During the production of cement, a significant amount of CO2 emissions is generated. To address this issue, Lime Stone Calcinated Clay (LC3) was introduced in cement as a sustainable alternative, reducing the use of cement by 40-50% by replacing LC3 in the cement. This study investigates the effectiveness of LC3 in the hydration process, microstructural analysis, and sustainability. At the time of hydration, calcium hydroxide was generated, which, when mixed with metakaolin, produced a significant amount of CSH gel, thereby enhancing the mechanical strength and microstructural properties. Sturdy carboaluminates are created when limestone and aluminates interact, increasing chloride and sulfate resistance. Geometrical stability is ensured by controlled ettringite development and calcium Aluminate Ferrite trisubstituted (Aft)- Alumina-Ferric oxide-mono (AFm) transitions, although reinforcement is sustained by carbonation resistance. LC³ attains mechanical and durability properties when compared with conventional cement by decreasing emissions by reducing approximately 50% clinker factor and calcination temperatures from 700-900 °C.

This paper describes a new framework to integrate artificial intelligence (AI) with steel structural design for high-risk infrastructure industries such as oil & gas, petrochemical, and refinery usage. Employing machine learning (ML), deep learning (DL), and neural networks (NNs), the framework transforms traditional structural workflows to intelligent, adaptive processes. Trained with large collections of real-world engineering projects, AI models demonstrate significant performance enhancements—reducing design cycle time by 27%, raising structural accuracy, and enhancing resistance to dynamic strain from operational forces. The outcome heralds a new paradigm for industrial engineering, profiling by example how predictive modeling can be employed to design more safely, more efficiently, and code-compliant structures.