Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC)
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 techniqu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509523008057 |
| _version_ | 1797454587422048256 |
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| author | Pengwei Guo Soroush Mahjoubi Kaijian Liu Weina Meng Yi Bao |
| author_facet | Pengwei Guo Soroush Mahjoubi Kaijian Liu Weina Meng Yi Bao |
| author_sort | Pengwei Guo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:39:20Z |
| format | Article |
| id | doaj.art-c374b03423f943a98b7ab1ad3cf4d3c3 |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-03-09T15:39:20Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-c374b03423f943a98b7ab1ad3cf4d3c32023-11-25T04:49:32ZengElsevierCase Studies in Construction Materials2214-50952023-12-0119e02625Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC)Pengwei Guo0Soroush Mahjoubi1Kaijian Liu2Weina Meng3Yi Bao4Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United StatesDepartment of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United StatesDepartment of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United StatesDepartment of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United StatesCorresponding author.; Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United StatesMachine 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.http://www.sciencedirect.com/science/article/pii/S2214509523008057AI-assisted designDesign optimizationInformation extractionMachine learningProperty predictionUltra-high-performance concrete (UHPC) |
| spellingShingle | Pengwei Guo Soroush Mahjoubi Kaijian Liu Weina Meng Yi Bao Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC) Case Studies in Construction Materials AI-assisted design Design optimization Information extraction Machine learning Property prediction Ultra-high-performance concrete (UHPC) |
| title | Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC) |
| title_full | Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC) |
| title_fullStr | Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC) |
| title_full_unstemmed | Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC) |
| title_short | Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC) |
| title_sort | self updatable ai assisted design of low carbon cost effective ultra high performance concrete uhpc |
| topic | AI-assisted design Design optimization Information extraction Machine learning Property prediction Ultra-high-performance concrete (UHPC) |
| url | http://www.sciencedirect.com/science/article/pii/S2214509523008057 |
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