Advancing Structural Design with Machine Learning: Stress Field Predic- tion in Plates with Cutouts

Resumo

Machine learning techniques are creating disruptive approaches in diverse engineering fields, including
in engineering and structural design, pushing the boundaries of performance and reliability. This presentation
delves into the development of a machine learning model that accurately predicts stress fields in complex structures.
By leveraging the SimuStruct dataset, encompassing diverse geometries and configurations, valuable insights are
gained into the challenges faced in structural engineering.
Complex structures, especially those with holes, introduce complexities that affect structural behavior and

integrity. Accurately predicting stress distribution in these configurations is crucial for ensuring safety and perfor-
mance. The incorporation of hole-containing structures into the SimuStruct dataset enables training and evaluating

machine learning models specifically tailored to address this critical aspect of structural design. This resource
facilitates optimization and informed decision-making.
The application of machine learning in predicting stress fields in structures with holes holds promise for
enhanced design and performance. By precisely capturing stress distribution, engineers can identify regions of

heightened stress concentration, enabling informed choices in material selection, reinforcement, and weight reduc-
tion. These advancements lead to improved efficiency, reliability, and safety in structural operations. The focus on

structures with holes in the SimuStruct dataset, alongside the development of machine learning models for stress
prediction, significantly impacts the engineering industry, fostering innovation and optimization while ensuring
structural integrity under complex loading conditions.

In conclusion, the integration of structures with holes or other discontinuities into the SimuStruct dataset di-
rectly addresses ongoing challenges in structural design. The remarkable development of machine learning models

for stress prediction represents a significant leap forward, empowering researchers and engineers to optimize de-
sign and achieve substantial improvements in efficiency, reliability, and safety. With a focus on complex structures,

machine learning techniques revolutionize the industry, paving the way for innovative advancements in structural
integrity and performance.