Semester of Graduation

Fall 2025

Degree

Master of Science (MS)

Department

Bert. S Tuner Department of Construction Management

Document Type

Thesis

Abstract

Construction 3D Printing (C3DP) holds great potential for automated construction but faces key challenges, including the high Portland cement content of its printing materials. The high cement content raises material costs, causes shrinkage and thermal cracking, and increases its carbon footprint. This study introduces a novel hybrid computational pipeline that combines image based automated gradation analysis with 2D stochastic particle packing simulation, to design low-cement printable materials with minimal experimental testing. The image-based gradation system achieved a mean absolute error under 3.64% across six fine and coarse aggregate types. The particle packing simulation demonstrated an average error of less than 1.7%, with a maximum error of 2.2% across 225 simulation cases involving 46 aggregate blends. Using the simulation outputs, four printing materials were designed and tested, with cement contents ranging from 355 to 385 kg/m³, achieving a 35-45% reduction in cement compared to conventional printing mixtures. The mixture with the highest packing density and the lowest cement content (355.8 kg/m³) showed the highest 14-day flexural strength of 6.86 MPa, along with acceptable shape stability and printability. The proposed computational framework minimizes the need for extensive, time-consuming trials while enabling significant cement reduction without compromising mechanical performance. It offers a scalable approach to designing printing materials with local aggregates, thereby lowering carbon footprints, minimizing the risk of shrinkage cracking, and improving the economic viability of C3DP.

Date

10-26-2025

Committee Chair

Ali Kazemian, Ph.D.

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Available for download on Friday, May 01, 2026

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