Investigation of mechanical properties of high-performance concrete via multi-method of regression tree approach
Concrete's workability and durability are influenced by its mechanical properties, including Tensile and Compressive strength. High-performance concrete exhibits non-linear relationships between its compressive and tensile strength characteristics and the proportions of its constituent material...
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| Vydáno v: | Materials today communications Ročník 40; s. 109922 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.08.2024
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| ISSN: | 2352-4928, 2352-4928 |
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| Abstract | Concrete's workability and durability are influenced by its mechanical properties, including Tensile and Compressive strength. High-performance concrete exhibits non-linear relationships between its compressive and tensile strength characteristics and the proportions of its constituent materials, such as water, aggregate, cement, and admixtures. The investigation of the relations between the constituents of concrete and its mechanical properties has posed a challenging issue. This paper seeks to develop a range of single, hybrid, and ensemble models to resolve the problem of accurately estimating the mechanical properties of concrete according to its constituent components as input variables. In this regard, various frameworks incorporating a machine learning technique called decision tree and four metaheuristic optimization algorithms were utilized in model development to emulate the values of tensile and compressive strength. The findings indicated that the decision tree-dynamic arithmetic optimization algorithm hybrid model produced results with an correlation of determination of 0.9919 in compressive strength and 0.9933 in tensile strength, which were on average 1–3 % higher than that of the decision tree-dandelion optimizer algorithm, decision tree-arithmetic optimization algorithm, and decision tree-coot optimization algorithm, indicating superior optimization performance of dynamic arithmetic optimization algorithm. Additionally, the powerful prediction capability of the decision tree + dynamic arithmetic optimization algorithm + dandelion optimizer algorithm + coot optimization algorithm + arithmetic optimization algorithm ensemble model was noticeable with R2 of 0.9882 and 0.9936 in compressive and tensile strength estimation reliable to be used for various data produced in the future. In general, the utilization of coupling techniques to generate hybrid and ensemble models can enhance the accuracy of predictions while reducing the time and costs associated with experimental procedures.
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| AbstractList | Concrete's workability and durability are influenced by its mechanical properties, including Tensile and Compressive strength. High-performance concrete exhibits non-linear relationships between its compressive and tensile strength characteristics and the proportions of its constituent materials, such as water, aggregate, cement, and admixtures. The investigation of the relations between the constituents of concrete and its mechanical properties has posed a challenging issue. This paper seeks to develop a range of single, hybrid, and ensemble models to resolve the problem of accurately estimating the mechanical properties of concrete according to its constituent components as input variables. In this regard, various frameworks incorporating a machine learning technique called decision tree and four metaheuristic optimization algorithms were utilized in model development to emulate the values of tensile and compressive strength. The findings indicated that the decision tree-dynamic arithmetic optimization algorithm hybrid model produced results with an correlation of determination of 0.9919 in compressive strength and 0.9933 in tensile strength, which were on average 1–3 % higher than that of the decision tree-dandelion optimizer algorithm, decision tree-arithmetic optimization algorithm, and decision tree-coot optimization algorithm, indicating superior optimization performance of dynamic arithmetic optimization algorithm. Additionally, the powerful prediction capability of the decision tree + dynamic arithmetic optimization algorithm + dandelion optimizer algorithm + coot optimization algorithm + arithmetic optimization algorithm ensemble model was noticeable with R2 of 0.9882 and 0.9936 in compressive and tensile strength estimation reliable to be used for various data produced in the future. In general, the utilization of coupling techniques to generate hybrid and ensemble models can enhance the accuracy of predictions while reducing the time and costs associated with experimental procedures.
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| ArticleNumber | 109922 |
| Author | Tang, HaiLin Qi, Rui Wu, Haiyan Qi, Yongjun |
| Author_xml | – sequence: 1 givenname: Rui surname: Qi fullname: Qi, Rui organization: School of Architectural Engineering, Guangzhou Institute of Science and Technology, Guangzhou, Guangdong 510540, China – sequence: 2 givenname: Haiyan surname: Wu fullname: Wu, Haiyan email: why103392283@126.com organization: School of Management, South China Business College Guangdong University of Foreign Studies, Guangzhou, Guangdong 510545, China – sequence: 3 givenname: Yongjun surname: Qi fullname: Qi, Yongjun email: qyj200702022@baiyunu.edu.cn organization: Faculty of Megadata and Computing, Guangdong Baiyun University, Guangzhou, Guangdong 510450, China – sequence: 4 givenname: HaiLin surname: Tang fullname: Tang, HaiLin organization: Faculty of Megadata and Computing, Guangdong Baiyun University, Guangzhou, Guangdong 510450, China |
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| Keywords | B W/B High-performance concrete Decision tree DTCA DT DAOA AOA SF COA SI DOA W Ensemble learning SP MK DTAO R2 SHAP Meta-heuristic algorithms RMSE DTDA Compressive and tensile strength CS MAE NMSE HPC FA DTDO DTDDAC Ca TS |
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