Julien Ston

Julien Ston is a Researcher and Lecturer at the University of Applied Sciences and Arts of Fribourg, specifically within the School of Engineering and Architecture. His work focuses on advancing sustainable construction materials through innovative research approaches that combine traditional materials science with modern computational techniques.

His research interests span sustainable construction materials with particular expertise in recycled concrete technology, machine learning applications in civil engineering, and biomass waste utilization. Ston's work addresses critical challenges in the Swiss construction industry, where natural aggregate resources are becoming increasingly limited while demolition waste presents both a challenge and opportunity for sustainable development.

Ston's recent publications demonstrate a strong focus on developing predictive models for recycled concrete performance using machine learning techniques, with particular attention to fine recycled aggregates which represent 70-80% of concrete volume but remain underutilized due to performance uncertainties. His work bridges the gap between theoretical models and practical applications in sustainable construction.

His research has been supported by significant funding through multiple projects totaling over 575,000 CHF, reflecting the importance and relevance of his work to the construction industry's sustainability challenges. Current projects focus on developing adaptive tools for concrete formulation, optimizing recycled concrete mix designs using artificial intelligence, and creating new ecological concretes based on bio-based lightweight aggregates.

Ston collaborates extensively within the Institut iTEC and with colleagues across the School of Engineering and Architecture, contributing to a vibrant research community focused on sustainable construction solutions. His work has practical applications for concrete producers seeking to incorporate higher percentages of recycled materials while maintaining structural performance requirements.