Design of Intelligent Construction System for Assembly Building Based on Improved IoT

被引:0
作者
Wang J. [1 ]
机构
[1] College of Civil Engineering, Zhengzhou University of Technology, Zhengzhou
来源
Informatica (Slovenia) | 2024年 / 48卷 / 10期
关键词
assembly building; genetic simulated annealing algorithm; IoT; smart construction; synergy;
D O I
10.31449/inf.v48i10.5889
中图分类号
学科分类号
摘要
Assembled construction can serve the purpose of saving resources, reducing costs and minimizing the impact on the environment. The construction process is essentially impossible without the support of advanced information technology. The study analyzes the functional requirements of an intelligent construction system for assembly buildings in four stages: design, production, transportation and installation based on the Internet of Things technology in order to realize the collaborative efforts of the stakeholders of assembly buildings. Additionally, by utilizing social network analysis and synergy theory, the study develops a dual-objective optimization model that thoroughly takes into account the synergistic influence between assembly construction services and service quality. The findings indicated that the system combines both efficiency and safety, and the genetic simulated annealing algorithm used to improve the Internet of Things assembly intelligent construction system has a service quality value of 4.52, a synergy value of 5.26, and an objective function value of 4.81, which are greater than that of the genetic algorithm and the simulated annealing algorithm. This intelligent construction system serves as a model for the development of intelligent construction systems for assembly buildings. It enables the realization of process data sharing, enabling data-driven business decision-making, increased productivity, and decreased management costs. © 2024 Slovene Society Informatika. All rights reserved.
引用
收藏
页码:103 / 118
页数:15
相关论文
共 25 条
[1]  
Xu Z., Hu G., Jia M., Dong L., Potential transmission choice for Internet of Things (IoT): Wireless and batteryless communications and open problems, China Communications, 18, 2, pp. 241-249, (2021)
[2]  
Usman A. M., Abdullah M. K., An assessment of building energy consumption characteristics using analytical energy and carbon footprint assessment model, Green and Low-Carbon Economy, 1, 1, pp. 28-40, (2023)
[3]  
Liu X. M., Singh T. P., Gupta R. K., Onyema E. M., Chaotic Association Feature Extraction of Big Data Clustering Based on the Internet of Things, Informatica, 46, 3, pp. 333-342, (2022)
[4]  
Liu K., Sun Y., Yang D., The administrative center or economic center: Which dominates the regional green development pattern. A case study of Shandong peninsula urban agglomeration, China, Green and Low-Carbon Economy, 1, 3, pp. 110-120, (2023)
[5]  
Bhor H. N., Kalla M., TRUST-based features for detecting the intruders in the Internet of Things network using deep learning, Computational Intelligence, 38, 2, pp. 438-462, (2022)
[6]  
Yan X., Zhang H., Zhang W., Intelligent monitoring and evaluation for the prefabricated construction schedule, Computer-aided Civil and Infrastructure Engineering, 38, 3, pp. 391-407, (2023)
[7]  
Ouyang P., Ouyang X., Peng Y. Z., Pan A. Y., Information visualization method for intelligent construction of prefabricated buildings based on P-ISOMAP algorithm, International Journal of Emerging Electric Power Systems, 24, 1, pp. 73-89, (2022)
[8]  
Li Z., Huang Y., Antiseismic method of prestressed fabricated building structure under intelligent big data, Mathematical Problems in Engineering, 21, 46, pp. 770-782, (2021)
[9]  
Ding H., Li M., Zhong R. Y., Huang G. Q., Multistage self-adaptive decision-making mechanism for prefabricated building modules with IoT-enabled graduation manufacturing system, Automation in Construction, 148, 4, pp. 1047551-10475518, (2023)
[10]  
Wang X., Du Q., Lu C., Li J., Exploration in carbon emission reduction effect of low-carbon practices in prefabricated building supply chain, Journal of Cleaner Production, 368, 25, pp. 1-13, (2022)