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Khan Y, Zafar A, Rehman MF, Javed MF, Iftikhar B, Gamil Y. Bio-inspired based meta-heuristic approach for predicting the strength of fiber-reinforced based strain hardening cementitious composites. Heliyon 2023; 9:e21601. [PMID: 38027981 PMCID: PMC10665749 DOI: 10.1016/j.heliyon.2023.e21601] [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: 01/16/2023] [Revised: 09/27/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
A recently introduced bendable concrete having hundred times greater strain capacity provides promising results in repair of engineering structures, known as strain hardening cementitious composites (SHHCs). The current research creates new empirical prediction models to assess the mechanical properties of strain-hardening cementitious composites (SHCCs) i.e., compressive strength (CS), first crack tensile stress (TS), and first crack flexural stress (FS), using gene expression programming (GEP). Wide-ranging records were considered with twelve variables i.e., cement percentage by weight (C%), fine aggregate percentage by weight (Fagg%), fly-ash percentage by weight (FA%), Water-to-binder ratio (W/B), super-plasticizer percentage by weight (SP%), fiber amount percentage by weight (Fib%), length to diameter ratio (L/D), fiber tensile strength (FTS), fiber elastic modulus (FEM), environment temperature (ET), and curing time (CT). The performance of the models was deduced using correlation coefficient (R) and slope of regression line. The established models were also assessed using relative root mean square error (RRMSE), Mean absolute error (MAE), Root squared error (RSE), root mean square error (RMSE), objective function (OBF), performance index (PI) and Nash-Sutcliffe efficiency (NSE). The resulting mathematical GP-based equations are easy to understand and are consistent disclosing the originality of GEP model with R in the testing phase equals to 0.8623, 0.9269, and 0.8645 for CS, TS and FS respectively. The PI and OBF are both less than 0.2 and are in line with the literature, showing that the models are free from overfitting. Consequently, all proposed models have high generalization with less error measures. The sensitivity analysis showed that C%, Fagg%, and ET are the most significant variables for all three models developed with sensitiveness index higher than 10 %. The result of the research can assist researchers, practitioners, and designers to assess SHCC and will lead to sustainable, faster, and safer construction from environment-friendly waste management point of view.
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Affiliation(s)
- Yasar Khan
- Department of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan
| | - Adeel Zafar
- Department of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan
| | | | - Muhammad Faisal Javed
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan
| | - Bawar Iftikhar
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan
| | - Yaser Gamil
- Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Sweden
- Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia
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Qian Y, Sufian M, Accouche O, Azab M. Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete. PLoS One 2022; 17:e0278161. [PMID: 36548370 PMCID: PMC9779036 DOI: 10.1371/journal.pone.0278161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 11/11/2022] [Indexed: 12/24/2022] Open
Abstract
The estimation of concrete characteristics through artificial intelligence techniques is come out to be an effective way in the construction sector in terms of time and cost conservation. The manufacturing of Ultra-High-Performance Concrete (UHPC) is based on combining numerous ingredients, resulting in a very complex composite in fresh and hardened form. The more ingredients, along with more possible combinations, properties and relative mix proportioning, results in difficult prediction of UHPC behavior. The main aim of this research is the development of Machine Learning (ML) models to predict UHPC flowability and compressive strength. Accordingly, sophisticated and effective artificial intelligence approaches are employed in the current study. For this purpose, an individual ML model named Decision Tree (DT) and ensembled ML algorithms called Bootstrap Aggregating (BA) and Gradient Boosting (GB) are applied. Statistical analyses like; Determination Coefficient (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are also employed to evaluate algorithms' performance. It is concluded that the GB approach appropriately forecasts the UHPC flowability and compressive strength. The higher R2 value, i.e., 0.94 and 0.95 for compressive and flowability, respectively, of the DT technique and lesser error values, have higher precision than other considered algorithms with lower R2 values. SHAP analysis reveals that limestone powder content and curing time have the highest SHAP values for UHPC flowability and compressive strength, respectively. The outcomes of this research study would benefit the scholars of the construction industry to quickly and effectively determine the flowability and compressive strength of UHPC.
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Affiliation(s)
- Yunfeng Qian
- School of Civil Engineering, Changsha University of Science & Technology, Changsha, PR China
| | - Muhammad Sufian
- School of Civil Engineering, Southeast University, Nanjing, PR China
- * E-mail:
| | - Oussama Accouche
- College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
| | - Marc Azab
- College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
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A Review on Pollution Treatment in Cement Industrial Areas: From Prevention Techniques to Python-Based Monitoring and Controlling Models. Processes (Basel) 2022. [DOI: 10.3390/pr10122682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Anthropogenic climate change, global warming, environmental pollution, and fossil fuel depletion have been identified as critical current scenarios and future challenges. Cement plants are one of the most impressive zones, emitting 15% of the worldwide contaminations into the environment among various industries. These contaminants adversely affect human well-being, flora, and fauna. Meanwhile, the use of cement-based substances in various fields, such as civil engineering, medical applications, etc., is inevitable due to the continuous increment of population and urbanization. To cope with this challenge, numerous filtering methods, recycling techniques, and modeling approaches have been introduced. Among the various statistical, mathematical, and computational modeling solutions, Python has received tremendous attention because of the benefit of smart libraries, heterogeneous data integration, and meta-models. The Python-based models are able to optimize the raw material contents and monitor the released pollutants in cement complex outputs with intelligent predictions. Correspondingly, this paper aims to summarize the performed studies to illuminate the resultant emissions from the cement complexes, their treatment methods, and the crucial role of Python modeling toward the high-efficient production of cement via a green and eco-friendly procedure. This comprehensive review sheds light on applying smart modeling techniques rather than experimental analysis for fundamental and applied research and developing future opportunities.
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Bulbul AMR, Khan K, Nafees A, Amin MN, Ahmad W, Usman M, Nazar S, Arab AMA. In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches. MATERIALS (BASEL, SWITZERLAND) 2022; 15:7764. [PMID: 36363356 PMCID: PMC9655191 DOI: 10.3390/ma15217764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/22/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
In recent decades, a variety of organizational sectors have demanded and researched green structural materials. Concrete is the most extensively used manmade material. Given the adverse environmental effect of cement manufacturing, research has focused on minimizing environmental impact and cement-based product costs. Metakaolin (MK) as an additive or partial cement replacement is a key subject of concrete research. Developing predictive machine learning (ML) models is crucial as environmental challenges rise. Since cement-based materials have few ML approaches, it is important to develop strategies to enhance their mechanical properties. This article analyses ML techniques for forecasting MK concrete compressive strength (fc'). Three different individual and ensemble ML predictive models are presented in detail, namely decision tree (DT), multilayer perceptron neural network (MLPNN), and random forest (RF), along with the most effective factors, allowing for efficient investigation and prediction of the fc' of MK concrete. The authors used a database of MK concrete mechanical features for model generalization, a key aspect of any prediction or simulation effort. The database includes 551 data points with relevant model parameters for computing MK concrete's fc'. The database contains cement, metakaolin, coarse and fine aggregate, water, silica fume, superplasticizer, and age, which affect concrete's fc' but were seldom considered critical input characteristics in the past. Finally, the performance of the models is assessed to pick and deploy the best predicted model for MK concrete mechanical characteristics. K-fold cross validation was employed to avoid overfitting issues of the models. Additionally, ML approaches were utilized to combine SHapley Additive exPlanations (SHAP) data to better understand the MK mix design non-linear behaviour and how each input parameter's weighting influences the total contribution. Results depict that DT AdaBoost and modified bagging are the best ML algorithms for predicting MK concrete fc' with R2 = 0.92. Moreover, according to SHAP analysis, age impacts MK concrete fc' the most, followed by coarse aggregate and superplasticizer. Silica fume affects MK concrete's fc' least. ML algorithms estimate MK concrete's mechanical characteristics to promote sustainability.
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Affiliation(s)
| | - Kaffayatullah Khan
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Afnan Nafees
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan
| | - Muhammad Nasir Amin
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Waqas Ahmad
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan
| | - Muhammad Usman
- Interdisciplinary Research Center for Hydrogen and Energy Storage (IRC-HES), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
| | - Sohaib Nazar
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan
| | - Abdullah Mohammad Abu Arab
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
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Al-Hashem MN, Amin MN, Ahmad W, Khan K, Ahmad A, Ehsan S, Al-Ahmad QMS, Qadir MG. Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete. MATERIALS (BASEL, SWITZERLAND) 2022; 15:6928. [PMID: 36234267 PMCID: PMC9572500 DOI: 10.3390/ma15196928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 09/28/2022] [Accepted: 10/01/2022] [Indexed: 06/16/2023]
Abstract
Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique's higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC's compressive and flexural strengths, respectively.
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Affiliation(s)
- Mohammed Najeeb Al-Hashem
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Muhammad Nasir Amin
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Waqas Ahmad
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan
| | - Kaffayatullah Khan
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Ayaz Ahmad
- MaREI Centre, Ryan Institute and School of Engineering, College of Science and Engineering, National University of Ireland Galway, H91 TK33 Galway, Ireland
| | - Saqib Ehsan
- Department of Civil Engineering, NFC Institute of Engineering and Fertilizer Research, Faisalabad 38090, Pakistan
| | - Qasem M. S. Al-Ahmad
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Muhammad Ghulam Qadir
- Department of Environmental Sciences, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan
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