Abstract : Geo-polymer composites are emerging as sustainable replacement options of conventional cement concrete, for effective reduction in carbon footprint. Owing to the use of supplementary cementitious materials viz. fly ash, GGBS, metakaolin, brick husk ash and their combinations, the resulting concrete is different from the conventional concrete in terms of fineness, density, packing etc. Hence, the correlation equations used to derive compressive strength values from non- destructive test results, for conventional concrete, cannot be applied to the Geo-polymer concrete. In the recent experimental research program, six types of geo-polymer concrete specimen were prepared to evaluate their performance. Both nondestructive tests (Schmidt rebound hammer, Ultrasonic pulse velocity) and destructive tests were conducted to determine compressive strength using suitable regression equations. The collected data from tests were first imported into predefined models for analysis of polynomial regression technique (PRT) to derive the equations. To improvise the data structure performance and avoid issues such as over fitting and under fitting, the datasets were imported in machine learning algorithms, including ANN, ANFIS techniques. If we compare regression analysis, ANN and ANFIS, ANN the latter yields less error than the former..
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Abstract : The paper addresses challenges in measuring sustainable development indicators in cities, highlighting the importance of considering variations in city categories and sizes. It emphasises that problems arise from measurement methods that do not account for differences in developmental capabilities and categories of cities. The study proposes a novel approach to reclassifying sustainable development indicators based on the developmental capabilities of each city, with a specific focus on the Al-Qassim region in Saudi Arabia. By studying the developmental capabilities of urban settlements in Al-Qassim and linking them to Sustainable Development Goals (SDGs), the research aims to establish a connection between these capabilities and relevant indicators. This process involves excluding certain indicators or adjusting calculation methods based on the developmental capacities of cities, ensuring a more accurate reflection of their sustainable development performance. The classification process involves assessing variables such as population size, service centrality, and spatial centrality to determine the minimum threshold for urban settlements. Cities that fall below this threshold are excluded from the analysis. The study highlights the need to reevaluate indicators related to population size and service levels, especially for cities with regional roles, to align measurement methods with the specific characteristics of each city. Ultimately, the research underscores the significance of aligning sustainable development indicators with the developmental capabilities of cities to enhance decision-making processes and promote more effective urban development strategies..
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