Optimization of Construction Processes Using Artificial Intelligence and Big Data Analytics

The rise in the Internet of Things (IoT)-based smart building construction process offers an acceptable fix to the challenging issues of energy administration, equitable expansion, and economic optimization as the number of people living in metropolitan areas rises and the desire for eco-friendly lifestyles rises. This study investigates the construction process in smart buildings by offering a deep learning (DL) approach to energy optimization. The investigation outlines a novel architecture that uses DL methods to optimize energy use in IoT-based smart buildings. To enable intelligent energy optimization and performance choices, it leverages current statistics from various resources, such as detectors, gadgets, big data processing, and smart grids. By using DL systems, such as neural networks and recurrent neural networks (RNNs), the suggested method provides accurate demand predictions while adhering to flexible use patterns. The empirical study demonstrates the efficacy of the DL-based approach using actual case investigations and intricate models conducted in multiple intelligent construction processes across the globe. The results demonstrate notable energy consumption drops, financial savings, and noteworthy actions to the eradication of carbon emissions, thus enhancing ecological sustainability. Furthermore, the system's adaptability and scalability demonstrate how it may be used in a range of urban contexts. In addition to addressing contemporary energy optimization in construction process concerns, this research paper lays the groundwork for a more efficient and ecologically responsible metropolitan future.