The field of civil engineering is on the brink of a significant change. For centuries, our work has relied on physics, data, and experienced judgment. These foundations are still essential, but a powerful new partner has appeared: Artificial Intelligence (AI). It is no longer just a futuristic idea; AI is quickly evolving from a helpful tool into a key driver of innovation. We are at the dawn of a new era in which complex algorithms, rather than mere physical rules, will govern the planning, construction, and maintenance of our built environment. A paradigm shift that reinterprets the fundamentals of civil engineering research and practice, the emergence of AI—including Machine Learning (ML), Deep Learning (DL), and advanced robotics—is not just a minor technological advancement. It is fundamentally changing how we conduct research, design infrastructure, and manage our built environment. This editorial presents the transformation that AI is causing in civil engineering research. We are moving beyond just automation to a new era of predictive, optimized, and resilient engineering solutions. The question is not if AI will change our field, but how quickly and deeply we can use it to create a safer, more efficient, and sustainable future.

AI is revolutionizing the field of civil engineering, presenting powerful tools for optimizing the design, analysis, and maintenance of civil engineering constructions [1]. With its speedy computational capabilities and superior statistical processing, AI enables engineers to perform complex calculations that are vital for enhancing the performance and durability of structural systems. By leveraging AI, structural designs for concrete can be fine-tuned more accurately, reducing waste and optimizing resource allocation in projects of all scales. Regarding developing the concrete mixtures, AI accelerates the creation of enhanced concrete combinations through evaluating numerous components, which can acquire the selected strength, durability, and sustainability [2]. By using AI algorithms, it could evaluate the influence of cement types and other components on the concrete mixture's performance. This leads to special concrete mixtures ideal for particular structural requirements.

Additionally, AI can predict the degradation patterns of concrete through the years, supporting engineers in planning protection schedules as they should be and extending the service life of concrete structures. The integration of AI into concrete engineering permits the development of safer, longer-lasting, and more environmentally friendly concrete structures. By minimizing human errors and enhancing precision at every phase—from mixture components to structural evaluation and predictive protection—AI-driven procedures assist the improvement of resilient infrastructure. These AI improvements are shaping the future of concrete engineering by covering the full spectrum of AI-based programs for design, production, and lifecycle management in concrete and concrete structures. The Journal of Emerging Engineering Technologies (JEET) is dedicated to being the center of this important conversation. As a new member of the Editorial Board, my goal is to clearly articulate a vision for how AI is already changing the fundamentals of civil engineering research. I also want to strongly encourage the academic community to accept and guide this unavoidable change. The future of sustainable, resilient, and improved infrastructure relies on our ability to use AI effectively and ethically.

Civil engineering has always produced vast amounts of data. This includes geotechnical surveys, structural health monitoring, construction logs, and traffic flows [3–6]. In the past, the ability to fully use this data was limited. AI, especially ML and DL, is changing this situation. It is creating a new wave of research methods. The use of AI forces us to rethink traditional research methods in the key areas of civil engineering. This opens up three important new research frontiers:

Accelerating research in materials science

Conventional materials research relies on iterative physical testing to validate theories and is frequently labor-intensive, costly, and slow. By accurately predicting material properties from chemical composition and microstructural data, AI tools are radically altering this process and speeding up the production of modern building materials. It must prioritize research on AI-driven generative design for materials. Before a single experimental plan is created, ML can be used to create new composite materials, performance-specific concretes, or enhanced geopolymers. By optimizing the use of low-carbon or recycled additives, this method promotes environmental sustainability while significantly reducing the cost and development time.

Revolutionizing structural health monitoring (SHM)

The traditional approach of examining vital infrastructure, for instance, bridges, tunnels, dams, and high-rise buildings. The SHM for those infrastructure efforts is manual, time-consuming, and reactive. The smart monitoring systems using AI-driven technology provide a proactive and nonstop opportunity, turning unprocessed data collected by sensors into useful, accurate predictions. The development of robust anomaly detection algorithms that can systemize huge, heterogeneous streams of information from Internet of Things (IoT) sensors, drone-captured imagery, and satellite synthetic aperture radar is a crucial area of research. This consists of the use of computer vision tools to robotically locate minute structural deterioration, the beginning of corrosion, or settling. The most promising study trajectory includes the introduction of AI-enhanced digital twins. These algorithms integrate live data collected by sensors with highly accurate, physics-based AI simulation models to predict the closing useful lives of infrastructure, transferring maintenance from reactionary repair to truly predictive intervention.

Improving adaptation to climate change and resilience

As global climate change intensifies, extreme weather events, such as severe flooding, prolonged drought, and powerful winds, pose hitherto unheard-of risks to civil infrastructure. AI provides the analytical power required to design for adequate long-term flexibility and adaptability. We encourage academic researchers to employ innovative AI models for climate-informed design and probabilistic risk assessment of civil infrastructure. The ML models should be trained on extensive historical and anticipated climate data (weather patterns, hydraulic models, seismic activity) to predict the potential vulnerabilities of different design solutions across different future climate conditions. Additionally, AI can optimize immediate post-disaster response and resource allocation by evaluating current damage assessments from unmanned aerial vehicles and quickly identifying critical failure points in the network. This capacity is paramount to securing essential community services.

While the applications are transformative, the civil engineering community must also urgently address the fundamental methodological and ethical challenges inherent in integrating AI. Failing to address these will limit the scalability and reliability of AI solutions.

Interoperability, standardization, and data accessibility

High-quality, consistent, and varied datasets are essential to effective AI. Building Information Modeling (BIM) files, sensor readings, geotechnical reports, and historical project logs are just a few examples of the civil engineering data that are infamously fragmented, frequently imprisoned in proprietary formats, or inconsistent across different nations.

The critical role of explainable AI (XAI)

In the world of vital infrastructure, a "black box" answer is fundamentally unacceptable. Engineers have to understand why an AI version made a selected prediction—be it classifying a structure as safe or recommending a high-priced retrofitting operation. Trust is constructed on transparency. It is imperative that Explainable AI (XAI) be developed specifically for the field of civil engineering. Research should concentrate on developing specialized XAI techniques that directly align with proven engineering concepts (e.g., stress distribution, load pathways, failure mechanisms) in order to see and explain the complicated decisions made by ML models. This ensures the transparency, justification, and complete auditability of AI decisions by qualified experts.

Promoting multidisciplinary cooperation

It seems unlikely that civil engineers working alone will make the next major advancement in the field. It will require cooperation from the smooth, ongoing cooperation of data scientists, computer vision specialists, and cognitive scientists with structural, geotechnical, and transportation engineers. To actively support interdisciplinary research teams, academic curricula and professional development efforts need to be redesigned. This entails educating a new generation of engineers who are proficient in data manipulation, AI algorithm selection, and ethical computational practice in addition to being fluent in structural mechanics and hydrology.

Both intellectual rigor and an entrepreneurial spirit are necessary for the shift to an AI-driven research environment. AI is increasingly essential to the primary goal of enhancing infrastructure globally; the time of considering it as an auxiliary tool is long gone. The Journal of Emerging Engineering Technologies (JEET) is currently soliciting outstanding research articles that investigate these crucial intersections. We are especially eager to publish validated research that tackles the ethical concerns of autonomous design, the methodological difficulties of data standardization, and the tangible, observable uses of AI in enhancing infrastructure resilience and overall sustainability. To our outstanding Editorial Board and fellow scholars: we must use our combined knowledge to steer the journal toward publishing the fundamental studies that will shape our field's future. Our ability to lead the way in the future now is what will enable us to create a world that is safer, smarter, and more sustainable. Let's take on this challenge and create the AI-based computation framework for civil engineering in the coming century.