2019/05/20 by Xiou Ge, Richard Goodwin, Richard T. Goodwin +12
Computer Science · Earth and Planetary Sciences · Engineering · #3D Surveying and Cultural Heritage #Artificial Intelligence (cs.AI) #Artificial intelligence #Building materials and conservation #Computer science #Ecology #Engineering #Environmental impact assessment #Extant taxon #FOS: Computer and information sciences #Generative grammar #Generative model #Infrastructure Maintenance and Monitoring #Machine learning #Resource (disambiguation) #Work (physics) #cs.AI
paper · pdf · doi:10.48550/arxiv.1905.08222
Presented at AAAI 2019 Spring Symposium, Towards AI for Collaborative Open Science (TACOS)
arxiv created 2019/05/20 · openalex publication_date 2019/05/20 · arxiv updated 2019/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Concrete is the most widely used engineered material in the world with more than 10 billion tons produced annually. Unfortunately, with that scale comes a significant burden in terms of energy, water, and release of greenhouse gases and other pollutants. As such, there is interest in creating concrete formulas that minimize this environmental burden, while satisfying engineering performance requirements. Recent advances in artificial intelligence have enabled machines to generate highly plausible artifacts, such as images of realistic looking faces. Semi-supervised generative models allow generation of artifacts with specific, desired characteristics. In this work, we use Conditional Variational Autoencoders (CVAE), a type of semi-supervised generative model, to discover concrete formulas with desired properties. Our model is trained using open data from the UCI Machine Learning Repository joined with environmental impact data computed using a web-based tool. We demonstrate CVAEs can design concrete formulas with lower emissions and natural resource usage while meeting design requirements. To ensure fair comparison between extant and generated formulas, we also train regression models to predict the environmental impacts and strength of discovered formulas. With these results, a construction engineer may create a formula that meets structural needs and best addresses local environmental concerns.