Crushed Bricks: Demolition Waste as a Sustainable Raw Material for Geopolymers

Valorisation of Tuff and Brick Wastes by Alkali Activation for Historical Building Remediation

Nowadays, the preservation and restoration of a historical building needs to be faced in accordance with a novel sensibility regarding the environment in order to preserve the building for future generations. In this context, the scientific community is focusing on novel and sustainable materials and techniques that allow for durability and mechanical performance as well as compatibility with the existing heritage. Alkali-activated materials represent a great challenge to the production of new materials, starting from the existing ones, with the goal of reducing consumption, emission of greenhouse gases and environmental impact. This study deals with the valorisation of waste materials coming from demolition and construction activities in the manufacture of geocomposites suitable for the restoration and conservation of historical heritage. In particular, waste from tuff sawing and brick grinding were used as raw materials, and then the geopolymeric samples produced were characterized based on a physical-chemical and mechanical point of view in order to investigate their performance and evaluate their suitability as materials for a historical building's recovery. The results showed that brick waste-based geocomposites were more compact than the tuff-based ones, as shown by the higher-density values and the lower values of open porosity and water absorption and as further confirmed by the trend of the mechanical performance. Moreover, experimental data showed that the physical and mechanical properties of both bricks and tuff waste-based geocomposites, even with different waste content, are compatible with existing building materials as well as traditional repairing products.

Investigation of Mechanical Properties and Microstructure of Construction- and Demolition-Waste-Based Geopolymers

Construction and demolition waste (CDW) is the third-most abundant waste generated annually in the countries of the European Union. One of the alternatives to the use of these wastes is geopolymeric materials. Partial replacement of commonly used raw materials for the production of these materials can help reduce the number of landfills and the consumption of natural resources. In this study, the authors partially replaced metakaolin and fly ash with clay bricks and concrete debris. The research method in article is connected with analysis of microstructures and the mechanical and physical properties of the geopolymers. The results obtained show the possibility of manufacturing useful construction materials based on industrial byproducts (fly ash) and CDW. Compressive strength and flexural strength were, for samples containing metakaolin, 20.1 MPa and 5.3 MPa, respectively. Geopolymers containing fly ash displayed 19.7 MPa of compressive strength and 3.0 MPa of flexural strength. The results for both synthesized materials give them perspectives for future applications in the construction industry.

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na₂SiO₃/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study’s aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient (R²). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts.