Research

AI-based prediction for 3D-printed concrete

Predicting settlement and deformation is an important part of understanding how freshly printed concrete behaves. This research area connects data-driven modeling with the material and process conditions of additive construction.

Research focus

Settlement and deformation prediction, fresh-state behavior and the use of AI methods in concrete 3D printing.

The exact model, dataset and individual contribution will be added from the verified manuscript. This is a topic summary, not the paper’s formal title.

Nano-SiO₂ modified loess

Research team member studying the mechanical and time-dependent behavior of loess modified with nano-silica.

Methods and contribution

Participated in a program involving 32 triaxial specimens and analysis using a four-element Burgers model.

Reported project results

Model fits reached R² = 0.990–0.994; the reported reduction in void ratio was 7.94%. These are project-level results.

Self-healing stabilized road-base materials

Core team member working on crush-resistant microcapsules for stabilized road-base materials. The project investigates microcapsules that can withstand compaction and contribute to material repair.

Methods and contribution

Contributed to shell-material formulations and crush-resistance design, with triaxial, permeability and durability testing. Took a principal drafting role in patent materials.

Innovation output

An invention patent application has been published. Application status is listed separately from granted patents.