Alkali Activation and Natural Fiber Reinforcement of Termite Mound Soil for Sustainable, Low Cost and Environmentally Friendly Construction Materials
In the construction field, development of green, sustainable and renewable materials is the main focus of research. In that direction, alkali activation technique has attracted a lot of attention in the past few years. The alkali activation technique consists of activating aluminosilicates materials under alkaline conditions. To limit the risk of toxicity or exposure to high alkaline solution and eco-friendly concerns, One-part alkali activation is considered in this study. Besides, the application of this technique to optimize earth-based materials implies the production of robust earth-based bio-composites. However, termite mound soil is an earth-based material which has not been investigated sufficiently as a construction material. This study provides enlightenment on the application of one-part alkali activation of termite mound soil. The effects of the activator’s concentration, curing conditions, curing temperature on the specimens were appraised. The bio-composite was manufactured through mechanical compaction technique to obtain closely packed specimens. Fiber reinforcement was used as a strengthening and toughening process. The physical, macro-microstructural and mechanical properties were investigated to correlate between the microstructure and bulk properties. Results have shown improved mechanical properties after the alkaline activation. Furthermore, the dimensional stability of the specimens after the alkaline activation was satisfactory compared to the inactivated termite mound soil. Inclusion of the natural fiber impacted positively the mechanical properties of the bio-composite via crack propagation’s limitations within the termite mound soil-based matrix as the compressive strength increased after fiber’s inclusion. Furthermore, the Machine Learning techniques used to predict the compressive strength of the alkali activated termite mound soil based were successful confirming the right selection of the factors affecting the compressive strength. These Machine Learning approaches would reduce cost and time for the laboratory experiments and also predict the desired properties based on the mixtures required. The implications of these results are considered for the development of low-cost housing. Especially in regions with high housing demand but without accessibility to conventional building materials. Moreover, the results are analyzed as eco-friendly, renewable and sustainable prospective construction materials requiring effortless techniques to be replicated at the industrial scale. This investigation has demonstrated the feasibility of transforming termite mound soil and natural waste into sustainable construction materials to be easily industrialized.