Artificial Intelligence for Smart Infrastructure System: Integrating Civil and Electrical Engineering

Authors

  • Mohib ur Rahman (Corresponding Author) Iqra National University Peshawar M/S Haji Latif Construction Author
  • Wasiq Attique Institute of Environmental Sciences and Engineering (IESE), NUST Author
  • Ayesha Samreen Department of Electrical Engineering, NFC Institute of Engineering and Technology Multan Author
  • Maaz Bin Ubaid Punjab Irrigation Department. Government of the Punjab, Bahawalpur Zone, Pakistan The Islamia University of Bahawalpur (IUB), Department of Civil Engineering, Bahawalpur, Pakistan Author
  • Ahmad Saleem The Islamia University of Bahawalpur Author
  • Zohaib Akhtar The Islamia University of Bahawalpur Author

DOI:

https://doi.org/10.53762/grjnst.04.03.05

Keywords:

Smart Infrastructure, Artificial Intelligence, Digital Twins, Smart Grids, Civil Engineering, Electrical Engineering, Structural Health Monitoring, Reinforcement Learning, Smart Materials, Sustainable Infrastructure

Abstract

The rapid convergence of Artificial Intelligence (AI) with civil and electrical engineering is transforming traditional infrastructure into intelligent, adaptive, and sustainable smart infrastructure systems. This study presents a comprehensive review of AI-driven integration frameworks that bridge the physical domain of civil engineering with the energy and communication networks of electrical engineering. It highlights the role of machine learning, deep learning, and reinforcement learning techniques in enabling predictive modeling, real-time monitoring, and autonomous decision-making across infrastructure lifecycles. Key applications discussed include structural health monitoring using data-driven models, computer vision-based damage detection, and time-series forecasting for infrastructure performance. The paper further explores the critical role of smart grids in modern energy systems, emphasizing AI-based load forecasting, fault detection, and self-healing capabilities. Emerging paradigms such as Vehicle-to-Grid (V2G) systems and microgrid integration are examined as essential components of future urban resilience. Digital Twin technology is identified as a cornerstone of smart infrastructure, enabling real-time synchronization between physical assets and virtual models for predictive maintenance, lifecycle optimization, and risk-informed design. Additionally, the integration of smart materials, including self-healing concrete, shape memory alloys, and piezoelectric systems, introduces a new layer of material intelligence that enhances infrastructure durability and efficiency. Despite significant advancements, challenges such as data interoperability, cybersecurity, energy demands of AI systems, and regulatory constraints remain critical barriers. The study underscores the importance of interdisciplinary collaboration, standardization frameworks, and sustainable design strategies to fully realize the potential of AI-enabled infrastructure. Ultimately, the integration of AI with civil and electrical engineering offers a transformative pathway toward resilient, efficient, and future-ready built environments.

Downloads

Download data is not yet available.

Published

2026-05-10

Issue

Section

Articles