Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches
The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) t...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/12/7/914 |
| _version_ | 1797407178273849344 |
|---|---|
| author | Syyed Adnan Raheel Shah Marc Azab Hany M. Seif ElDin Osama Barakat Muhammad Kashif Anwar Yasir Bashir |
| author_facet | Syyed Adnan Raheel Shah Marc Azab Hany M. Seif ElDin Osama Barakat Muhammad Kashif Anwar Yasir Bashir |
| author_sort | Syyed Adnan Raheel Shah |
| collection | DOAJ |
| description | The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R<sup>2</sup>, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R<sup>2</sup> values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings. |
| first_indexed | 2024-03-09T03:37:28Z |
| format | Article |
| id | doaj.art-649d505dd1634046a56334d34f84b274 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T03:37:28Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-649d505dd1634046a56334d34f84b2742023-12-03T14:45:56ZengMDPI AGBuildings2075-53092022-06-0112791410.3390/buildings12070914Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making ApproachesSyyed Adnan Raheel Shah0Marc Azab1Hany M. Seif ElDin2Osama Barakat3Muhammad Kashif Anwar4Yasir Bashir5Department of Civil Engineering, Pakistan Institute of Engineering and Technology, Multan 66000, PakistanCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitDepartment of Civil Engineering, Pakistan Institute of Engineering and Technology, Multan 66000, PakistanNorth Region, Punjab Aab-e-Pak Authority, Lahore 54660, PakistanThe utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R<sup>2</sup>, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R<sup>2</sup> values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.https://www.mdpi.com/2075-5309/12/7/914sustainabilityreinforced concreteindustrial wastescompressive strengthgreen and sustainable concretehigh performance concrete |
| spellingShingle | Syyed Adnan Raheel Shah Marc Azab Hany M. Seif ElDin Osama Barakat Muhammad Kashif Anwar Yasir Bashir Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches Buildings sustainability reinforced concrete industrial wastes compressive strength green and sustainable concrete high performance concrete |
| title | Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches |
| title_full | Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches |
| title_fullStr | Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches |
| title_full_unstemmed | Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches |
| title_short | Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches |
| title_sort | predicting compressive strength of blast furnace slag and fly ash based sustainable concrete using machine learning techniques an application of advanced decision making approaches |
| topic | sustainability reinforced concrete industrial wastes compressive strength green and sustainable concrete high performance concrete |
| url | https://www.mdpi.com/2075-5309/12/7/914 |
| work_keys_str_mv | AT syyedadnanraheelshah predictingcompressivestrengthofblastfurnaceslagandflyashbasedsustainableconcreteusingmachinelearningtechniquesanapplicationofadvanceddecisionmakingapproaches AT marcazab predictingcompressivestrengthofblastfurnaceslagandflyashbasedsustainableconcreteusingmachinelearningtechniquesanapplicationofadvanceddecisionmakingapproaches AT hanymseifeldin predictingcompressivestrengthofblastfurnaceslagandflyashbasedsustainableconcreteusingmachinelearningtechniquesanapplicationofadvanceddecisionmakingapproaches AT osamabarakat predictingcompressivestrengthofblastfurnaceslagandflyashbasedsustainableconcreteusingmachinelearningtechniquesanapplicationofadvanceddecisionmakingapproaches AT muhammadkashifanwar predictingcompressivestrengthofblastfurnaceslagandflyashbasedsustainableconcreteusingmachinelearningtechniquesanapplicationofadvanceddecisionmakingapproaches AT yasirbashir predictingcompressivestrengthofblastfurnaceslagandflyashbasedsustainableconcreteusingmachinelearningtechniquesanapplicationofadvanceddecisionmakingapproaches |