Early-age creep behaviour of 3D printable mortars: Experimental characterisation and analytical modelling

The construction sector is experiencing significant technological innovations with digitalisation tools and automated construction techniques, such as additive manufacturing. Additive manufacturing utilising cement-based materials can potentially remove the technological/economic barriers associated with innovative architectural/structural shapes which are not suitable for conventional formworks adopted for concrete material. However, in the “free-form” digital fabrication with concrete, the mechanical properties prediction of the material in the fresh state is essential for controlling both the element deformations and overall stability during printing. In this paper, the authors explore the critical aspects related to the determination of the early-age creep properties of a 3D printable cement-based material, particularly investigating such a behaviour at different resting times. The experimental results are used to calibrate the Burgers’ analytical model to consider both the elastic and the viscous response of the 3D printable mortar investigated in the fresh state. The visco-elastic model is validated by comparing the analytical total strain vs time curve with the corresponding experimental counterpart replicating the layer-by-layer stacking process in the 3D concrete printing process. It was found that the Burgers’ model represents a valuable numerical approach to evaluate the overall accumulation of layer deformation of a 3D printed element, since it is capable of taking into account the time dependency due to the time gap and the variable material stiffness over the process time.

MECHANICAL PROPERTIES PREDICTION OF PHENOLIC RESIN: A MOLECULAR DYNAMICS STUDY

Carbon-carbon composites (CCCs) widely used in the aerospace and automotive industries due to their excellent mechanical and thermal properties. Phenolic resins have a relatively high carbon yield, which makes them a suitable candidate for CCCs manufacturing. Molecular Dynamics (MD) can further reduce costs by predicting properties of a material before manufacturing and testing. In the present work, a Molecular Dynamics (MD) model of a crosslinked phenolic resin was developed to predict mechanical properties by implementing the fix bond/react algorithm in LAMMPS. The predicted mass density (ρ) and Young’s Modulus (E) agree well with experimental values and highlights the validity of the topologybased approach to building stable molecular models of phenolic resins.

Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction

The reinforcement of stabilized soils with fibers arises as an interesting technique to overcome the two main limitations of the stabilized soils: the weak tensile/flexural strength and the higher brittleness of the behavior. These types of mixtures require extensive laboratory characterization since they entail the study of a great number of parameters, which consumes time and resources. Thus, this work presents an alternative approach to predict the unconfined compressive strength (UCS) and the tensile strength of soil-binder-water mixtures reinforced with short fibers, following a Machine Learning (ML) approach. Four ML algorithms (Artificial Neural Networks, Support Vector Machines, Random Forest and Multiple Regression) are explored for mechanical prediction of reinforced soil-binder-water mixtures with fibers. The proposed models are supported on representative databases with approximately 100 records for each type of test (UCS and splitting tensile strength tests) and on the consideration of sixteen properties of the composite material (soil, fibers and binder). The predictive models provide an accurate estimation (R2 higher than 0.95 for Artificial Neuronal Networks algorithm) of the compressive and the tensile strength of the soil-water-binder-fiber mixtures. Additionally, the results of the proposed models are in line with the main experimental findings, i.e., the great effect of the binder content in compressive and tensile strength, and the significant effect of the type and the fiber properties in the assessment of the tensile strength.

Design of Experiment on Concrete Mechanical Properties Prediction: A Critical Review

Concrete mix design and the determination of concrete performance are not merely engineering studies, but also mathematical and statistical endeavors. The study of concrete mechanical properties involves a myriad of factors, including, but not limited to, the amount of each constituent material and its proportion, the type and dosage of chemical additives, and the inclusion of different waste materials. The number of factors and combinations make it difficult, or outright impossible, to formulate an expression of concrete performance through sheer experimentation. Hence, design of experiment has become a part of studies, involving concrete with material addition or replacement. This paper reviewed common design of experimental methods, implemented by past studies, which looked into the analysis of concrete performance. Several analysis methods were employed to optimize data collection and data analysis, such as analysis of variance (ANOVA), regression, Taguchi method, Response Surface Methodology, and Artificial Neural Network. It can be concluded that the use of statistical analysis is helpful for concrete material research, and all the reviewed designs of experimental methods are helpful in simplifying the work and saving time, while providing accurate prediction of concrete mechanical performance.