As part of the research colloquium for final theses and doctoral as well as habiliation candidates, we cordially invite you to join us on Tuesday, February 10, 2026 at 4:30 pm in room 3516 (Mönchebergstr. 7). We are pleased to present the lecture by Ms. Daniela Masarzyck M.Sc. with the title

"Topology Optimization of Reinforced Concrete Structures: on the Concurrent Optimization of Structural Geometry and Material Properties"

We look forward to seeing you there!

Abstract

Concrete, the most popular construction material in civil engineering, exhibits a strong anisotropy in compressive and tensile strength, with its compressive strength exceeding the tensile strength by approximately a factor of ten. This limitation is commonly addressed by the introduction of steel reinforcement to the fresh concrete matrix, resulting in an anisotropic composite material. The objective of this study is to develop a topology optimization model that incorporates the material behavior of reinforced concrete and enables the simultaneous optimization of both structural layout and reinforcement distribution.

Topology optimization supports resource-efficient structural design in early planning stages by optimizing the material distribution within a design domain that is discretized by finite elements. Mechanical efficiency is ensured by minimizing elastic strain energy, promoting load transfer primarily through normal forces.

This work investigates the simultaneous optimization of structural design and local material properties, using a SIMP-based topology optimization framework. Reinforced concrete is represented using a smeared, macroscopic approach based on the theory of mixtures and the concept of volume fractions.The steel reinforcement is modeled as unidirectional fibers embedded in a concrete matrix, enhancing tensile strength and stiffness along their orientation. The design variable vector is extended to include reinforcement volume fraction and orientation. The method is thoroughly tested, and the interactions between design variables are evaluated.