Predicting Green Concrete Performance Using AI
Keywords:
AI, artificial intelligence; concrete; infrastructure; life-cycle assessment; machine learning; neural network; sustainability.Abstract
Performance prediction of concrete exhibiting extreme average performance, or a sufficiently accurate upper bound on performance, can be achieved with a comparatively small number of high-performance samples. AI-assisted performance prediction thus offers an attractive research avenue for use, for instance, in accelerating the quest for ideal material compositions allowing for reduced emissions while fulfilling performance criteria. When trained on a suitable dataset, phonetic characteristics from AI-driven models can demonstrate how changing a specific variable influences performance outcome, thus providing insight for design. Moreover, when calibrated with the appropriate input parameters, AI-based prediction models can be incorporated into broader Civil Engineering models to account for durability of surface materials used in infrastructures such as bridges or tunnels exposed to specific environmental loading.
The AI approach can be used advantageously when applied in areas where specialised knowledge is limited or when it is difficult to explore a problem space adequately via simulations. Results presented in this study therefore aim to advance these areas with consideration to sustainability by demonstrating a specific AI model capable of increasing the development speed and enhancing the knowledge regarding a considered concrete. Further results will support the analysis of durability of campuses and Civil Engineering assets under specific environmental loading and will pave the way towards a PhD research topic encompassing planning and support for future interventions in Rock Engineering related scenarios.C
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