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Dodo Y, Arif K, Alyami M, Ali M, Najeh T, Gamil Y. Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI). Sci Rep 2024; 14:4598. [PMID: 38409333 PMCID: PMC10897462 DOI: 10.1038/s41598-024-54513-y] [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: 10/28/2023] [Accepted: 02/13/2024] [Indexed: 02/28/2024] Open
Abstract
Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.
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Affiliation(s)
- Yakubu Dodo
- Architectural Engineering Department, College of Engineering, Najran University, Najran, Kingdom of Saudi Arabia
| | - Kiran Arif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, 47040, Pakistan.
| | - Mana Alyami
- Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia
| | - Mujahid Ali
- Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland
| | - Taoufik Najeh
- Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Luleå, Sweden.
| | - Yaser Gamil
- Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
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Shi X, Chen S, Wang Q, Lu Y, Ren S, Huang J. Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete. Gels 2024; 10:148. [PMID: 38391478 PMCID: PMC10887719 DOI: 10.3390/gels10020148] [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: 01/16/2024] [Revised: 02/08/2024] [Accepted: 02/11/2024] [Indexed: 02/24/2024] Open
Abstract
As an environmentally responsible alternative to conventional concrete, geopolymer concrete recycles previously used resources to prepare the cementitious component of the product. The challenging issue with employing geopolymer concrete in the building business is the absence of a standard mix design. According to the chemical composition of its components, this work proposes a thorough system or framework for estimating the compressive strength of fly ash-based geopolymer concrete (FAGC). It could be possible to construct a system for predicting the compressive strength of FAGC using soft computing methods, thereby avoiding the requirement for time-consuming and expensive experimental tests. A complete database of 162 compressive strength datasets was gathered from the research papers that were published between the years 2000 and 2020 and prepared to develop proposed models. To address the relationships between inputs and output variables, long short-term memory networks were deployed. Notably, the proposed model was examined using several soft computing methods. The modeling process incorporated 17 variables that affect the CSFAG, such as percentage of SiO2 (SiO2), percentage of Na2O (Na2O), percentage of CaO (CaO), percentage of Al2O3 (Al2O3), percentage of Fe2O3 (Fe2O3), fly ash (FA), coarse aggregate (CAgg), fine aggregate (FAgg), Sodium Hydroxide solution (SH), Sodium Silicate solution (SS), extra water (EW), superplasticizer (SP), SH concentration, percentage of SiO2 in SS, percentage of Na2O in SS, curing time, curing temperature that the proposed model was examined to several soft computing methods such as multi-layer perception neural network (MLPNN), Bayesian regularized neural network (BRNN), generalized feed-forward neural networks (GFNN), support vector regression (SVR), decision tree (DT), random forest (RF), and LSTM. Three main innovations of this study are using the LSTM model for predicting FAGC, optimizing the LSTM model by a new evolutionary algorithm called the marine predators algorithm (MPA), and considering the six new inputs in the modeling process, such as aggregate to total mass ratio, fine aggregate to total aggregate mass ratio, FASiO2:Al2O3 molar ratio, FA SiO2:Fe2O3 molar ratio, AA Na2O:SiO2 molar ratio, and the sum of SiO2, Al2O3, and Fe2O3 percent in FA. The performance capacity of LSTM-MPA was evaluated with other artificial intelligence models. The results indicate that the R2 and RMSE values for the proposed LSTM-MPA model were as follows: MLPNN (R2 = 0.896, RMSE = 3.745), BRNN (R2 = 0.931, RMSE = 2.785), GFFNN (R2 = 0.926, RMSE = 2.926), SVR-L (R2 = 0.921, RMSE = 3.017), SVR-P (R2 = 0.920, RMSE = 3.291), SVR-S (R2 = 0.934, RMSE = 2.823), SVR-RBF (R2 = 0.916, RMSE = 3.114), DT (R2 = 0.934, RMSE = 2.711), RF (R2 = 0.938, RMSE = 2.892), LSTM (R2 = 0.9725, RMSE = 1.7816), LSTM-MPA (R2 = 0.9940, RMSE = 0.8332), and LSTM-PSO (R2 = 0.9804, RMSE = 1.5221). Therefore, the proposed LSTM-MPA model can be employed as a reliable and accurate model for predicting CSFAG. Noteworthy, the results demonstrated the significance and influence of fly ash and sodium silicate solution chemical compositions on the compressive strength of FAGC. These variables could adequately present variations in the best mix designs discovered in earlier investigations. The suggested approach may also save time and money by accurately estimating the compressive strength of FAGC with low calcium content.
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Affiliation(s)
- Xuyang Shi
- School of Mines, China University of Mining and Technology, Xuzhou 221116, China
| | - Shuzhao Chen
- School of Mines, China University of Mining and Technology, Xuzhou 221116, China
| | - Qiang Wang
- School of Mines, China University of Mining and Technology, Xuzhou 221116, China
| | - Yijun Lu
- School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
| | - Shisong Ren
- Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 Delft, The Netherlands
| | - Jiandong Huang
- School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
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Zhou J, Su Z, Hosseini S, Tian Q, Lu Y, Luo H, Xu X, Chen C, Huang J. Decision tree models for the estimation of geo-polymer concrete compressive strength. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:1413-1444. [PMID: 38303471 DOI: 10.3934/mbe.2024061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash-Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R2 of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a20 of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R2 of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a20 of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R2 were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models.
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Affiliation(s)
- Ji Zhou
- College of Civil and Environmental Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China
| | - Zhanlin Su
- Shandong Energy Group Xinwen Mining Co., Ltd., Taian 271233, China
| | - Shahab Hosseini
- Faculty of the Engineering, Tarbiat Modares University, Jalal AleAhmad, Nasr, Tehran, Iran
| | - Qiong Tian
- College of Civil and Environmental Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China
| | - Yijun Lu
- School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
| | - Hao Luo
- School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
| | - Xingquan Xu
- Guangdong Hualu Transport Technology Co., Ltd, Guangzhou, China
| | - Chupeng Chen
- School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
- Guangdong Hualu Transport Technology Co., Ltd, Guangzhou, China
| | - Jiandong Huang
- School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
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Qureshi HJ, Saleem MU, Javed MF, Al Fuhaid AF, Ahmad J, Amin MN, Khan K, Aslam F, Arifuzzaman M. Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches. MATERIALS (BASEL, SWITZERLAND) 2022; 15:7412. [PMID: 36363008 PMCID: PMC9656842 DOI: 10.3390/ma15217412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the nth estimators to achieve a robust R2. Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R2 of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.
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Affiliation(s)
- Hisham Jahangir Qureshi
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | | | - Muhammad Faisal Javed
- Department of Civil Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan
| | - Abdulrahman Fahad Al Fuhaid
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Jawad Ahmad
- Department of Civil Engineering, Swedish College of Engineering, Wah Cantt 47070, Pakistan
| | - Muhammad Nasir Amin
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Kaffayatullah Khan
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Fahid Aslam
- Department of Civil Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Md Arifuzzaman
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
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Khan K, Ahmad W, Amin MN, Ahmad A, Nazar S, Alabdullah AA, Arab AMA. Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms. MATERIALS 2022; 15:ma15124108. [PMID: 35744167 PMCID: PMC9227983 DOI: 10.3390/ma15124108] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 05/30/2022] [Accepted: 06/01/2022] [Indexed: 02/06/2023]
Abstract
Recently, the high demand for marble stones has progressed in the construction industry, ultimately resulting in waste marble production. Thus, environmental degradation is unavoidable because of waste generated from quarry drilling, cutting, and blasting methods. Marble waste is produced in an enormous amount in the form of odd blocks and unwanted rock fragments. Absence of a systematic way to dispose of these marble waste massive mounds results in environmental pollution and landfills. To reduce this risk, an effort has been made for the incorporation of waste marble powder into concrete for sustainable construction. Different proportions of marble powder are considered as a partial substitute in concrete. A total of 40 mixes are prepared. The effectiveness of marble in concrete is assessed by comparing the compressive strength with the plain mix. Supervised machine learning algorithms, bagging (Bg), random forest (RF), AdaBoost (AdB), and decision tree (DT) are used in this study to forecast the compressive strength of waste marble powder concrete. The models’ performance is evaluated using correlation coefficient (R2), root mean square error, and mean absolute error and mean square error. The achieved performance is then validated by using the k-fold cross-validation technique. The RF model, having an R2 value of 0.97, has more accurate prediction results than Bg, AdB, and DT models. The higher R2 values and lesser error (RMSE, MAE, and MSE) values are the indicators for better performance of RF model among all individual and ensemble models. The implementation of machine learning techniques for predicting the mechanical properties of concrete would be a practical addition to the civil engineering domain by saving effort, resources, and time.
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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; (M.N.A.); (A.A.A.); (A.M.A.A.)
- Correspondence:
| | - Waqas Ahmad
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan; (W.A.); (S.N.)
| | - Muhammad Nasir Amin
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia; (M.N.A.); (A.A.A.); (A.M.A.A.)
| | - Ayaz Ahmad
- MaREI Centre, Ryan Institute, School of Engineering, College of Science and Engineering, National University of Ireland Galway, H91 HX31 Galway, Ireland;
| | - Sohaib Nazar
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan; (W.A.); (S.N.)
| | - Anas Abdulalim Alabdullah
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia; (M.N.A.); (A.A.A.); (A.M.A.A.)
| | - Abdullah Mohammad Abu Arab
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia; (M.N.A.); (A.A.A.); (A.M.A.A.)
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Rathakrishnan V, Bt Beddu S, Ahmed AN. Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms. Sci Rep 2022; 12:9539. [PMID: 35680937 PMCID: PMC9184605 DOI: 10.1038/s41598-022-12890-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/18/2022] [Indexed: 11/30/2022] Open
Abstract
Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. Researchers have predicted the compressive strength of concrete for various mixes using machine learning and deep learning models. In this research, compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement is predicted using boosting machine learning (BML) algorithms, namely, Light Gradient Boosting Machine, CatBoost Regressor, Gradient Boosting Regressor (GBR), Adaboost Regressor, and Extreme Gradient Boosting. In these studies, the BML model’s performance is evaluated based on prediction accuracy and prediction error rates, i.e., R2, MSE, RMSE, MAE, RMSLE, and MAPE. Additionally, the BML models were further optimised with Random Search algorithms and compared to BML models with default hyperparameters. Comparing all 5 BML models, the GBR model shows the highest prediction accuracy with R2 of 0.96 and lowest model error with MAE and RMSE of 2.73 and 3.40, respectively for test dataset. In conclusion, the GBR model are the best performing BML for predicting the compressive strength of concrete with the highest prediction accuracy, and lowest modelling error.
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Affiliation(s)
- Vimal Rathakrishnan
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia.
| | - Salmia Bt Beddu
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
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Ullah HS, Khushnood RA, Farooq F, Ahmad J, Vatin NI, Ewais DYZ. Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches. MATERIALS 2022; 15:ma15093166. [PMID: 35591498 PMCID: PMC9102231 DOI: 10.3390/ma15093166] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/08/2022] [Accepted: 04/25/2022] [Indexed: 11/16/2022]
Abstract
The entraining and distribution of air voids in the concrete matrix is a complex process that makes the mechanical properties of lightweight foamed concrete (LFC) highly unpredictable. To study the complex nature of aerated concrete, a reliable and robust prediction model is required, employing different machine learning (ML) techniques. This study aims to predict the compressive strength of LFC by using a support vector machine (SVM) as an individual learner along with bagging, boosting, and random forest (RF) as a modified ensemble learner. For that purpose, a database of 191 data points was collected from published literature, where the mix design ingredients, i.e., cement content, sand content, water to cement ratio, and foam volume, were chosen to predict the compressive strength of LFC. The 10-K fold cross-validation method and different statistical error and regression tools, i.e., mean absolute error (MAE), root means square error (RMSE), and coefficient of determinant (R2), were used to evaluate the performance of the developed ML models. The modified ensemble learner (RF) outperforms all models by yielding a strong correlation of R2 = 0.96 along with the lowest statistical error values of MAE = 1.84 MPa and RMSE = 2.52 MPa. Overall, the result suggests that the ensemble learners would significantly enhance the performance and robustness of ML models.
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Affiliation(s)
- Haji Sami Ullah
- NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan; (H.S.U.); (R.A.K.); (J.A.)
| | - Rao Arsalan Khushnood
- NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan; (H.S.U.); (R.A.K.); (J.A.)
| | - Furqan Farooq
- NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan; (H.S.U.); (R.A.K.); (J.A.)
- Military Engineer Service (MES), Ministry of Defence (MoD), Rawalpindi 43600, Pakistan
- Correspondence:
| | - Junaid Ahmad
- NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan; (H.S.U.); (R.A.K.); (J.A.)
| | | | - Dina Yehia Zakaria Ewais
- Structural Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt;
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Shang M, Li H, Ahmad A, Ahmad W, Ostrowski KA, Aslam F, Joyklad P, Majka TM. Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms. MATERIALS 2022; 15:ma15020647. [PMID: 35057364 PMCID: PMC8778266 DOI: 10.3390/ma15020647] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/28/2021] [Accepted: 01/04/2022] [Indexed: 02/06/2023]
Abstract
Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model's performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.
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Affiliation(s)
- Meijun Shang
- School of Architetrue and Civil Engineering, Changchun Sci-Tech Unversity, Changchun 130600, China
- Correspondence: (M.S.); (A.A.)
| | - Hejun Li
- Jilin Northeast Architectural and Municipal Engineering Design Institute Co., Ltd., Changchun 130062, China;
| | - Ayaz Ahmad
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan;
- Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland;
- Correspondence: (M.S.); (A.A.)
| | - Waqas Ahmad
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan;
| | - Krzysztof Adam Ostrowski
- Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland;
| | - Fahid Aslam
- Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Panuwat Joyklad
- Department of Civil and Environmental Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhonnayok 26120, Thailand;
| | - Tomasz M. Majka
- Department of Chemistry and Technology of Polymers, Faculty of Chemical Engineering and Technology, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland;
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Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP. MATERIALS 2021; 14:ma14247531. [PMID: 34947124 PMCID: PMC8703652 DOI: 10.3390/ma14247531] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/24/2021] [Accepted: 11/30/2021] [Indexed: 11/16/2022]
Abstract
Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases' features to promote the usage of green concrete.
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Ahmad A, Ahmad W, Chaiyasarn K, Ostrowski KA, Aslam F, Zajdel P, Joyklad P. Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms. Polymers (Basel) 2021; 13:polym13193389. [PMID: 34641204 PMCID: PMC8512145 DOI: 10.3390/polym13193389] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 09/26/2021] [Accepted: 09/29/2021] [Indexed: 12/01/2022] Open
Abstract
The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R2 equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. The lesser values of the errors, MAE, MSE, and RMSE of the boosting technique give 1.69 MPa, 4.16 MPa, and 2.04 MPa, respectively, indicating the high accuracy of the boosting algorithm. However, the statistical check of the errors (MAE, MSE, RMSE) and k-fold cross-validation method confirms the high precision of the boosting technique. In addition, the sensitivity analysis was also introduced to evaluate the contribution level of the input parameters towards the prediction of CS of GPC. The better accuracy can be achieved by incorporating other ensemble ML techniques such as AdaBoost, bagging, and gradient boosting.
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Affiliation(s)
- Ayaz Ahmad
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan; (A.A.); (W.A.)
- Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland; (K.A.O.); (P.Z.)
| | - Waqas Ahmad
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan; (A.A.); (W.A.)
| | - Krisada Chaiyasarn
- Thammasat Research Unit in Infrastructure Inspection and Monitoring, Repair and Strengthening (IIMRS), Faculty of Engineering, Thammasat University Rangsit, Klong Luang Pathumthani 12121, Thailand
- Correspondence:
| | - Krzysztof Adam Ostrowski
- Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland; (K.A.O.); (P.Z.)
| | - Fahid Aslam
- Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Paulina Zajdel
- Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland; (K.A.O.); (P.Z.)
| | - Panuwat Joyklad
- Department of Civil and Environmental Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhonnayok 26120, Thailand;
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11
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Assessment of cement characteristics affecting rheological properties of cement pastes. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05925-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash. MATERIALS 2021; 14:ma14174934. [PMID: 34501024 PMCID: PMC8434412 DOI: 10.3390/ma14174934] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/16/2021] [Accepted: 08/24/2021] [Indexed: 11/25/2022]
Abstract
Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R2) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect.
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13
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Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature. MATERIALS 2021; 14:ma14154222. [PMID: 34361416 PMCID: PMC8348726 DOI: 10.3390/ma14154222] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 11/17/2022]
Abstract
High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.
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14
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Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material. MATERIALS 2021; 14:ma14092297. [PMID: 33946688 PMCID: PMC8125406 DOI: 10.3390/ma14092297] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/21/2021] [Accepted: 04/28/2021] [Indexed: 11/25/2022]
Abstract
Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.
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15
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Sun T, Lv X, Cai Y, Pan Y, Huang J. Software test quality evaluation based on fuzzy mathematics. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The thesis starts with the connotation and attributes of software testing quality, introduces software testing quality evaluation methods, and analyzes and discusses software testing quality evaluation models based on fuzzy mathematics theory. Focusing on the key technical problems of software testing quality, discuss the key technologies to solve the software testing quality evaluation model establishment. Through the use of fuzzy models, the cost of software testing quality evaluation is effectively reduced, and the reliability of software testing quality evaluation methods is improved. This model can quickly evaluate the quality of software testing, can avoid the occurrence of local maxima, overcome the shortcomings of existing evaluation models and tools, and can correctly reflect the relationship between the internal and external properties of the software. Using the new software testing quality evaluation method, comparing the evaluation models and tools used before, summarizing the methods of software testing quality improvement. The application of these methods effectively improves the software testing quality.
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Affiliation(s)
- Tingting Sun
- College of Science and Teachnology, Agricultural University of Hebei, Huanghua Hebei, China
| | - Xingjun Lv
- College of Science and Teachnology, Agricultural University of Hebei, Huanghua Hebei, China
| | - Yakun Cai
- College of Science and Teachnology, Agricultural University of Hebei, Huanghua Hebei, China
| | - Yuqing Pan
- College of Science and Teachnology, Agricultural University of Hebei, Huanghua Hebei, China
| | - Jianchang Huang
- College of Science and Teachnology, Agricultural University of Hebei, Huanghua Hebei, China
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16
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Design Optimization of Rubber-Basalt Fiber- Modified Concrete Mix Ratios Based on a Response Surface Method. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10196753] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Rubber aggregates produced from waste rubber materials and environmentally friendly basalt fibers are excellent concrete modification materials, which significantly improve the working performance and mechanical properties of concrete. This paper studied the influences of water-binder ratio, basalt fiber content and rubber content on the properties of rubber-basalt fiber modified concrete (RBFC). Based on the response surface method (RSM), optimization schemes of three preparation parameters were designed. The results showed that all preparation parameters have significant impacts on the slump. The rubber content has a closer relationship with the compressive strength and the quadratic term of the basalt fiber content has a significant impact on the flexural strength. According to the analysis, the optimal mix ratio which possesses reliable accuracy compared with experimental results includes a water-binder ratio of 0.39, a basalt fiber content of 4.56 kg/m3 and a rubber content of 10%,
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17
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Emerging intelligent algorithms: challenges and applications. Neural Comput Appl 2019. [DOI: 10.1007/s00521-018-3930-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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