TY - JOUR
T1 - Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC)
AU - Guo, Pengwei
AU - Mahjoubi, Soroush
AU - Liu, Kaijian
AU - Meng, Weina
AU - Bao, Yi
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - AI-assisted design
KW - Design optimization
KW - Information extraction
KW - Machine learning
KW - Property prediction
KW - Ultra-high-performance concrete (UHPC)
UR - http://www.scopus.com/inward/record.url?scp=85175487214&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85175487214&partnerID=8YFLogxK
U2 - 10.1016/j.cscm.2023.e02625
DO - 10.1016/j.cscm.2023.e02625
M3 - Article
AN - SCOPUS:85175487214
SN - 2214-5095
VL - 19
JO - Case Studies in Construction Materials
JF - Case Studies in Construction Materials
M1 - e02625
ER -