Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete
The present study utilizes advanced numerical evaluation techniques like Artificial Intelligence (AI), including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems with Genetic Algorithms (ANFIS-GA), Gene Expression Programming (GEP), and Multiple...
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| Format: | Article |
| Language: | English |
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Sciendo
2025-01-01
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| Series: | Architecture, Civil Engineering, Environment |
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| Online Access: | https://doi.org/10.2478/acee-2024-0014 |
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| author | AGRAWAL Achal CHANDAK Narayan |
| author_facet | AGRAWAL Achal CHANDAK Narayan |
| author_sort | AGRAWAL Achal |
| collection | DOAJ |
| description | The present study utilizes advanced numerical evaluation techniques like Artificial Intelligence (AI), including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems with Genetic Algorithms (ANFIS-GA), Gene Expression Programming (GEP), and Multiple Linear Regression (MLR) to develop and compare the predictive models for determination of compressive and tensile strength. Partial mutual information for selection and establishment of the degree of association of variables was used to aid in better attainment of results obtained through predictive models. It was observed that amongst the modeling techniques, the results obtained for compressive strength through the SVM technique were excellent, producing an Index of Agreement of 0.96, Akaike Information Criterion of 68.33, skill score of 0.96, and symmetric uncertainty of 0.93, thus indicating a simpler, robust, and low uncertainty predictive model. Furthermore, the adapted technique MLR was found to predict tensile strength characteristics better, with the MLR model demonstrating a higher R2 value of 0.81, thus implying a reliable tensile strength prediction model. However, SVM consistently performed well for both compressive and tensile strength characteristics thus endorsing the reliability of the predictive model. Overall, the study aids in getting new insights about improvising the strength properties of SCC and its evaluation through predictive techniques. |
| format | Article |
| id | doaj-art-b6db822ef0314cb7b3d4452903e71b5c |
| institution | Kabale University |
| issn | 2720-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Architecture, Civil Engineering, Environment |
| spelling | doaj-art-b6db822ef0314cb7b3d4452903e71b5c2025-01-14T14:22:36ZengSciendoArchitecture, Civil Engineering, Environment2720-69472025-01-01172698610.2478/acee-2024-0014Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting ConcreteAGRAWAL Achal0CHANDAK Narayan1Research Scholar; MPSTME NMIMS (Deemed to be University), Mumbai, IndiaAssistant Prof.; Department of Civil Engineering, SVKMs Institute of Technology, Dhule, IndiaProf.; Department of Civil Engineering, SVKMs Institute of Technology, Dhule, IndiaThe present study utilizes advanced numerical evaluation techniques like Artificial Intelligence (AI), including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems with Genetic Algorithms (ANFIS-GA), Gene Expression Programming (GEP), and Multiple Linear Regression (MLR) to develop and compare the predictive models for determination of compressive and tensile strength. Partial mutual information for selection and establishment of the degree of association of variables was used to aid in better attainment of results obtained through predictive models. It was observed that amongst the modeling techniques, the results obtained for compressive strength through the SVM technique were excellent, producing an Index of Agreement of 0.96, Akaike Information Criterion of 68.33, skill score of 0.96, and symmetric uncertainty of 0.93, thus indicating a simpler, robust, and low uncertainty predictive model. Furthermore, the adapted technique MLR was found to predict tensile strength characteristics better, with the MLR model demonstrating a higher R2 value of 0.81, thus implying a reliable tensile strength prediction model. However, SVM consistently performed well for both compressive and tensile strength characteristics thus endorsing the reliability of the predictive model. Overall, the study aids in getting new insights about improvising the strength properties of SCC and its evaluation through predictive techniques.https://doi.org/10.2478/acee-2024-0014artificial intelligencecompressive strengthsupport vector machinesself-compacting concretetensile strength |
| spellingShingle | AGRAWAL Achal CHANDAK Narayan Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete Architecture, Civil Engineering, Environment artificial intelligence compressive strength support vector machines self-compacting concrete tensile strength |
| title | Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete |
| title_full | Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete |
| title_fullStr | Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete |
| title_full_unstemmed | Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete |
| title_short | Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete |
| title_sort | advanced ai tools for predicting mechanical properties of self compacting concrete |
| topic | artificial intelligence compressive strength support vector machines self-compacting concrete tensile strength |
| url | https://doi.org/10.2478/acee-2024-0014 |
| work_keys_str_mv | AT agrawalachal advancedaitoolsforpredictingmechanicalpropertiesofselfcompactingconcrete AT chandaknarayan advancedaitoolsforpredictingmechanicalpropertiesofselfcompactingconcrete |