Foundations for Dynamic Equipment.” Farmington Hills, Michigan, United States of America, 2004.
[18]    Deutsches Institut Für Normung. “DIN 4024-1 – Machine Foundations – Rigid Foundations for Machinery Subjected to Periodic Vibration”, Berlin, German, 1998.
[19]    Deutsches Institut Für Normung. “DIN 4024-2 – Machine Foundations – Rigid Foundations for Machinery Subjected to Periodic Vibration.” Berlin, German, 1991.
[20]    International Standard Organization. “ISO 2372: Mechanical Vibration of Machines with Operating Speeds from 10 to 200 rev/s.” Basis for Specifying Evaluation Standards., n.d.
[21]    International Standard Organization. ISO 2631-1: “Mechanical Vibration and Shock - Evaluation of Human Exposure to Whole-Body Vibration – Part 1: General Requirements.,” 1997.
[22]    International Standard Organization. ISO 2631-2: “Evaluation of Human Exposure to Whole-Body Vibration – Part 2: Human Exposure to Continuous and Shock-Induced Vibrations in Buildings (1 to 80Hz).,” 2001.
[23]    Norma Regulamentadora. NR 15. Attachment No 8: Vibrations – Amended by Ordinance SSMT N. o 12, Ministry of Labour, Brazil., 1983.
[24]    Petrobras – Petróleo Brasileiro S.A. – “N-1848 – REV. C – Design of Machine Foundation” Rio de Janeiro, Brazil., 2011.

Files

History

  • Receive Date: 27 September 2019
  • Revise Date: 24 November 2019
  • Accept Date: 26 November 2019

Share

How to cite

Statistics