A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash
Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (A...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1944/14/17/4934 |
| _version_ | 1797521158348734464 |
|---|---|
| author | Furqan Farooq Slawomir Czarnecki Pawel Niewiadomski Fahid Aslam Hisham Alabduljabbar Krzysztof Adam Ostrowski Klaudia Śliwa-Wieczorek Tomasz Nowobilski Seweryn Malazdrewicz |
| author_facet | Furqan Farooq Slawomir Czarnecki Pawel Niewiadomski Fahid Aslam Hisham Alabduljabbar Krzysztof Adam Ostrowski Klaudia Śliwa-Wieczorek Tomasz Nowobilski Seweryn Malazdrewicz |
| author_sort | Furqan Farooq |
| collection | DOAJ |
| description | Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (<i>R</i><sup>2</sup>) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect. |
| first_indexed | 2024-03-10T08:07:39Z |
| format | Article |
| id | doaj.art-3caea164e0a94866ab48ba2826b04c37 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T08:07:39Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-3caea164e0a94866ab48ba2826b04c372023-11-22T10:53:50ZengMDPI AGMaterials1996-19442021-08-011417493410.3390/ma14174934A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly AshFurqan Farooq0Slawomir Czarnecki1Pawel Niewiadomski2Fahid Aslam3Hisham Alabduljabbar4Krzysztof Adam Ostrowski5Klaudia Śliwa-Wieczorek6Tomasz Nowobilski7Seweryn Malazdrewicz8Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Materials Engineering and Construction Processes, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, PolandDepartment of Materials Engineering and Construction Processes, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, PolandDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaFaculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, PolandFaculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, PolandDepartment of Materials Engineering and Construction Processes, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, PolandDepartment of Materials Engineering and Construction Processes, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, PolandArtificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (<i>R</i><sup>2</sup>) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect.https://www.mdpi.com/1996-1944/14/17/4934self-compacting concretefly ashmachine learningartificial neural networkgene engineering programming |
| spellingShingle | Furqan Farooq Slawomir Czarnecki Pawel Niewiadomski Fahid Aslam Hisham Alabduljabbar Krzysztof Adam Ostrowski Klaudia Śliwa-Wieczorek Tomasz Nowobilski Seweryn Malazdrewicz A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash Materials self-compacting concrete fly ash machine learning artificial neural network gene engineering programming |
| title | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
| title_full | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
| title_fullStr | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
| title_full_unstemmed | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
| title_short | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
| title_sort | comparative study for the prediction of the compressive strength of self compacting concrete modified with fly ash |
| topic | self-compacting concrete fly ash machine learning artificial neural network gene engineering programming |
| url | https://www.mdpi.com/1996-1944/14/17/4934 |
| work_keys_str_mv | AT furqanfarooq acomparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT slawomirczarnecki acomparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT pawelniewiadomski acomparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT fahidaslam acomparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT hishamalabduljabbar acomparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT krzysztofadamostrowski acomparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT klaudiasliwawieczorek acomparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT tomasznowobilski acomparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT sewerynmalazdrewicz acomparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT furqanfarooq comparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT slawomirczarnecki comparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT pawelniewiadomski comparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT fahidaslam comparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT hishamalabduljabbar comparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT krzysztofadamostrowski comparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT klaudiasliwawieczorek comparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT tomasznowobilski comparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash AT sewerynmalazdrewicz comparativestudyforthepredictionofthecompressivestrengthofselfcompactingconcretemodifiedwithflyash |