Abstract
With the development of construction industrialization technology, prefabricated construction technology overcomes the shortcomings of previous on-site pouring construction methods with its good economic and social benefits, and has become a research hotspot in the current construction industry. Using genetic algorithm to optimize the structure design of prefabricated concrete frame building (PCFB) can not only achieve better economic efficiency, but also ensure the safety of the structure. Based on this, this article will study the PCFB optimization based on genetic algorithm. This paper investigates the current application of prefabricated concrete frame structures and the application of component optimization based on Matlab genetic algorithm. This paper analyzes the advantages of PCFB and the obstacles to its development, and proposes a PCFB optimization scheme based on Matlab genetic algorithm. The survey data shows that with regard to the application of genetic algorithm and BIM technology in PCFB optimization, 94.94%, 86.71% and 83.23% are used for “component collision check”, “prefabricated component drawing”, and “project construction simulation” respectively. It can be seen that the genetic algorithm has considerable application effects in PCFB optimization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boscardin, J.T., Yepes, V., Kripka, M.: Optimization of reinforced concrete building frames with automated grouping of columns. Autom. Constr. 104(Aug), 331–340 (2019)
Abey, S.T., Anand, K.B.: Embodied energy comparison of prefabricated and conventional building construction. J. Inst. Eng. (India) 100(4), 777–790 (2019)
Tan, T., et al.: Barriers to Building Information Modeling (BIM) implementation in China’s prefabricated construction: an interpretive structural modeling (ISM) approach. J. Clean. Prod. 219(May 10), 949–959 (2019)
Baghdadi, A., Heristchian, M., Kloft, H.: Connections placement optimization approach toward new prefabricated building systems. Eng. Struct. 233(2), 111648 (2021)
Razavialavi, S.R., Abourizk, S.: Genetic algorithm-simulation framework for decision making in construction site layout planning. J. Constr. Eng. Manag. 143(1), 04016084.1–04016084.13 (2017)
Wang, L., Janssen, P., Ji, G.: SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design. Artif. Intell. Eng. Des. Anal. Manuf. 34(4), 458–476 (2020)
Sabharwal, S., Bansal, P., Mittal, N.: Construction of t-way covering arrays using genetic algorithm. Int. J. Syst. Assur. Eng. Manag. 8(2), 264–274 (2017)
Ngowtanasawan, G.: A causal model of BIM adoption in the Thai architectural and engineering design industry. Proc. Eng. 180, 793–803 (2017)
Potter, A., et al.: Product, process and customer preference alignment in prefabricated house building. Int. J. Prod. Econ. 183(Jan. Pt.A), 79–90 (2017)
Sardone, L., et al.: A preliminary study on a variable section beam through Algorithm-Aided Design: a way to connect architectural shape and structural optimization. Proc. Manuf. 44, 497–504 (2020)
Maljaars, E., Felici, F.: Actuator allocation for integrated control in tokamaks: architectural design and a mixed-integer programming algorithm. Fusion Eng. Des. 122(Nov), 94–112 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Geng, Z. (2022). Optimization of Prefabricated Concrete Frame Building Based on Genetic Algorithm. In: Sugumaran, V., Sreedevi, A.G., Xu , Z. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. ICMMIA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-031-05484-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-05484-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05483-9
Online ISBN: 978-3-031-05484-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)