Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC)

Pengwei Guo, Soroush Mahjoubi, Kaijian Liu, Weina Meng, Yi Bao

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Machine learning has exhibited high efficiency in designing concrete. However, collecting the dataset for training machine learning models is challenging. To address this challenge, this paper develops an approach to collect concrete design data automatically based on information extraction techniques. The approach enables machine learning models to automatically track, extract, and learn knowledge embedded in data from relevant publications. The approach has been incorporated into AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC) via integrating the capabilities of automatically collecting and processing data, predicting UHPC properties, and optimizing UHPC properties regarding the material cost, carbon footprint, and compressive strength. A self-updating mechanism is imparted to continuously learn available data. Such a mechanism enables the self-updatable automatic discovery of low-carbon cost-effective UHPC. The results showed increasing prediction accuracy and optimization performance of the proposed approach over time when more knowledge was learned from new data, therefore accelerating the design of UHPC.

Original languageEnglish
Article numbere02625
JournalCase Studies in Construction Materials
Volume19
DOIs
StatePublished - Dec 2023

Keywords

  • AI-assisted design
  • Design optimization
  • Information extraction
  • Machine learning
  • Property prediction
  • Ultra-high-performance concrete (UHPC)

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