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A platform for research: civil engineering, architecture and urbanism
Soft computing in estimating the compressive strength for high-performance concrete via concrete composition appraisal
https://orcid.org/0000-0002-6846-3154
https://orcid.org/0000-0002-7241-3483
Highlights The BooST algorithm is adopted to predict concrete compressive strength performance. Concrete composition appraisal for high-performance is studied. Different algorithms are compared to depict predictive accuracy of proposed models. Sensitivity analyses accounting for relationships of CCS and composition are studied.
Abstract This study investigates the predictive performance of Concrete Compressive Strength (CCS) for high-performance, based on concrete mixture constituents and proportioning. A new ensemble computational technique – Boosting Smooth Transition regression trees (BooST), is adopted and compared with other contemporary methods for higher predictive accuracy and analyses. With variations in CCS performances due to complexities in concrete compositions, ten unique models are created and divided into three sets from which several analytic techniques are employed to predict CCS at 28 days for high-performance. The results showed that BooST dominance in prediction accuracy over the other methods with minimal errors and better fit to experimental laboratory results.
Soft computing in estimating the compressive strength for high-performance concrete via concrete composition appraisal
https://orcid.org/0000-0002-6846-3154
https://orcid.org/0000-0002-7241-3483
Highlights The BooST algorithm is adopted to predict concrete compressive strength performance. Concrete composition appraisal for high-performance is studied. Different algorithms are compared to depict predictive accuracy of proposed models. Sensitivity analyses accounting for relationships of CCS and composition are studied.
Abstract This study investigates the predictive performance of Concrete Compressive Strength (CCS) for high-performance, based on concrete mixture constituents and proportioning. A new ensemble computational technique – Boosting Smooth Transition regression trees (BooST), is adopted and compared with other contemporary methods for higher predictive accuracy and analyses. With variations in CCS performances due to complexities in concrete compositions, ten unique models are created and divided into three sets from which several analytic techniques are employed to predict CCS at 28 days for high-performance. The results showed that BooST dominance in prediction accuracy over the other methods with minimal errors and better fit to experimental laboratory results.
Soft computing in estimating the compressive strength for high-performance concrete via concrete composition appraisal
https://orcid.org/0000-0002-6846-3154
https://orcid.org/0000-0002-7241-3483
Highlights The BooST algorithm is adopted to predict concrete compressive strength performance. Concrete composition appraisal for high-performance is studied. Different algorithms are compared to depict predictive accuracy of proposed models. Sensitivity analyses accounting for relationships of CCS and composition are studied.
Abstract This study investigates the predictive performance of Concrete Compressive Strength (CCS) for high-performance, based on concrete mixture constituents and proportioning. A new ensemble computational technique – Boosting Smooth Transition regression trees (BooST), is adopted and compared with other contemporary methods for higher predictive accuracy and analyses. With variations in CCS performances due to complexities in concrete compositions, ten unique models are created and divided into three sets from which several analytic techniques are employed to predict CCS at 28 days for high-performance. The results showed that BooST dominance in prediction accuracy over the other methods with minimal errors and better fit to experimental laboratory results.
Soft computing in estimating the compressive strength for high-performance concrete via concrete composition appraisal
Anyaoha, Uchenna (author) / Zaji, Amirhossein (author) / Liu, Zheng (author)
2020-05-06
Article (Journal)
Electronic Resource
English
Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model
| DOAJ | 2016
CONCRETE COMPOSITION, AND COMPRESSIVE STRENGTH ENHANCING METHOD OF CONCRETE COMPOSITION
| European Patent Office | 2023
Composition and compressive strength of concrete
| Engineering Index Backfile | 1927
Testing High-Strength Concrete Compressive Strength
| Online Contents | 1993