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General Article

Frequency tunable terahertz antenna for structural health monitoring device and sustainable urban development
Rahul Gupta1*
,
Hemlata Sinha2
,
Anil Kumar Soni3
,
Madan Pal Singh4
,
Lohit Upadhyay5
,
Deepika Bairagi1
1Assistant Professor, Department of Electronics and Telecommunication, Government Engineering College, Bilaspur (C.G.), India
2Professor, Department of Electronics and Communication Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur (C.G), India
3Assistant Professor, Department of Electronics and Communication Engineering, Guru Ghasidas Vishwavidyalaya, Bilaspur (C.G.), India
4Assistant Professor, Department of Electronics and Communication Engineering, Medicaps University, Indore (M.P.), India
5Assistant Professor, Department of Civil Engineering, PIET-DS, Parul University, Vadodara, India
*Corresponding Author
31 December 2025. pp. 477-493
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Abstract

The rapid advancement of smart and sustainable building technologies has created a growing need for compact, high-performance terahertz (THz) antennas to enable reliable indoor connectivity, non- invasive health monitoring, and advanced sensing of building environments. Conventional antenna design relies heavily on iterative electromagnetic simulations and manual tuning, which are resource-intensive and time-consuming, and often fail to capture the nonlinear parameter interactions important for structural and environmental applications. This paper introduces a two-stage hybrid machine learning framework that integrates MATLAB-based simulation-driven data generation with an Autoencoder-XGBoost pipeline to optimize frequency-tunable THz antennas for building-related sensing and communication tasks. The Autoencoder extracts critical latent features, while XGBoost accurately predicts key antenna performance metrics, such as return loss (S11), resonant frequency, bandwidth, and gain, which are defined earlier in the introduction to support interdisciplinary readers. In particular, the proposed antenna design enables low-power and efficient data transmission for structural health monitoring applications, such as temperature, humidity, and moisture sensing in building materials-facilitating early damage detection and improved energy management. The proposed hybrid approach significantly outperforms conventional methods (Random Forest, SVR, MLP), achieving near-perfect predictive accuracy (MSE = 0.001, RMSE = 0.03, MAE = 0.02, R2 = 0.99), with robust generalizability confirmed through error distribution and k-fold validation. Overall, the framework offers an effective, sustainable, and scalable solution for the design and optimization of THz antennas, supporting next-generation smart building monitoring, healthy indoor environments, and sustainable urban development applications.

Keywords
terahertz antennas
smart and sustainable buildings
machine learning optimization
structural health monitoring
sustainable urban development technologies
MATLAB simulation
auto encoder
XGBoost
hybrid model
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Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 16
  • No :4
  • Pages :477-493
  • Received Date : 2025-10-28
  • Accepted Date : 2025-11-20
  • DOI :https://doi.org/10.22712/susb.20250032
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