This book investigates the integration of machine learning with additive manufacturing for optimizing engineering structures. It presents the latest research from the 1st International Conference on ADDitively Manufactured OPTimized Structures by means of Machine Learning (ADDOPTML), focusing on applications in civil structures and optimized structural elements. The content spans various optimization techniques, including linear, nonlinear, stochastic, and dynamic programming, alongside hybrid methods, metaheuristics, game theory, and nature-inspired algorithms. The contributions, selected through rigorous peer review, aim to advance research and foster collaboration in this interdisciplinary field.

This collection of research papers, stemming from the ADDOPTML conference, is intended for researchers and engineers at the forefront of structural optimization and additive manufacturing. The rigorous peer-review process suggests a high standard of technical content, likely appealing to specialists seeking to understand the cutting edge of machine learning applications in structural design. The breadth of topics covered indicates a comprehensive overview of current methodologies and future research avenues within the field.