Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm
This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The deve...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1944/14/11/3049 |
| _version_ | 1797531422340153344 |
|---|---|
| author | Khaled A. Alawi Al-Sodani Adeshina Adewale Adewumi Mohd Azreen Mohd Ariffin Mohammed Maslehuddin Mohammad Ismail Hamza Onoruoiza Salami Taoreed O. Owolabi Hatim Dafalla Mohamed |
| author_facet | Khaled A. Alawi Al-Sodani Adeshina Adewale Adewumi Mohd Azreen Mohd Ariffin Mohammed Maslehuddin Mohammad Ismail Hamza Onoruoiza Salami Taoreed O. Owolabi Hatim Dafalla Mohamed |
| author_sort | Khaled A. Alawi Al-Sodani |
| collection | DOAJ |
| description | This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete. |
| first_indexed | 2024-03-10T10:44:31Z |
| format | Article |
| id | doaj.art-0bb0ec1dda5d4aae830e52601ccacbf6 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T10:44:31Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-0bb0ec1dda5d4aae830e52601ccacbf62023-11-21T22:41:55ZengMDPI AGMaterials1996-19442021-06-011411304910.3390/ma14113049Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic AlgorithmKhaled A. Alawi Al-Sodani0Adeshina Adewale Adewumi1Mohd Azreen Mohd Ariffin2Mohammed Maslehuddin3Mohammad Ismail4Hamza Onoruoiza Salami5Taoreed O. Owolabi6Hatim Dafalla Mohamed7Department of Civil Engineering, University of Hafr Al Batin, Hafar Al-Batin 31991, Saudi ArabiaDepartment of Civil Engineering, University of Hafr Al Batin, Hafar Al-Batin 31991, Saudi ArabiaSchool of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru 81310, MalaysiaIntegrated Center for Research on Construction and Building Materials, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaSchool of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru 81310, MalaysiaCollege of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al-Batin 31991, Saudi ArabiaPhysics and Electronics Department, Adekunle Ajasin University, Akungba Akoko 341112, Ondo State, NigeriaCore Research Facilities, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaThis paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete.https://www.mdpi.com/1996-1944/14/11/3049compressive strengthnatural pozzolangenetic algorithmlimestone powdersupport vector regression |
| spellingShingle | Khaled A. Alawi Al-Sodani Adeshina Adewale Adewumi Mohd Azreen Mohd Ariffin Mohammed Maslehuddin Mohammad Ismail Hamza Onoruoiza Salami Taoreed O. Owolabi Hatim Dafalla Mohamed Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm Materials compressive strength natural pozzolan genetic algorithm limestone powder support vector regression |
| title | Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm |
| title_full | Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm |
| title_fullStr | Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm |
| title_full_unstemmed | Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm |
| title_short | Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm |
| title_sort | experimental and modelling of alkali activated mortar compressive strength using hybrid support vector regression and genetic algorithm |
| topic | compressive strength natural pozzolan genetic algorithm limestone powder support vector regression |
| url | https://www.mdpi.com/1996-1944/14/11/3049 |
| work_keys_str_mv | AT khaledaalawialsodani experimentalandmodellingofalkaliactivatedmortarcompressivestrengthusinghybridsupportvectorregressionandgeneticalgorithm AT adeshinaadewaleadewumi experimentalandmodellingofalkaliactivatedmortarcompressivestrengthusinghybridsupportvectorregressionandgeneticalgorithm AT mohdazreenmohdariffin experimentalandmodellingofalkaliactivatedmortarcompressivestrengthusinghybridsupportvectorregressionandgeneticalgorithm AT mohammedmaslehuddin experimentalandmodellingofalkaliactivatedmortarcompressivestrengthusinghybridsupportvectorregressionandgeneticalgorithm AT mohammadismail experimentalandmodellingofalkaliactivatedmortarcompressivestrengthusinghybridsupportvectorregressionandgeneticalgorithm AT hamzaonoruoizasalami experimentalandmodellingofalkaliactivatedmortarcompressivestrengthusinghybridsupportvectorregressionandgeneticalgorithm AT taoreedoowolabi experimentalandmodellingofalkaliactivatedmortarcompressivestrengthusinghybridsupportvectorregressionandgeneticalgorithm AT hatimdafallamohamed experimentalandmodellingofalkaliactivatedmortarcompressivestrengthusinghybridsupportvectorregressionandgeneticalgorithm |