IoT-Driven Green Building Materials and Life Cycle Assessment with Gradient Boosting Machines

To evaluate the long-term viability of sustainable building materials, this research proposes a new method that combines the Internet of Things (IoT) with Gradient Boosting Machines (GBM). The need for reliable ways to assess the environmental impact of construction materials has grown in tandem with the growing relevance of ecological concerns in industry. Using IoT sensors, our proposed technique tracks material characteristics, energy usage, and environmental implications over the product's life cycle in real-time. It analyzes the data obtained to predict the environmental performance and evaluate the sustainability of green building materials using GBM, an advanced machine learning technique. It shows that our method effectively assesses the environmental impacts of different building materials over their life cycles through case studies and empirical validation. This will help architects, engineers, and legislators make better decisions about sustainable construction. More eco-friendly structures and infrastructure may be created with the help of an IoT framework, which provides an effective and scalable way to speed up the adoption of green construction materials.