10173016Cementitious Advanced Material Prediction and Evaluation Resource (CAMPER)ClosedFast Start ResponseInnovate UK**Cementitious Advanced Material Prediction and Evaluation Resource (CAMPER)** CAMPER is an innovative AI-powered framework that will transform sustainable construction by enabling the intelligent design of low-carbon concrete mixtures. The project focuses on the development and validation of a novel digital tool to predict the performance of waste-derived supplementary cementitious materials (SCMs), supporting a more circular, affordable, and environmentally responsible construction sector. Cement production accounts for around 8% of global CO2 emissions, largely due to the manufacturing of conventional Portland cement (CEM I). As global demand for concrete continues to grow, identifying sustainable alternatives is essential. SCMs made from industrial and agricultural waste---such as fly ash, ground granulated blast furnace slag, rice husk ash, biochar, and waste glass---have great potential, but their widespread use is limited by the variability and complexity of their properties. CAMPER addresses this challenge by combining experimental material characterisation with advanced data analytics and machine learning. The project will develop a digital platform that integrates key physicochemical data---such as elemental composition, microstructure, pozzolanic activity, and environmental indicators---with measured performance outcomes like strength, durability, and leachability. These data will be used to train predictive algorithms capable of recommending optimal material combinations for SCM use. A web-based interface will make the system accessible to researchers, engineers, and material developers, enabling them to explore novel SCM combinations, reduce testing costs, and accelerate sustainable innovation in the construction industry. Selected combinations of waste-based materials will be validated through mortar mix testing to confirm the framework's predictions in practical applications. The expected outcomes include: * A validated machine learning model for predicting SCM performance * New, data-driven insights into the behaviour of waste-based cementitious materials * Reduced reliance on high-carbon cement, contributing to national net-zero targets * A public-facing digital tool to support low-carbon concrete mix development CAMPER represents a major step forward in the use of AI for green construction innovation and will support the UK's transition to a circular, low-carbon economy.