Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete
The construction sector would greatly benefit from a strategy for optimizing high-performance concrete mixtures. However, traditional proportioning techniques are insufficient because of their high prices, usage restrictions, and inability to account for nonlinear interactions between components and...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
|
| Series: | Applications in Engineering Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666496823000080 |
| _version_ | 1797672051154092032 |
|---|---|
| author | M.M Jibril M.A Zayyan Salim Idris Malami A.G. Usman Babatunde A. Salami Abdulazeez Rotimi S.I. Abba |
| author_facet | M.M Jibril M.A Zayyan Salim Idris Malami A.G. Usman Babatunde A. Salami Abdulazeez Rotimi S.I. Abba |
| author_sort | M.M Jibril |
| collection | DOAJ |
| description | The construction sector would greatly benefit from a strategy for optimizing high-performance concrete mixtures. However, traditional proportioning techniques are insufficient because of their high prices, usage restrictions, and inability to account for nonlinear interactions between components and concrete qualities. High-performance concrete (HPC) is a complicated composite material with highly nonlinear mechanical behaviour. When strength can be accurately predicted, design costs, design time, and material waste caused by several mixing trials can all be reduced. In this research, feed-forward neural network (FFNN), Elman neural network (ENN), support vector machine (SVM) and multilinear regression (MLR) were employed for predicting the compressive strength of HPC. The input variables include cement (C), cement strength (CeS), superplasticizer (S), fly ash (F), air entraining agent (A), coarse aggregate (CA), Sand (Sd) and water/binder (W/B) and 28 days’ compressive strength as the output variables. Finally, the results indicate that the proposed model has predictive robustness for predicting the compressive strength of HPC. The results showed that FFNN-M4, ENN-M4, SVM-M4, and MLR-M4 combination have the highest performance evaluation criteria of R2=0.9950, R2=0.9853, R2=0.9736, R2= 0.9678 in the testing phase respectively. The outcomes also show that the proposed model has high accuracy and effectiveness in predicting the compressive strength of HPC. |
| first_indexed | 2024-03-11T21:24:27Z |
| format | Article |
| id | doaj.art-faf3e258cf824478a9b909a9889b0e1c |
| institution | Directory Open Access Journal |
| issn | 2666-4968 |
| language | English |
| last_indexed | 2024-03-11T21:24:27Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applications in Engineering Science |
| spelling | doaj.art-faf3e258cf824478a9b909a9889b0e1c2023-09-28T05:26:29ZengElsevierApplications in Engineering Science2666-49682023-09-0115100133Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concreteM.M Jibril0M.A Zayyan1Salim Idris Malami2A.G. Usman3Babatunde A. Salami4Abdulazeez Rotimi5S.I. Abba6Faculty of Engineering, Department of Civil Engineering, Kano University of Science and Technology, KUST, Wudil, Nigeria; Corresponding authors.Department of Civil Engineering, Federal University DutsinMa, Katsina State, NigeriaFaculty of Engineering, Department of Civil Engineering, Kano University of Science and Technology, KUST, Wudil, Nigeria; School of Energy, Geoscience, Infrastructure and Society, Institute for Sustainable Built Environment, Heriot-Watt University, Edinburgh, United KingdomOperational Research Center in Healthcare, Near East University, Nicosia, Turkish Republic of Northern Cyprus; Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, Nicosia, Turkish Republic of Northern CyprusSchool of Computing, Engineering & Digital Technologies, Teesside University, Tees Valley, Middlesbrough, TS1 3BX, UKDepartment of Civil Engineering, Baze University Abuja, NigeriaInterdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; Corresponding authors.The construction sector would greatly benefit from a strategy for optimizing high-performance concrete mixtures. However, traditional proportioning techniques are insufficient because of their high prices, usage restrictions, and inability to account for nonlinear interactions between components and concrete qualities. High-performance concrete (HPC) is a complicated composite material with highly nonlinear mechanical behaviour. When strength can be accurately predicted, design costs, design time, and material waste caused by several mixing trials can all be reduced. In this research, feed-forward neural network (FFNN), Elman neural network (ENN), support vector machine (SVM) and multilinear regression (MLR) were employed for predicting the compressive strength of HPC. The input variables include cement (C), cement strength (CeS), superplasticizer (S), fly ash (F), air entraining agent (A), coarse aggregate (CA), Sand (Sd) and water/binder (W/B) and 28 days’ compressive strength as the output variables. Finally, the results indicate that the proposed model has predictive robustness for predicting the compressive strength of HPC. The results showed that FFNN-M4, ENN-M4, SVM-M4, and MLR-M4 combination have the highest performance evaluation criteria of R2=0.9950, R2=0.9853, R2=0.9736, R2= 0.9678 in the testing phase respectively. The outcomes also show that the proposed model has high accuracy and effectiveness in predicting the compressive strength of HPC.http://www.sciencedirect.com/science/article/pii/S2666496823000080High-performance concreteFeedforward neural networkElman neural networkSupport vector machineMultilinear regression |
| spellingShingle | M.M Jibril M.A Zayyan Salim Idris Malami A.G. Usman Babatunde A. Salami Abdulazeez Rotimi S.I. Abba Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete Applications in Engineering Science High-performance concrete Feedforward neural network Elman neural network Support vector machine Multilinear regression |
| title | Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete |
| title_full | Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete |
| title_fullStr | Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete |
| title_full_unstemmed | Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete |
| title_short | Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete |
| title_sort | implementation of nonlinear computing models and classical regression for predicting compressive strength of high performance concrete |
| topic | High-performance concrete Feedforward neural network Elman neural network Support vector machine Multilinear regression |
| url | http://www.sciencedirect.com/science/article/pii/S2666496823000080 |
| work_keys_str_mv | AT mmjibril implementationofnonlinearcomputingmodelsandclassicalregressionforpredictingcompressivestrengthofhighperformanceconcrete AT mazayyan implementationofnonlinearcomputingmodelsandclassicalregressionforpredictingcompressivestrengthofhighperformanceconcrete AT salimidrismalami implementationofnonlinearcomputingmodelsandclassicalregressionforpredictingcompressivestrengthofhighperformanceconcrete AT agusman implementationofnonlinearcomputingmodelsandclassicalregressionforpredictingcompressivestrengthofhighperformanceconcrete AT babatundeasalami implementationofnonlinearcomputingmodelsandclassicalregressionforpredictingcompressivestrengthofhighperformanceconcrete AT abdulazeezrotimi implementationofnonlinearcomputingmodelsandclassicalregressionforpredictingcompressivestrengthofhighperformanceconcrete AT siabba implementationofnonlinearcomputingmodelsandclassicalregressionforpredictingcompressivestrengthofhighperformanceconcrete |