Dataset Information

Development of 3D Printed Networks in Self-Healing Concrete.

ABSTRACT: This paper presents a new form of biomimetic cementitious material, which employs 3D-printed tetrahedral mini-vascular networks (MVNs) to store and deliver healing agents to damage sites within cementitious matrices. The MVNs are required to not only protect the healing agent for a sufficient period of time but also survive the mixing process, release the healing agent when the cementitious matrix is damaged, and have minimal impact on the physical and mechanical properties of the host cementitious matrix. A systematic study is described which fulfilled these design requirements and determined the most appropriate form and material for the MVNs. A subsequent series of experiments showed that MVNs filled with sodium silicate, embedded in concrete specimens, are able to respond effectively to damage, behave as a perfusable vascular system and thus act as healing agent reservoirs that are available for multiple damage-healing events. It was also proved that healing agents encapsulated within these MVNs can be transported to cracked zones in concrete elements under capillary driving action, and produce a recovery of strength, stiffness and fracture energy.

SUBMITTER: De Nardi C

PROVIDER: S-EPMC7143803 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Development of 3D Printed Networks in Self-Healing Concrete.

De Nardi Cristina C Gardner Diane D Jefferson Anthony Duncan AD

Materials (Basel, Switzerland) 20200314 6

This paper presents a new form of biomimetic cementitious material, which employs 3D-printed tetrahedral mini-vascular networks (MVNs) to store and deliver healing agents to damage sites within cementitious matrices. The MVNs are required to not only protect the healing agent for a sufficient period of time but also survive the mixing process, release the healing agent when the cementitious matrix is damaged, and have minimal impact on the physical and mechanical properties of the host cementiti ...[more]

PMID: 32183343

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Project description:The additive manufacturing of concrete, also known as 3D-printed concrete, is produced layer by layer using a 3D printer. The three-dimensional printing of concrete offers several benefits compared to conventional concrete construction, such as reduced labor costs and wastage of materials. It can also be used to build complex structures with high precision and accuracy. However, optimizing the mix design of 3D-printed concrete is challenging, involving numerous factors and extensive hit-and-trail experimentation. This study addresses this issue by developing predictive models, such as the Gaussian Process Regression model, Decision Tree Regression model, Support Vector Machine model, and XGBoost Regression models. The input parameters were water (Kg/m3), cement (Kg/m3), silica fume (Kg/m3), fly ash (Kg/m3), coarse aggregate (Kg/m3 & mm for diameter), fine aggregate (Kg/m3 & mm for diameter), viscosity modifying agent (Kg/m3), fibers (Kg/m3), fiber properties (mm for diameter and MPa for strength), print speed (mm/sec), and nozzle area (mm2), while target properties were the flexural and tensile strength of concrete (MPa data from 25 literature studies were collected. The water/binder ratio used in the dataset ranged from 0.27 to 0.67. Different types of sands and fibers have been used, with fibers having a maximum length of 23 mm. Based upon the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) for casted and printed concrete, the SVM model performed better than other models. All models' cast and printed flexural strength values were also correlated. The model's performance has also been checked on six different mix proportions from the dataset to show its accuracy. It is worth noting that the lack of ML-based predictive models for the flexural and tensile properties of 3D-printed concrete in the literature makes this study a novel innovation in the field. This model could reduce the computational and experimental effort required to formulate the mixed design of printed concrete.

Dataset Information

Development of 3D Printed Networks in Self-Healing Concrete.

Publications

Development of 3D Printed Networks in Self-Healing Concrete.

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