AI-Enabled Lifecycle Analysis of Sustainable Composites for Nigeria’s Low-Cost Housing Sector

Abstract

The intersection of sustainable material development and artificial intelligence (AI) presents transformative opportunities for addressing Nigeria’s growing affordable housing deficit. This study investigates the lifecycle performance of four agro-based composite materials, like bamboo-cement panels, palm kernel shell concrete, rice husk ash blended cement, and coconut coir–stabilized earth blocks, tailored for application in low-cost housing across Nigeria’s diverse climatic zones. A comprehensive methodology combining experimental testing, lifecycle assessment (LCA), and AI-based predictive modeling, including Artificial Neural Networks (ANN), Random Forest, and Gradient Boosting adopted to evaluate the structural, environmental, and economic suitability of each composite. Mechanical and thermal properties were assessed in accordance with ASTM standards, while lifecycle environmental impacts, including global warming potential (GWP) and embodied energy, were modeled using OpenLCA and the ReCiPe Midpoint method. Economic performance was evaluated over a 30-year horizon. ANN models achieved R² values of up to 0.94, affirming their utility in predictive lifecycle analysis. The results demonstrated significant performance trade-offs. Bamboo-cement offered the highest compressive strength but incurred the greatest GWP and cost. Rice husk ash composites emerged as the most environmentally and economically sustainable option. Coconut coir–earth blocks exhibited superior thermal insulation at low cost but limited structural performance. The study provides a robust, replicable framework for material selection and sustainability optimization in emerging economies and recommends integration of AI-enhanced LCA tools into Nigeria’s national building codes to guide evidence-based material choices. By embedding AI into LCA workflows, this research enables evidence-based decision-making for climate-resilient, affordable housing in Nigeria.