Cement is among the major contributors to the global carbon dioxide emissions. Thus, sustainable alternatives to the conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics to be a sustainable option for further research and investigation. Since the experimental work required for assessing its properties is both time consuming and complex, machine learning can be used to successfully predict the properties of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine learning methods, i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), multiple nonlinear regression (NLR), and linear regression are employed in this research. Sensitivity analysis, parametric analysis, and correlation factor (R²) are used to evaluate the obtained results. The ANN and ANFIS outperformed other methods.

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory experiments for designing the optimum content portions of RHAC.

This research focused on creating sustainable geopolymer mortar using waste materials such as corn cob ash (CCA) and bauxite. The CCA was obtained by burning corn cobs in an open environment and then further treated at 600 °C to remove carbon impurities. The resulting ash was ground to improve reactivity and used as a binder. Sodium silicate was used as an activator for geopolymerization. The geopolymer was prepared by combining the binder with fine aggregate, ground bauxite, and CCA in different proportions. The curing process involved heating the samples at 70 °C for 24 h followed by ambient temperature curing. Compression testing was conducted at 7, 14, and 28 days to assess the strength and durability of the geopolymer mortar. Testing was performed according to ASTM standards.