Dataset Information

Automation in the Construction of a 3D-Printed Concrete Wall with the Use of a Lintel Gripper.

ABSTRACT: Developments in the automation of construction processes, observable in recent years, is focused on speeding up the construction of buildings and structures. Additive manufacturing using concrete mixes are among the most promising technologies in this respect. 3D concrete printing allows the building up of structure by extruding a mix layer by layer. However, the mix initially has low capacity to transfer loads, which can be particularly troublesome in cases of external components that need to be placed on top such as precast lintels or floor beams. This article describes the application of additive manufacturing technology in the fabrication of a building wall model, in which the door opening was finished with automatic lintel installation. The research adjusts the wall design and printing process, accounting for the rheological and mechanical properties of the fresh concrete, as well as design requirements of Eurocode. The article demonstrates that the process can be planned precisely and how the growth of stress in fresh concrete can be simulated, against the strength level developed. The conclusions drawn from this research will be of use in designing larger civil structures. Furthermore, the adverse effects of concrete shrinkage on structures is also presented, together with appropriate methods of control.

SUBMITTER: Hoffmann M

PROVIDER: S-EPMC7215518 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Automation in the Construction of a 3D-Printed Concrete Wall with the Use of a Lintel Gripper.

Hoffmann Marcin M Skibicki Szymon S Pankratow Paweł P Zieliński Adam A Pajor Mirosław M Techman Mateusz M

Materials (Basel, Switzerland) 20200411 8

Developments in the automation of construction processes, observable in recent years, is focused on speeding up the construction of buildings and structures. Additive manufacturing using concrete mixes are among the most promising technologies in this respect. 3D concrete printing allows the building up of structure by extruding a mix layer by layer. However, the mix initially has low capacity to transfer loads, which can be particularly troublesome in cases of external components that need to b ...[more]

PMID: 32290384

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Project description:A single universal robotic gripper with the capacity to fulfill a wide variety of gripping and grasping tasks has always been desirable. A three-dimensional (3D) printed modular soft gripper with highly conformal soft fingers that are composed of positive pressure soft pneumatic actuators along with a mechanical metamaterial was developed. The fingers of the soft gripper along with the mechanical metamaterial, which integrates a soft auxetic structure and compliant ribs, was 3D printed in a single step, without requiring support material and postprocessing, using a low-cost and open-source fused deposition modeling (FDM) 3D printer that employs a commercially available thermoplastic poly (urethane) (TPU). The soft fingers of the gripper were optimized using finite element modeling (FEM). The FE simulations accurately predicted the behavior and performance of the fingers in terms of deformation and tip force. Also, FEM was used to predict the contact behavior of the mechanical metamaterial to prove that it highly decreases the contact pressure by increasing the contact area between the soft fingers and the grasped objects and thus proving its effectiveness in enhancing the grasping performance of the gripper. The contact pressure can be decreased by up to 8.5 times with the implementation of the mechanical metamaterial. The configuration of the highly conformal gripper can be easily modulated by changing the number of fingers attached to its base to tailor it for specific manipulation tasks. Two-dimensional (2D) and 3D grasping experiments were conducted to assess the grasping performance of the soft modular gripper and to prove that the inclusion of the metamaterial increases its conformability and reduces the out-of-plane deformations of the soft monolithic fingers upon grasping different objects and consequently, resulting in the gripper in three different configurations including two, three and four-finger configurations successfully grasping a wide variety of objects.

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

Automation in the Construction of a 3D-Printed Concrete Wall with the Use of a Lintel Gripper.

Publications

Automation in the Construction of a 3D-Printed Concrete Wall with the Use of a Lintel Gripper.

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