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Javed MF, Khan M, Fawad M, Alabduljabbar H, Najeh T, Gamil Y. Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand. Sci Rep 2024; 14:14617. [PMID: 38918460 PMCID: PMC11199582 DOI: 10.1038/s41598-024-65255-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 06/18/2024] [Indexed: 06/27/2024] Open
Abstract
The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.
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Affiliation(s)
- Muhammad Faisal Javed
- Department of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Swabi, 23640, Pakistan
- Western Caspian University, Baku, Azerbaijan
| | - Majid Khan
- Civil Engineering Department, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan.
| | - Muhammad Fawad
- Silesian University of Technology, Gliwice, Poland
- Budapest University of Technology and Economics Hungary, Budapest, Hungary
| | - Hisham Alabduljabbar
- Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Taoufik Najeh
- Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Lulea, Sweden.
| | - Yaser Gamil
- Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, 47500, Malaysia
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Asghar M, Javed MF, Khan MI, Abdullaev S, Awwad FA, Ismail EAA. Empirical models for compressive and tensile strength of basalt fiber reinforced concrete. Sci Rep 2023; 13:19909. [PMID: 37964000 PMCID: PMC10646001 DOI: 10.1038/s41598-023-47330-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/12/2023] [Indexed: 11/16/2023] Open
Abstract
When molten magma solidifies, basalt fiber (BF) is produced as a byproduct. Due to its remaining pollutants that could affect the environment, it is regarded as a waste product. To determine the compressive strength (CS) and tensile strength (TS) of basalt fiber reinforced concrete (BFRC), this study will develop empirical models using gene expression programming (GEP), Artificial Neural Network (ANN) and Extreme Gradient Boosting (XG Boost). A thorough search of the literature was done to compile a variety of information on the CS and TS of BFRC. 153 CS findings and 127 TS outcomes were included in the review. The water-to-cement, BF, fiber length (FL), and coarse aggregates ratios were the influential characteristics found. The outcomes showed that GEP can accurately forecast the CS and TS of BFRC as compared to ANN and XG Boost. Efficiency of GEP was validated by comparing Regression (R2) value of all three models. It was shown that the CS and TS of BFRC increased initially up to a certain limit and then started decreasing as the BF % and FL increased. The ideal BF content for industrial-scale BF reinforcement of concrete was investigated in this study which could be an economical solution for production of BFRC on industrial scale.
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Affiliation(s)
- Muhammad Asghar
- Department of Geotechnical Engineering, NICE, National University of Science and Technology, Islamabad, Pakistan
| | - Muhammad Faisal Javed
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Islamabad, Pakistan
| | - M Ijaz Khan
- Department of Mechanical Engineering, Lebanese American University, Beirut, Lebanon.
- Department of Mathematics and Statistics, Riphah International University I-14, Islamabad, 44000, Pakistan.
- Department of Mechanics and Engineering Science, Peking University, Beijing 100871, China.
| | - Sherzod Abdullaev
- Faculty of Chemical Engineering, New Uzbekistan University, Tashkent, Uzbekistan
- Department of Science and Innovation, Tashkent State Pedagogical University Named After Nizami, Bunyodkor Street 27, Tashkent, Uzbekistan
| | - Fuad A Awwad
- Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, 11587, Riyadh, Saudi Arabia
| | - Emad A A Ismail
- Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, 11587, Riyadh, Saudi Arabia
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Al Noman A, Tasneem Z, Abhi SH, Badal FR, Rafsanzane M, Islam MR, Alam F. Savonius wind turbine blade design and performance evaluation using ANN-based virtual clone: A new approach. Heliyon 2023; 9:e15672. [PMID: 37180909 PMCID: PMC10173602 DOI: 10.1016/j.heliyon.2023.e15672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/10/2023] [Accepted: 04/18/2023] [Indexed: 05/16/2023] Open
Abstract
The drag based Savonius wind turbine (SWT) has shown immense potential for renewable power generation in built-up areas under complex urban wind conditions. While a series of studies have been conducted on improving SWT's efficiency, optimal performance has yet to be achieved using traditional design approaches such as experimental and/or computational fluid dynamics methods. Recently, artificial intelligence and machine learning have been widely used in design optimization. As such, an ANN-based virtual clone can be an alternative to traditional design methods for wind turbine performance determination. Therefore, the main goal of this study is to investigate whether ANN-based virtual clones are capable of determining the performance of SWTs with a shorter timeframe and minimal resources compared to traditional methods. To achieve the objective, an ANN-based virtual clone model is developed. Two sets of data (computational and experimental) are used to validate and determine the efficacy of the proposed ANN-based virtual clone model. Using experimental data, the model's fidelity is over 98%. The proposed model produces results in one-fifth the time of the existing simulation (based on the combined ANN + GA metamodel) method. The model also reveals the location of the dataset's optimized point for augmenting the turbine's performance.
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Affiliation(s)
- Abdullah Al Noman
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Zinat Tasneem
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Sarafat Hussain Abhi
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Faisal R. Badal
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Rafsanzane
- Department of Mechanical Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Robiul Islam
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Firoz Alam
- School of Engineering (Aerospace, Mechanical and Manufacturing), RMIT University, Melbourne, Australia
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Numerical Analysis of Shallow Foundations with Varying Loading and Soil Conditions. BUILDINGS 2022. [DOI: 10.3390/buildings12050693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The load–deformation relationship under the footing is essential for foundation design. Shallow foundations are subjected to changes in hydrological conditions such as rainfall and drought, affecting their saturation level and conditions. The actual load–settlement response for design and reconstructions is determined experimentally, numerically, or utilizing both approaches. Ssettlement computation is performed through large-scale physical modeling or extensive laboratory testing. It is expensive, labor intensive, and time consuming. This study is carried out to determine the effect of different saturation degrees and loading conditions on settlement shallow foundations using numerical modeling in Plaxis 2D, Bentley Systems, Exton, Pennsylvania, US. Plastic was used for dry soil calculation, while fully coupled flow deformation was used for partially saturated soil. Pore pressure and deformation changes were computed in fully coupled deformation. The Mohr–Columb model was used in the simulation, and model parameters were calculated from experimental results. The study results show that the degree of saturation is more critical to soil settlement than loading conditions. When a 200 KPa load was applied at the center of the footing, settlement was recored as 28.81 mm, which was less than 42.96 mm in the case of the full-depth shale layer; therefore, settlement was reduced by 30% in the underlying limestone rock layer. Regarding settlement under various degrees of saturation (DOS), settlment is increased by an increased degree of saturation, which increases pore pressure and decreases the shear strength of the soil. Settlement was observed as 0.69 mm at 0% saturation, 1.93 mm at 40% saturation, 2.21 mm at 50% saturation, 2.77 mm at 70% saturation, and 2.84 mm at 90% saturation of soil.
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Comparative Analysis of Machine Learning and Numerical Modeling for Combined Heat Transfer in Polymethylmethacrylate. Polymers (Basel) 2022; 14:polym14101996. [PMID: 35631878 PMCID: PMC9144265 DOI: 10.3390/polym14101996] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/27/2022] [Accepted: 05/10/2022] [Indexed: 12/10/2022] Open
Abstract
This study has compared different methods to predict the simultaneous effects of conductive and radiative heat transfer in a polymethylmethacrylate (PMMA) sample. PMMA is a type of polymer utilized in various sensors and actuator devices. One-dimensional combined heat transfer is considered in numerical analysis. Computer implementation was obtained for the numerical solution of the governing equation with the implicit finite difference method in the case of discretization. Kirchhoff transformation was used to obtain data from a non-linear equation of conductive heat transfer by considering monochromatic radiation intensity and temperature conditions applied to the PMMA sample boundaries. For the deep neural network (DNN) method, the novel long short-term memory (LSTM) method was introduced to find accurate results in the least processing time compared to the numerical method. A recent study derived the combined heat transfer and temperature profiles for the PMMA sample. Furthermore, the transient temperature profile was validated by another study. A comparison proves the perfect agreement. It shows the temperature gradient in the primary positions, which provides a spectral amount of conductive heat transfer from the PMMA sample. It is more straightforward when they are compared with the novel DNN method. Results demonstrate that this artificial intelligence method is accurate and fast in predicting problems. By analyzing the results from the numerical solution, it can be understood that the conductive and radiative heat flux are similar in the case of gradient behavior, but the amount is also twice as high approximately. Hence, total heat flux has a constant value in an approximated steady-state condition. In addition to analyzing their composition, the receiver operating characteristic (ROC) curve and confusion matrix were implemented to evaluate the algorithm’s performance.
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Asghar R, Javed MF, Alrowais R, Khalil A, Mohamed AM, Mohamed A, Vatin NI. Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming. MATERIALS 2022; 15:ma15072673. [PMID: 35408010 PMCID: PMC9000259 DOI: 10.3390/ma15072673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 02/05/2023]
Abstract
This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.
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Affiliation(s)
- Raheel Asghar
- Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan;
| | - Muhammad Faisal Javed
- Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan;
- Correspondence:
| | - Raid Alrowais
- Department of Civil Engineering, Jouf University, Sakaka, Al-Jouf 72388, Saudi Arabia;
| | - Alamgir Khalil
- Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan;
| | - Abdeliazim Mustafa Mohamed
- Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj 16273, Saudi Arabia;
- Building and Construction Technology Department, Bayan College of Science and Technology, Khartoum 210, Sudan
| | - Abdullah Mohamed
- Research Centre, Future University in Egypt, New Cairo 11835, Egypt;
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Numerical Analysis of Piled-Raft Foundations on Multi-Layer Soil Considering Settlement and Swelling. BUILDINGS 2022. [DOI: 10.3390/buildings12030356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Numerical modelling can simulate the interaction between structural elements and the soil continuum in a piled-raft foundation. The present work utilized a two-dimensional finite element Plaxis 2D software to investigate the settlement, swelling, and structural behavior of foundations during the settlement and swelling of soil on various soil profiles under various load combinations and geometry conditions. The field and laboratory testing have been performed to determine the behavior soil parameters necessary for numerical modelling. The Mohr–Coulomb model is utilized to simulate the behavior of soil, as this model requires very few input parameters, which is important for the practical geotechnical behavior of soil. From this study, it was observed that, as soil is soft and has less stiffness, the un-piled raft was not sufficient to resists and higher loads and exceeds the limits of settlement. Piled raft increases the load carrying capacity of soil, and the lower soil layer has a higher stiffness where the pile rests, decreasing the significant settlement. Further, the effects of (L/d) and (s/d) of the pile and Krs on the settlement are also discussed, detailed numerically under different scenarios. The swelling of expansive soil was also simulated in Plaxis 2D with an application of positive volumetric strain. The above-mentioned parametric study was similarly implemented for the heaving of foundation on expansive soil.
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