Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete
Concrete made with additives like slag and fly ash has revolutionized construction by reducing carbon emissions, minimizing waste, lowering labor costs, and enhancing durability and accuracy. Predicting the compressive strength (CS) is vital for achieving optimal performance. Given the nonlinear cha...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Hybrid Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773207X25000284 |
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| author | Abba Bashir Esar Ahmad Shashivendra Dulawat Sani I. Abba |
| author_facet | Abba Bashir Esar Ahmad Shashivendra Dulawat Sani I. Abba |
| author_sort | Abba Bashir |
| collection | DOAJ |
| description | Concrete made with additives like slag and fly ash has revolutionized construction by reducing carbon emissions, minimizing waste, lowering labor costs, and enhancing durability and accuracy. Predicting the compressive strength (CS) is vital for achieving optimal performance. Given the nonlinear characteristics of supplementary cement material concrete (SCMC) mixtures, researchers are increasingly turning to machine learning methods. This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). By addressing issues related to mechanical property variation, dataset coverage, and model evaluation, the study achieved high prediction accuracy across all nine models. The RF model optimized with CSO, MFO, and WOA consistently performed well across various metrics having R2 = 0.98, RMSE = 0.03 during training and R2 = 0.87 and RMSE = 0.07 during testing. The visual evidence highlights several advantages, including superior quality control, cost savings, increased safety, and environmental sustainability, which underscore the effectiveness of these models. In addition, feature analysis was performed using SHAP analysis, age and cement are identified as the dominant inputs exacting influence on the CS of SCMC. |
| format | Article |
| id | doaj-art-5bd583e8597d496fabeeeb9656419903 |
| institution | Kabale University |
| issn | 2773-207X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Hybrid Advances |
| spelling | doaj-art-5bd583e8597d496fabeeeb96564199032025-02-07T04:48:35ZengElsevierHybrid Advances2773-207X2025-06-019100404Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concreteAbba Bashir0Esar Ahmad1Shashivendra Dulawat2Sani I. Abba3Department of Civil Engineering, Federal University Dutsin-ma, Katsina state, Nigeria; Department of Civil Engineering, Mewar University, Chittorgarh, Rajasthan, India; Corresponding author. Department of Civil Engineering, Federal University Dutsin-ma, Katsina state, Nigeria.Department of Civil Engineering, Mewar University, Chittorgarh, Rajasthan, IndiaDepartment of Civil Engineering, Amity University, Gwalior, Madhya Pradesh, IndiaDepartment of Civil Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia; Corresponding author.Concrete made with additives like slag and fly ash has revolutionized construction by reducing carbon emissions, minimizing waste, lowering labor costs, and enhancing durability and accuracy. Predicting the compressive strength (CS) is vital for achieving optimal performance. Given the nonlinear characteristics of supplementary cement material concrete (SCMC) mixtures, researchers are increasingly turning to machine learning methods. This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). By addressing issues related to mechanical property variation, dataset coverage, and model evaluation, the study achieved high prediction accuracy across all nine models. The RF model optimized with CSO, MFO, and WOA consistently performed well across various metrics having R2 = 0.98, RMSE = 0.03 during training and R2 = 0.87 and RMSE = 0.07 during testing. The visual evidence highlights several advantages, including superior quality control, cost savings, increased safety, and environmental sustainability, which underscore the effectiveness of these models. In addition, feature analysis was performed using SHAP analysis, age and cement are identified as the dominant inputs exacting influence on the CS of SCMC.http://www.sciencedirect.com/science/article/pii/S2773207X25000284Supplementary cementitious materialChicken swarm optimizationMoth flame optimizationWhale optimizationArtificial neural networkSupport vector regression |
| spellingShingle | Abba Bashir Esar Ahmad Shashivendra Dulawat Sani I. Abba Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete Hybrid Advances Supplementary cementitious material Chicken swarm optimization Moth flame optimization Whale optimization Artificial neural network Support vector regression |
| title | Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete |
| title_full | Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete |
| title_fullStr | Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete |
| title_full_unstemmed | Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete |
| title_short | Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete |
| title_sort | harnessing synergy of machine learning and nature inspired optimization for enhanced compressive strength prediction in concrete |
| topic | Supplementary cementitious material Chicken swarm optimization Moth flame optimization Whale optimization Artificial neural network Support vector regression |
| url | http://www.sciencedirect.com/science/article/pii/S2773207X25000284 |
| work_keys_str_mv | AT abbabashir harnessingsynergyofmachinelearningandnatureinspiredoptimizationforenhancedcompressivestrengthpredictioninconcrete AT esarahmad harnessingsynergyofmachinelearningandnatureinspiredoptimizationforenhancedcompressivestrengthpredictioninconcrete AT shashivendradulawat harnessingsynergyofmachinelearningandnatureinspiredoptimizationforenhancedcompressivestrengthpredictioninconcrete AT saniiabba harnessingsynergyofmachinelearningandnatureinspiredoptimizationforenhancedcompressivestrengthpredictioninconcrete |