Estimation of the compressive strength of ultrahigh performance concrete using machine learning models
The compressive strength of ultrahigh performance concrete (UHPC) is influenced by the composition, quality, and quantity of its constituent elements. Using traditional statistical methods, it is difficult for us to quantify the relationships between the technical properties of UHPC and the composit...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305324001455 |
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| author | Rakesh Kumar Divesh Ranjan Kumar Warit Wipulanusat Chanachai Thongchom Pijush Samui Baboo Rai |
| author_facet | Rakesh Kumar Divesh Ranjan Kumar Warit Wipulanusat Chanachai Thongchom Pijush Samui Baboo Rai |
| author_sort | Rakesh Kumar |
| collection | DOAJ |
| description | The compressive strength of ultrahigh performance concrete (UHPC) is influenced by the composition, quality, and quantity of its constituent elements. Using traditional statistical methods, it is difficult for us to quantify the relationships between the technical properties of UHPC and the composition of the mixture because of their complexity and nonlinearity. This work aims to develop advanced prediction models for estimating UHPC compressive strength over a large spectrum of supplementary cementitious material combinations and aggregate sizes. The models trained on the UHPC mixture dataset with 15 input variables included the group method of data handling, recurrent neural networks, long short-term memory, and bidirectional long short-term memory (Bi-LSTM). These models routinely forecast UHPC compressive strength according to sensitivity analysis, external validation, and statistical performance measures. During testing, the Bi-LSTM model outperformed the other models, with an RMSE of 0.0482 and an R² value of 0.9464. These results maximize component selection by showing how effectively the Bi-LSTM model might reduce UHPC formulation development and lower the cost and testing time span. |
| format | Article |
| id | doaj-art-50d31fa2d15b42c0ade9fcad8e375e63 |
| institution | DOAJ |
| issn | 2667-3053 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-50d31fa2d15b42c0ade9fcad8e375e632025-08-20T03:04:38ZengElsevierIntelligent Systems with Applications2667-30532025-03-012520047110.1016/j.iswa.2024.200471Estimation of the compressive strength of ultrahigh performance concrete using machine learning modelsRakesh Kumar0Divesh Ranjan Kumar1Warit Wipulanusat2Chanachai Thongchom3Pijush Samui4Baboo Rai5Department of Civil Engineering, Dayananda Sagar College of Engineering, Bengaluru, 560111, IndiaResearch Unit in Data Science and Digital Transformation, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, ThailandResearch Unit in Data Science and Digital Transformation, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand; Corresponding author.Research Unit in Structural and Foundation Engineering, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, ThailandDepartment of Civil Engineering, National Institute of Technology, Patna, IndiaDepartment of Civil Engineering, National Institute of Technology, Patna, IndiaThe compressive strength of ultrahigh performance concrete (UHPC) is influenced by the composition, quality, and quantity of its constituent elements. Using traditional statistical methods, it is difficult for us to quantify the relationships between the technical properties of UHPC and the composition of the mixture because of their complexity and nonlinearity. This work aims to develop advanced prediction models for estimating UHPC compressive strength over a large spectrum of supplementary cementitious material combinations and aggregate sizes. The models trained on the UHPC mixture dataset with 15 input variables included the group method of data handling, recurrent neural networks, long short-term memory, and bidirectional long short-term memory (Bi-LSTM). These models routinely forecast UHPC compressive strength according to sensitivity analysis, external validation, and statistical performance measures. During testing, the Bi-LSTM model outperformed the other models, with an RMSE of 0.0482 and an R² value of 0.9464. These results maximize component selection by showing how effectively the Bi-LSTM model might reduce UHPC formulation development and lower the cost and testing time span.http://www.sciencedirect.com/science/article/pii/S2667305324001455Ultrahigh performance concreteGroup method of data handlingLong short-term memoryBidirectional long short-term memoryRecurrent neural network |
| spellingShingle | Rakesh Kumar Divesh Ranjan Kumar Warit Wipulanusat Chanachai Thongchom Pijush Samui Baboo Rai Estimation of the compressive strength of ultrahigh performance concrete using machine learning models Intelligent Systems with Applications Ultrahigh performance concrete Group method of data handling Long short-term memory Bidirectional long short-term memory Recurrent neural network |
| title | Estimation of the compressive strength of ultrahigh performance concrete using machine learning models |
| title_full | Estimation of the compressive strength of ultrahigh performance concrete using machine learning models |
| title_fullStr | Estimation of the compressive strength of ultrahigh performance concrete using machine learning models |
| title_full_unstemmed | Estimation of the compressive strength of ultrahigh performance concrete using machine learning models |
| title_short | Estimation of the compressive strength of ultrahigh performance concrete using machine learning models |
| title_sort | estimation of the compressive strength of ultrahigh performance concrete using machine learning models |
| topic | Ultrahigh performance concrete Group method of data handling Long short-term memory Bidirectional long short-term memory Recurrent neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2667305324001455 |
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