An Editorial vision for civil engineering: navigating intelligence and innovation
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Date
2025-11-01
Journal Title
Journal ISSN
Volume Title
Publisher
Mesopotamian Academic Press
Abstract
The integration of Artificial Intelligence in civil engineering has seen a major advancement over the applications of primitive data-driven models, reaching advanced hybrid physics-informed models. This evolution represents a significant change of paradigm from the basic predictive analytics to what is now called "structural cognition." But in this cutting-edge paradigm, the evolutionary learning of AI beyond the structural prediction is further developed, with the ability to learn underlying causal relationships in complicated engineering systems. This not only allows them to diagnose potential problems, but also to proactively propose well-targeted remedial measures, giving engineers a greater understanding of the situation as well as better, better-informed decision-making with respect to infrastructure sustainability and safety [1], [2]. A critical element to this evolution is the emergence of "perceptive infrastructure." This idea is further greatly supported by similar inventions like wavelet-diffusion architectures, and it is very useful in the design of the most robust vision-based monitoring systems. The systems are especially well-suited to demanding environments, such as low-light and other bad weather conditions where conventional surveillance techniques tend to break down. By equipping infrastructure with the ability to sense and make real-time sense of their environment with high fidelity, these technologies are ushering in a new era of self-sensing, self-reporting and even self-predicting civil assets, heading toward constant intelligent self-diagnosis [1]. Figure 1 illustrates artificial intelligence tools in civil engineering.
Description
The editorial explores the transformative integration of artificial intelligence (AI) in civil engineering, evolving from basic predictive analytics to hybrid physics‑informed “structural cognition” models that autonomously diagnose, predict, and remediate infrastructure issues, thus enhancing sustainability and safety. By introducing “perceptive infrastructure” using wavelet‑diffusion architectures and vision‑based monitoring in challenging conditions, it highlights a paradigm shift toward self‑sensing, self‑reporting civil assets. This aligns with SDG 9 (industry, innovation), SDG 11 (sustainable cities), SDG 3 (health and safety), and SDG 13 (resilience), and resonates with Uganda’s NDP IV aspirations for technological innovation, resilient infrastructure, and environmental sustainability.
Keywords
Civil Engineering, Editorial Vision, Intelligence, Innovation
Citation
Mijwil, M. M., Adamopoulos, I., & Ali, G. (2025). An Editorial vision for civil engineering: navigating intelligence and innovation. Mesopotamian Journal of Civil Engineering, 2025, 79–82. https://doi.org/10.58496/MJCE/2025/006