This study presents an investigation into the durability of geopolymer concrete prepared using high calcium fly ash along with alkaline activators when exposed to 2% solution of sulfuric acid and 5% magnesium sulphate for up to 45 days. The durability was also assessed by measuring water absorption and sorptivity. Ordinary Portland cement concrete was also prepared as control concrete. The grades chosen for the investigation were M20, M40, and M60. The alkaline solution used for present study is the combination of sodium silicate and sodium hydroxide solution with the ratio of 2.50. The molarity of sodium hydroxide was fixed as 12. The test specimens were 150 × 150 × 150 mm cubes, 100 × 200 mm cylinders, and 100 × 50 mm discs cured at ambient temperature. Surface deterioration, density, and strength over a period of 14, 28, and 45 days were observed. The results of geopolymer and ordinary Portland cement concrete were compared and discussed. After 45 days of exposure to the magnesium sulfate solution, the reduction in strength was up to 12% for geopolymer concrete and up to 25% for ordinary Portland cement concrete. After the same period of exposure to the sulphuric acid solution, the compressive strength decrease was up to 20% for geopolymer concrete and up to 28% for ordinary Portland cement concrete.

Liveability is an abstract concept with multiple definitions and interpretations. This study builds a tangible metric for liveability using responses from a user survey and uses Machine Learning (ML) to understand the importance of different factors of the metric. The study defines the liveability metric as an individual’s willingness to live in their current location for the foreseeable future. Stratified random samples of the results from an online survey conducted were used for the analysis. The different factors that the residents identified as impacting their willingness to continue living in their neighborhood were defined as the “perception features” and their decision itself was defined as the “liveability feature”. The survey data were then used in an ML classification model, which predicted any user’s liveability feature, given their perception features. ‘Shapley Scores’ were then used to quantify the marginal contribution of the perception features on the liveability metric. From this study, the most important actionable features impacting the liveability of a neighborhood were identified as Safety and Access to the Internet/Organic farm products/healthcare/Public transportation. The main motivation of the study is to offer useful insights and a data-driven framework to the local administration and non-governmental organizations for building more liveable communities.

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