Exploring Artificial Intelligence & Machine Learning Applications in Structural Engineering Trends and Challenges

The primary focus in structural engineering is to create designs that are more efficient and secure while also advancing sustainable solutions. It is essential to adopt innovative approaches in design, analysis, and management as constructions become increasingly complex and projects expand in scale. Consequently, structural engineering today is progressively recognizing the potential for AI to bring about significant changes. The dominant approaches to design have largely been iterative, with certain cases involving the application of manual calculations. Nonetheless, it is essential to adopt a more analytical perspective in light of contemporary frameworks and transformations across different sectors. Artificial intelligence and its four types are widely utilized today, encompassing machine learning, data analytics, and cognitive computing. The various aspects of structural engineering can be enhanced through the analysis of computational data, the identification of patterns within extensive data sets, and the formulation of unique decisions. Consequently, this paper examines the various applications of structural engineering through the lens of AI. Fields including predictive modeling, health monitoring, seismic analysis, construction, and various other domains are encompassed within the scope of the examination. Nonetheless, certain drawbacks must be acknowledged, such as data limitations, restricted model interpretability, and resource constraints. Nonetheless, suggestions for tackling the issues have been presented, including explainable AI, data synthesis, and integrated educational models. The study aims to deliver comprehensive insights into the influence of AI on structural engineering, emphasizing its role in fostering the creation of safer and environmentally friendly structures.