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Geopolymer concrete is sustainable, economical, eco-friendly, durable, and high-strength concrete. Geopolymer is a name for the bonding that occurs during the binding of materials in alkaline conditions. Due to the presence of high silica and alumina content, pozzolanic materials could be used as binding materials in the GPC. This research aims to check the sustainability and cost analysis of both GPC and conventional concrete with their physical, chemical, and mechanical properties. The experimental investigation analyzes both GPC and OPC concrete’s physical, chemical, and mechanical properties for the M30 mix design and analyzes the concrete’s cost and sustainability. The experimental investigation shows that the setting time, density, and drying shrinkage of conventional concrete are higher than the GPC. The compressive strength of the GPC and OPC concretes both showed similar trends at the 28-day strength, but the initial three-day strength of the GPC concrete was much higher than the OPC concrete. The splitting tensile strength and flexural strength of the GPC specimens are slightly higher than the OPC concrete mix specimens. The OPC concrete’s elastic modulus is slightly higher than the GPC mix design, whereas the Poisson’s ratio of the OPC concrete is slightly lower than the GPC specimens. The GPC specimens have higher thermal stability up to 800°C. The GPC utilizes industrial solid waste like fly ash and slag as a binding material and is activated by an alkaline solution containing NaOH and Na2SiO3 in the design mix. Therefore, the GPC has less embodied energy compared to the OPC concrete. The cost of the GPC at a bulk level reduced the cost of up to 40% of the OPC concrete.

The intelligent wireless system focuses on integrating with the advanced technologies like machine learning and related approaches in order to enhance the performance, productivity, and output. The implementation of machine learning approaches is mainly applied in order to enhance the efficient communication system, enable creation of variable node locations, support collection of data and information, analyze the pattern, and forecast so as to provide better services to the end users. The efficiency of using these technologies tend to lower the cost and support in deploying the resources effectively. The wireless network system tends to enhance the bandwidth, and the application of novel machine learning approaches supports detection of unrelated data and information and enables analysis of latency at each part of the communication channel. The study involves critically analyzing the key determinants of machine learning approaches in supporting enhanced intelligent network communication in the industries. The researchers are aimed at gathering both primary data and secondary data for the study. The respondents are chosen in the industry so that they can provide better inputs and insights related to the area of research. The key determinants considered for the study are machine learning-influenced management of hotspots, identification of critical congestion points, spectrum availability, and management. The analysis is made using SPSS data analysis package based on which it is noted that all the factors make major influences towards the intelligent communication, and hence machine learning supports critically in enhancing the user experience effectively.

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