Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations

Nulu Reddeppa; Bommireddy Jayarami Reddy; Hanchate Sudarsana Rao

doi:doi:10.11648/j.ajce.20210906.13

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Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations

Nulu Reddeppa, Bommireddy Jayarami Reddy, Hanchate Sudarsana Rao

Published in American Journal of Civil Engineering (Volume 9, Issue 6)

Received: 18 October 2021 Accepted: 8 November 2021 Published: 17 November 2021

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Abstract

Optimal structural design involves dealing with three main factors visibly cross-sectional properties of the members, topology and configuration and meeting the intended functional requirements. Most of the traditional optimization techniques are based on the mathematical programming techniques, which assume that the variables are continuous, but whereas the process of structural design is generally characterized by finite often large numbers of variables of discrete in nature. Genetic Algorithm is the technique which can be used efficiently for the design optimization of the structure with discrete variables. From the study on previous work done on GA’s application in civil engineering, it has been noticed that application of GA’s is not attempted in rotating machine foundations where there is scope for determining suitable optimum shape and member sizes to achieve a well-tuned foundation. Dynamic design of machine foundation involves broad criterion such as foundation natural frequency shall be away from the machine operating frequency and foundation displacement amplitudes shall be well within the specified allowable limits. The above criterion largely depends on design factors such as size of members, shape of the foundations, concrete grade and soil characters. Presently obtaining a best suitable solution meeting the frequency and amplitude criteria by varying above four design factors involves many manual trails. This involves lot of computer and human efforts to try various combinations to arrive at the solution. Considerable resources and time need to be spent on arriving a suitable solution. Yet the solution so arrived may not be an optimum solution. In this work, Genetic algorithms is applied for optimization of solution time and foundation volume for industrial medium and heavy rotating equipment foundations. Optimum solution is obtained with above variables by setting frequency as target criteria. The optimum solution obtained from Genetic Algorithms is further verified for its compliance to its intended functional parameters by means of finite element model study.

Published in	American Journal of Civil Engineering (Volume 9, Issue 6)
DOI	10.11648/j.ajce.20210906.13
Page(s)	194-212
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Genetic Algorithm, Mill Foundation, Turbine Generator Foundation, Induced Draft Fan Foundation, Shape Optimization

References

[1]	Jenkins, W. M., (1991) “Towards Structural Optimization via the Genetic Algorithms”, Computers and Structures, Vol. 40, No. 5, pp 1321-1327.
[2]	Gold berg, D. E., (1989), “Genetic Algorithm in Search Optimization and Machine Learning”, Addition – Wesley Publishing Company Inc., Reading, Massachusetts.
[3]	Rajeev, S., Krishnamoorthy, C. S., (1992), “Discrete Optimization of Structures using Genetic Algorithms”. Journal of Structural Engineering, Vol. 118, No. 5. Pp 1233-1250.
[4]	Rajeev, S., Krishnamoorthy, C. S., (1992), “Genetic Algorithms Discussion by Laurence Schmid”, Journal of Structural Engineering.
[5]	Rajeev, S., Krishnamoorthy, C. S., (1993), “Structural Optimization using Genetic Algorithms”, A paper presented at Advanced Study Institute on Computational Methods for Engineering Analysis and Design, IIT, Madras.
[6]	Saxena, Ashutosh., George, Suju. M., Rambabu, P., (1993), “Effectiveness of Variations in Mutation Operator in Genetic Algorithm”, Proceedings of the International Computing Congress at Hyderabad.
[7]	Tanweer Alam, Shamimul Qamar, Amit Dixit, Mohamed Benaida (2020) “Genetic Algorithm: Reviews, Implementations and Applications” A Paper published in International Journal of Engineering Pedagogy (iJEP).
[8]	P Srinivasulu and C V Vaidyanathan, (2018) “Hand Book of Machine Foundations” Structural Engineering Research Centre, Madras.
[9]	Hongchun Liu, Chair of the ASCE Task Committee for “Turbine-Generator Foundations - State-of-Practice Review” Conference on Structures Congress, April 19–21, 2018 \| Fort Worth, Texas.
[10]	Reddeppa Nulu, Jayarami Reddy Bommireddy, Hanchate Sudarsana Rao. (2020) “Foundation for Primary Air Fan of speed 1490 rpm - Study of dynamic behavior” SCIREA Journal of Civil Engineering and Building Construction.
[11]	Reddeppa Nulu, Jayarami Reddy Bommireddy, Hanchate Sudarsana Rao. (2020) “ID Fan Foundation: Study of Dynamic Behavior” A paper presented at Springer Journal of Inst. Eng. India Ser. A.
[12]	Anonymous, IS: 2974 (Part-3) – 1992, “Design and Construction of Machine Foundations – Foundations For Rotary Type Machines (Medium and High Frequency)”, Bureau of Indian Standards.
[13]	Anonymous, IS: 2974 (Part-4) – 2000, “Design and Construction of Machine Foundations – Foundations For Rotary Type Machines (Low Frequency)”, Bureau of Indian Standards.
[14]	Suresh Arya, Michel O' Neil, Gorge Pincus (1981) “Design of structures and Foundation for Vibrating Machines”.
[15]	Anonymous, ISO 10816-3 “Mechanical vibration — Evaluation of machine vibration by measurements on non-rotating parts – Part-3”.

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APA Style

Nulu Reddeppa, Bommireddy Jayarami Reddy, Hanchate Sudarsana Rao. (2021). Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations. American Journal of Civil Engineering, 9(6), 194-212. https://doi.org/10.11648/j.ajce.20210906.13

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ACS Style

Nulu Reddeppa; Bommireddy Jayarami Reddy; Hanchate Sudarsana Rao. Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations. Am. J. Civ. Eng. 2021, 9(6), 194-212. doi: 10.11648/j.ajce.20210906.13

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AMA Style

Nulu Reddeppa, Bommireddy Jayarami Reddy, Hanchate Sudarsana Rao. Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations. Am J Civ Eng. 2021;9(6):194-212. doi: 10.11648/j.ajce.20210906.13

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@article{10.11648/j.ajce.20210906.13,
  author = {Nulu Reddeppa and Bommireddy Jayarami Reddy and Hanchate Sudarsana Rao},
  title = {Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations},
  journal = {American Journal of Civil Engineering},
  volume = {9},
  number = {6},
  pages = {194-212},
  doi = {10.11648/j.ajce.20210906.13},
  url = {https://doi.org/10.11648/j.ajce.20210906.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20210906.13},
  abstract = {Optimal structural design involves dealing with three main factors visibly cross-sectional properties of the members, topology and configuration and meeting the intended functional requirements. Most of the traditional optimization techniques are based on the mathematical programming techniques, which assume that the variables are continuous, but whereas the process of structural design is generally characterized by finite often large numbers of variables of discrete in nature. Genetic Algorithm is the technique which can be used efficiently for the design optimization of the structure with discrete variables. From the study on previous work done on GA’s application in civil engineering, it has been noticed that application of GA’s is not attempted in rotating machine foundations where there is scope for determining suitable optimum shape and member sizes to achieve a well-tuned foundation. Dynamic design of machine foundation involves broad criterion such as foundation natural frequency shall be away from the machine operating frequency and foundation displacement amplitudes shall be well within the specified allowable limits. The above criterion largely depends on design factors such as size of members, shape of the foundations, concrete grade and soil characters. Presently obtaining a best suitable solution meeting the frequency and amplitude criteria by varying above four design factors involves many manual trails. This involves lot of computer and human efforts to try various combinations to arrive at the solution. Considerable resources and time need to be spent on arriving a suitable solution. Yet the solution so arrived may not be an optimum solution. In this work, Genetic algorithms is applied for optimization of solution time and foundation volume for industrial medium and heavy rotating equipment foundations. Optimum solution is obtained with above variables by setting frequency as target criteria. The optimum solution obtained from Genetic Algorithms is further verified for its compliance to its intended functional parameters by means of finite element model study.},
 year = {2021}
}

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TY  - JOUR
T1  - Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations
AU  - Nulu Reddeppa
AU  - Bommireddy Jayarami Reddy
AU  - Hanchate Sudarsana Rao
Y1  - 2021/11/17
PY  - 2021
N1  - https://doi.org/10.11648/j.ajce.20210906.13
DO  - 10.11648/j.ajce.20210906.13
T2  - American Journal of Civil Engineering
JF  - American Journal of Civil Engineering
JO  - American Journal of Civil Engineering
SP  - 194
EP  - 212
PB  - Science Publishing Group
SN  - 2330-8737
UR  - https://doi.org/10.11648/j.ajce.20210906.13
AB  - Optimal structural design involves dealing with three main factors visibly cross-sectional properties of the members, topology and configuration and meeting the intended functional requirements. Most of the traditional optimization techniques are based on the mathematical programming techniques, which assume that the variables are continuous, but whereas the process of structural design is generally characterized by finite often large numbers of variables of discrete in nature. Genetic Algorithm is the technique which can be used efficiently for the design optimization of the structure with discrete variables. From the study on previous work done on GA’s application in civil engineering, it has been noticed that application of GA’s is not attempted in rotating machine foundations where there is scope for determining suitable optimum shape and member sizes to achieve a well-tuned foundation. Dynamic design of machine foundation involves broad criterion such as foundation natural frequency shall be away from the machine operating frequency and foundation displacement amplitudes shall be well within the specified allowable limits. The above criterion largely depends on design factors such as size of members, shape of the foundations, concrete grade and soil characters. Presently obtaining a best suitable solution meeting the frequency and amplitude criteria by varying above four design factors involves many manual trails. This involves lot of computer and human efforts to try various combinations to arrive at the solution. Considerable resources and time need to be spent on arriving a suitable solution. Yet the solution so arrived may not be an optimum solution. In this work, Genetic algorithms is applied for optimization of solution time and foundation volume for industrial medium and heavy rotating equipment foundations. Optimum solution is obtained with above variables by setting frequency as target criteria. The optimum solution obtained from Genetic Algorithms is further verified for its compliance to its intended functional parameters by means of finite element model study.
VL  - 9
IS  - 6
ER  -

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Author Information

Nulu Reddeppa

Department of Civil Engineering, Jawaharlal Nehru Technological University, Anantapuramu, India
Bommireddy Jayarami Reddy

Directorate of IIIT, Rajiv Gandhi University of Knowledge Technologies, Ongole, India
Hanchate Sudarsana Rao

Department of Civil Engineering, Jawaharlal Nehru Technological University, Anantapuramu, India

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APA Style

Nulu Reddeppa, Bommireddy Jayarami Reddy, Hanchate Sudarsana Rao. (2021). Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations. American Journal of Civil Engineering, 9(6), 194-212. https://doi.org/10.11648/j.ajce.20210906.13

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ACS Style

Nulu Reddeppa; Bommireddy Jayarami Reddy; Hanchate Sudarsana Rao. Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations. Am. J. Civ. Eng. 2021, 9(6), 194-212. doi: 10.11648/j.ajce.20210906.13

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AMA Style

Nulu Reddeppa, Bommireddy Jayarami Reddy, Hanchate Sudarsana Rao. Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations. Am J Civ Eng. 2021;9(6):194-212. doi: 10.11648/j.ajce.20210906.13

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@article{10.11648/j.ajce.20210906.13,
  author = {Nulu Reddeppa and Bommireddy Jayarami Reddy and Hanchate Sudarsana Rao},
  title = {Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations},
  journal = {American Journal of Civil Engineering},
  volume = {9},
  number = {6},
  pages = {194-212},
  doi = {10.11648/j.ajce.20210906.13},
  url = {https://doi.org/10.11648/j.ajce.20210906.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20210906.13},
  abstract = {Optimal structural design involves dealing with three main factors visibly cross-sectional properties of the members, topology and configuration and meeting the intended functional requirements. Most of the traditional optimization techniques are based on the mathematical programming techniques, which assume that the variables are continuous, but whereas the process of structural design is generally characterized by finite often large numbers of variables of discrete in nature. Genetic Algorithm is the technique which can be used efficiently for the design optimization of the structure with discrete variables. From the study on previous work done on GA’s application in civil engineering, it has been noticed that application of GA’s is not attempted in rotating machine foundations where there is scope for determining suitable optimum shape and member sizes to achieve a well-tuned foundation. Dynamic design of machine foundation involves broad criterion such as foundation natural frequency shall be away from the machine operating frequency and foundation displacement amplitudes shall be well within the specified allowable limits. The above criterion largely depends on design factors such as size of members, shape of the foundations, concrete grade and soil characters. Presently obtaining a best suitable solution meeting the frequency and amplitude criteria by varying above four design factors involves many manual trails. This involves lot of computer and human efforts to try various combinations to arrive at the solution. Considerable resources and time need to be spent on arriving a suitable solution. Yet the solution so arrived may not be an optimum solution. In this work, Genetic algorithms is applied for optimization of solution time and foundation volume for industrial medium and heavy rotating equipment foundations. Optimum solution is obtained with above variables by setting frequency as target criteria. The optimum solution obtained from Genetic Algorithms is further verified for its compliance to its intended functional parameters by means of finite element model study.},
 year = {2021}
}

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TY  - JOUR
T1  - Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations
AU  - Nulu Reddeppa
AU  - Bommireddy Jayarami Reddy
AU  - Hanchate Sudarsana Rao
Y1  - 2021/11/17
PY  - 2021
N1  - https://doi.org/10.11648/j.ajce.20210906.13
DO  - 10.11648/j.ajce.20210906.13
T2  - American Journal of Civil Engineering
JF  - American Journal of Civil Engineering
JO  - American Journal of Civil Engineering
SP  - 194
EP  - 212
PB  - Science Publishing Group
SN  - 2330-8737
UR  - https://doi.org/10.11648/j.ajce.20210906.13
AB  - Optimal structural design involves dealing with three main factors visibly cross-sectional properties of the members, topology and configuration and meeting the intended functional requirements. Most of the traditional optimization techniques are based on the mathematical programming techniques, which assume that the variables are continuous, but whereas the process of structural design is generally characterized by finite often large numbers of variables of discrete in nature. Genetic Algorithm is the technique which can be used efficiently for the design optimization of the structure with discrete variables. From the study on previous work done on GA’s application in civil engineering, it has been noticed that application of GA’s is not attempted in rotating machine foundations where there is scope for determining suitable optimum shape and member sizes to achieve a well-tuned foundation. Dynamic design of machine foundation involves broad criterion such as foundation natural frequency shall be away from the machine operating frequency and foundation displacement amplitudes shall be well within the specified allowable limits. The above criterion largely depends on design factors such as size of members, shape of the foundations, concrete grade and soil characters. Presently obtaining a best suitable solution meeting the frequency and amplitude criteria by varying above four design factors involves many manual trails. This involves lot of computer and human efforts to try various combinations to arrive at the solution. Considerable resources and time need to be spent on arriving a suitable solution. Yet the solution so arrived may not be an optimum solution. In this work, Genetic algorithms is applied for optimization of solution time and foundation volume for industrial medium and heavy rotating equipment foundations. Optimum solution is obtained with above variables by setting frequency as target criteria. The optimum solution obtained from Genetic Algorithms is further verified for its compliance to its intended functional parameters by means of finite element model study.
VL  - 9
IS  - 6
ER  -

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