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Sharma S, Gupta V, Mudgal D. Response surface methodology and machine learning based tensile strength prediction in ultrasonic assisted coating of poly lactic acid bone plates manufactured using fused deposition modeling. ULTRASONICS 2024; 137:107204. [PMID: 37979518 DOI: 10.1016/j.ultras.2023.107204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Poly Lactic Acid (PLA) based bone plates fabricated using Fused Deposition Modeling have poor mechanical strength which can be improved by biocompatible polydopamine (PDM) coating. However, PDM particles, being heavy in nature, settle at the container bottom with increase in coating solution concentration at the time of bone plate coating using dip coating technique. Thus, the present work aims to witness the effect of ultrasonic assisted coating parameters on tensile strength of coated bone plates. The coating parameters involving power of ultrasonic vibrations, coating solution concentration and immersion time were varied. The standard Response Surface Methodology (RSM) was applied and experimental trials were performed for obtaining tensile strength of bone plates under varied coating parameters. The objective of the present study was to compare the values of tensile strength predicted using RSM and machine learning (ML) models. Based on the obtained experimental values, gradient boosting regression (GBReg), linear regression (LReg) and random forest regression (RFReg) were trained and tested for predicting tensile strength of bone plates. The accuracy and prediction errors corresponding to RSM and ML based models were compared with respect to R2, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings revealed that GBReg exhibited R2, MSE, RMSE and MAE values as 0.9312, 1.7142, 1.2877 and 1.0861 respectively, while RSM showed R2, MSE, RMSE and MAE values as 0.882, 2.13, 1.4595 and 1.258 respectively. RSM model has shown minimum accuracy with high prediction errors amongst the four models. GBReg has outperformed other ML models in terms of their accuracy and error metrics. The present study therefore suggests the application of GBReg based ML model for predicting tensile strength of PDM coated bone plates in response to its accurate and robust prediction performance.
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Affiliation(s)
- Shrutika Sharma
- Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India
| | - Vishal Gupta
- Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India.
| | - Deepa Mudgal
- Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India
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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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Workers’ Opinions on Using the Internet of Things to Enhance the Performance of the Olive Oil Industry: A Machine Learning Approach. Processes (Basel) 2023. [DOI: 10.3390/pr11010271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things (IoT) applications in the olive oil industry are almost limitless. Although previous studies have investigated the impact of the IoT on the performance of industries, this issue has yet to be explored in the olive oil industry. In this study we aimed to develop a new model to investigate the factors influencing supply chain improvement in olive oil companies. The model was used to evaluate the relationship between supply chain improvement and olive oil companies’ performance. Demand planning, manufacturing, transportation, customer service, warehousing, and inventory management were the main factors incorporated into the proposed model. Self-organizing map (SOM) clustering and decision trees were employed in the development of the method. The data were collected from respondents with knowledge related to integrating new technologies into the industry. The results demonstrated that IoT implementation in olive oil companies significantly improved their performance. Moreover, it was found that there was a positive relationship between supply chain improvements via IoT implementation in olive oil companies and their performance.
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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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Alkadhim HA, Amin MN, Ahmad W, Khan K, Al-Hashem MN, Houda S, Azab M, Baki ZA. Knowledge Mapping of the Literature on Fiber-Reinforced Geopolymers: A Scientometric Review. Polymers (Basel) 2022; 14:polym14225008. [PMID: 36433135 PMCID: PMC9698855 DOI: 10.3390/polym14225008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/05/2022] [Accepted: 11/10/2022] [Indexed: 11/19/2022] Open
Abstract
This study examined the bibliographic data on fiber-reinforced geopolymers (FRGPs) using scientometrics to determine their important features. Manual review articles are inadequate in their capability to connect various segments of literature in an ordered and systematic manner. Scientific mapping, co-citation, and co-occurrence are the difficult aspects of current research. The Scopus database was utilized to find and obtain the data needed to achieve the study's aims. The VOSviewer application was employed to assess the literature records from 751 publications, including citation, bibliographic, keyword, and abstract details. Significant publishing outlets, keywords, prolific researchers in terms of citations and articles published, top-cited documents, and locations actively participating in FRGP investigations were identified during the data review. The possible uses of FRGP were also highlighted. The scientometric analysis revealed that the most frequently used keywords in FRGP research are inorganic polymers, geopolymers, reinforcement, geopolymer, and compressive strength. Additionally, 27 authors have published more than 10 articles on FRGP, and 29 articles have received more than 100 citations up to June 2022. Due to the graphical illustration and quantitative contribution of scholars and countries, this study can support scholars in building joint ventures and communicating innovative ideas and practices.
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Affiliation(s)
- Hassan Ali Alkadhim
- 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
- Correspondence: ; Tel.: +966-13-589-5431; Fax: +966-13-581-7068
| | - 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
| | - Mohammed Najeeb Al-Hashem
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Sara Houda
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
| | - Marc Azab
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
| | - Zaher Abdel Baki
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
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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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Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete. Polymers (Basel) 2022; 14:polym14183906. [PMID: 36146051 PMCID: PMC9506242 DOI: 10.3390/polym14183906] [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: 08/09/2022] [Revised: 08/19/2022] [Accepted: 08/20/2022] [Indexed: 11/17/2022] Open
Abstract
In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achieve the study’s aims. The validity of employed models was tested and compared using the statistical tests, coefficient of determination (R2), and k-fold method. Moreover, a Shapley Additive Explanations (SHAP) analysis was used to observe the interaction and effect of input parameters on the CS of FRNSC. Six input features, including fiber volume, coarse aggregate to fine aggregate ratio, water to binder ratio, nano-silica, superplasticizer to binder ratio, and specimen age, were used for modeling. In predicting the CS of FRNSC, it was observed that gradient boosting was the model of lower accuracy and the AdaBoost regressor had the highest precision in forecasting the CS of FRNSC. However, the performance of random forest and the bagging regressor was also comparable to that of the AdaBoost regressor model. The R2 for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 0.82, 0.91, 0.91, and 0.92, respectively. Also, the error values of the models further validated the exactness of the ML methods. The average error values for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 5.92, 4.38, 4.24, and 3.73 MPa, respectively. SHAP study discovered that the coarse aggregate to fine aggregate ratio shows a greater negative correlation with FRNSC’s CS. However, specimen age affects FRNSC CS positively. Nano-silica, fiber volume, and the ratio of superplasticizer to binder have both positive and deleterious effects on the CS of FRNSC. Employing these methods will promote the building sector by presenting fast and economical methods for calculating material properties and the impact of raw ingredients.
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A Scientometric-Analysis-Based Review of the Research Development on Geopolymers. Polymers (Basel) 2022; 14:polym14173676. [PMID: 36080752 PMCID: PMC9459891 DOI: 10.3390/polym14173676] [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: 06/07/2022] [Revised: 06/24/2022] [Accepted: 06/25/2022] [Indexed: 11/28/2022] Open
Abstract
A scientometric-based assessment of the literature on geopolymers was conducted in this study to determine its critical aspects. Typical review studies are restricted in their capability to link disparate segments of the literature in a systematic and exact way. Knowledge mapping, co-citation, and co-occurrence are very difficult components of creative research. This study adopted an advanced strategy of data mining, data processing and analysis, visualization and presentation, and interpretation of the bibliographic data on geopolymers. The Scopus database was used to search for and retrieve the data needed to complete the study’s objectives. The relevant sources of publications, keyword assessment, productive authors based on publications and citations, top papers based on citations received, and areas actively engaged in the research of geopolymers are recognized during the data assessment. The VOSviewer (VOS: visualization of similarities) software application was employed to analyze the literature data comprising citation, bibliographic, abstract, keywords, funding, and other information from 7468 relevant publications. In addition, the applications and restrictions associated with the use of geopolymers in the construction sector are discussed, as well as possible solutions to overcome these restrictions. The scientometric analysis revealed that the leading publication source (journal) in terms of articles and citations is “Construction and building materials”; the mostly employed keywords are geopolymer, fly ash, and compressive strength; and the top active and contributing countries based on publications are China, India, and Australia. Because of the quantitative and graphical representation of participating nations and researchers, this study can help academics to create collaborative efforts and exchange creative ideas and approaches. In addition, this study concluded that the large-scale usage of geopolymer concrete is constrained by factors such as curing regime, activator solution scarcity and expense, efflorescence, and alkali–silica reaction. However, embracing the potential solutions outlined in this study might assist in boosting the building industry’s adoption of geopolymer concrete.
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Chen YC, Lee WH, Cheng TW, Chen W, Li YF. The Length Change Ratio of Ground Granulated Blast Furnace Slag-Based Geopolymer Blended with Magnesium Oxide Cured in Various Environments. Polymers (Basel) 2022; 14:polym14163386. [PMID: 36015642 PMCID: PMC9415670 DOI: 10.3390/polym14163386] [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: 07/21/2022] [Revised: 08/09/2022] [Accepted: 08/16/2022] [Indexed: 01/10/2023] Open
Abstract
Geopolymer (GP) has been considered a potential material to replace ordinary Portland cement (OPC) because of its excellent mechanical properties and environmentally friendly process. However, the promotion of GP is limited due to the large shrinkage and the different operating procedures compared to cement. This study aims to reduce the shrinkage of ground granulated blast furnace slag (GGBFS) based GP by the hydration expansion properties of activated magnesium oxide (MgO). The slurry of GP was blended from GGBFS, MgO, and activator; and the compositions of the activator are sodium hydroxide (NaOH), sodium silicate (Na2SiO3), and alumina silicate(NaAlO2). Herein, the GGFBS and MgO were a binder and a shrinkage compensation agent of GP, respectively. After unmolding, the GP specimens were cured under four types of environments and the lengths of the specimens were measured at different time intervals to understand the length change ratio of GP. In this study, two groups of GP specimens were made by fixing the activator to binder (A/B) ratio and the fluidity. The test results show that adding MgO will reduce the shrinkage of GP as A/B ratio was fixed. However, fixing the fluidity exhibited the opposite results. The X-ray diffraction (XRD) was used to check the Mg(OH)2 that occurred due to the MgO hydration under four curing conditions. Three statistical and machine learning methods were used to analyze the length change of GP based on the test data. The testing and analysis results show that the influence of curing environments is more significant for improving the shrinkage of GP than additive MgO.
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Affiliation(s)
- Yen-Chun Chen
- Institute of Mineral Resources Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 10608, Taiwan
- Correspondence: (Y.-C.C.); (Y.-F.L.)
| | - Wei-Hao Lee
- Institute of Mineral Resources Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 10608, Taiwan
| | - Ta-Wui Cheng
- Institute of Mineral Resources Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 10608, Taiwan
| | - Walter Chen
- Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Yeou-Fong Li
- Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
- Correspondence: (Y.-C.C.); (Y.-F.L.)
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Predicting the Rheological Properties of Super-Plasticized Concrete Using Modeling Techniques. MATERIALS 2022; 15:ma15155208. [PMID: 35955143 PMCID: PMC9369977 DOI: 10.3390/ma15155208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 12/07/2022]
Abstract
Interface yield stress (YS) and plastic viscosity (PV) have a significant impact on the pumpability of concrete mixes. This study is based on the application of predictive machine learning (PML) techniques to forecast the rheological properties of fresh concrete. The artificial neural network (NN) and random forest (R-F) PML approaches were introduced to anticipate the PV and YS of concrete. In comparison, the R-F model outperforms the NN model by giving the coefficient of determination (R2) values equal to 0.92 and 0.96 for PV and YS, respectively. In contrast, the model’s legitimacy was also verified by applying statistical checks and a k-fold cross validation approach. The mean absolute error, mean square error, and root mean square error values for R-F models by investigating the YS were noted as 30.36 Pa, 1141.76 Pa, and 33.79 Pa, respectively. Similarly, for the PV, these values were noted as 3.52 Pa·s, 16.48 Pa·s, and 4.06 Pa·s, respectively. However, by comparing these values with the NN’s model, they were found to be higher, which also gives confirmation of R-F’s high precision in terms of predicting the outcomes. A validation approach known as k-fold cross validation was also introduced to authenticate the precision of employed models. Moreover, the influence of the input parameters was also investigated with regard to predictions of PV and YS. The proposed study will be beneficial for the researchers and construction industries in terms of saving time, effort, and cost of a project.
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A Systematic Review of the Research Development on the Application of Machine Learning for Concrete. MATERIALS 2022; 15:ma15134512. [PMID: 35806636 PMCID: PMC9267835 DOI: 10.3390/ma15134512] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/06/2022] [Accepted: 06/12/2022] [Indexed: 12/31/2022]
Abstract
Research on the applications of new techniques such as machine learning is advancing rapidly. Machine learning methods are being employed to predict the characteristics of various kinds of concrete such as conventional concrete, recycled aggregate concrete, geopolymer concrete, fiber-reinforced concrete, etc. In this study, a scientometric-based review on machine learning applications for concrete was performed in order to evaluate the crucial characteristics of the literature. Typical review studies are limited in their capacity to link divergent portions of the literature systematically and precisely. Knowledge mapping, co-citation, and co-occurrence are among the most challenging aspects of innovative studies. The Scopus database was chosen for searching for and retrieving the data required to achieve the study’s aims. During the data analysis, the relevant sources of publications, relevant keywords, productive writers based on publications and citations, top articles based on citations received, and regions actively engaged in research into machine learning applications for concrete were identified. The citation, bibliographic, abstract, keyword, funding, and other data from 1367 relevant documents were retrieved and analyzed using the VOSviewer software tool. The application of machine learning in the construction sector will be advantageous in terms of economy, time-saving, and reduced requirement for effort. This study can aid researchers in building joint endeavors and exchanging innovative ideas and methods, due to the statistical and graphical portrayal of participating authors and countries.
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Khan K, Ahmad W, Amin MN, Nazar S. Nano-Silica-Modified Concrete: A Bibliographic Analysis and Comprehensive Review of Material Properties. NANOMATERIALS 2022; 12:nano12121989. [PMID: 35745327 PMCID: PMC9228660 DOI: 10.3390/nano12121989] [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: 04/26/2022] [Revised: 05/27/2022] [Accepted: 06/06/2022] [Indexed: 02/05/2023]
Abstract
Several review studies have been performed on nano-silica-modified concrete, but this study adopted a new method based on scientometric analysis for the keywords’ assessment in the current research area. A scientometric analysis can deal with vast bibliometric data using a software tool to evaluate the diverse features of the literature. Typical review studies are limited in their ability to comprehensively and accurately link divergent areas of the literature. Based on the analysis of keywords, this study highlighted and described the most significant segments in the research of nano-silica-modified concrete. The challenges associated with using nano-silica were identified, and future research is directed. Moreover, prediction models were developed using data from the literature for the strength estimation of nano-silica-modified concrete. It was noted that the application of nano-silica in cement-based composites is beneficial when used up to an optimal dosage of 2–3% due to high pozzolanic reactivity and a filler effect, whereas a higher dosage of nano-silica has a detrimental influence due to the increased porosity and microcracking caused by the agglomeration of nano-silica particles. The mechanical strength might enhance by 20–25% when NS is incorporated in the optimal amount. The prediction models developed for predicting the strength of nano-silica-modified concrete exhibited good agreement with experimental data due to lower error values. This type of analysis may be used to estimate the essential properties of a material, therefore saving time and money on experimental tests. It is recommended to investigate cost-effective methods for the dispersion of nano-silica in higher concentrations in cement mixes; further in-depth studies are required to develop more accurate prediction models to predict nano-silica-modified concrete properties.
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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;
- 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;
| | - Sohaib Nazar
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan; (W.A.); (S.N.)
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Amin MN, Khan K, Javed MF, Ewais DYZ, Qadir MG, Faraz MI, Alam MW, Alabdullah AA, Imran M. Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming. MATERIALS 2022; 15:ma15113808. [PMID: 35683107 PMCID: PMC9181226 DOI: 10.3390/ma15113808] [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: 04/23/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 02/01/2023]
Abstract
Rice husk ash (RHA) is a significant pollutant produced by agricultural sectors that cause a malignant outcome to the environment. To encourage the re-use of RHA, this work used multi expression programming (MEP) to construct an empirical model for forecasting the compressive nature of concrete made with RHA (CRHA) as a cement substitute. Thus, the compressive strength of CRHA was developed comprising of 192 findings from the broad and trustworthy database obtained from literature review. The most significant characteristics, namely the specimen’s age, the percentage of RHA, the amount of cement, superplasticizer, aggregates, and the amount of water, were used as input for the modeling of CRHA. External validation, sensitivity analysis, statistical checks, and Shapley Additive Explanations (SHAP) analysis were used to evaluate the models’ performance. It was discovered that the most significant factors impacting the compressive strength of CRHA are the age of the concrete sample (AS), the amount of cement (C) and the amount of aggregate (A). The findings of this study have the potential to increase the re-use of RHA in the production of green concrete, hence promoting environmental protection and financial gain.
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Affiliation(s)
- Muhammad Nasir Amin
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Hofuf, Al-Ahsa 31982, Saudi Arabia; (K.K.); (A.A.A.)
- Correspondence: ; Tel.: +966-13-589-5431; Fax: +966-13-581-7068
| | - Kaffayatullah Khan
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Hofuf, Al-Ahsa 31982, Saudi Arabia; (K.K.); (A.A.A.)
| | - Muhammad Faisal Javed
- Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan;
| | - Dina Yehia Zakaria Ewais
- Structural Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt;
| | - Muhammad Ghulam Qadir
- Department of Environmental Sciences, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan;
| | - Muhammad Iftikhar Faraz
- Department of Mechanical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Hofuf, Al-Ahsa 31982, Saudi Arabia;
| | - Mir Waqas Alam
- Department of Physics, College of Science, King Faisal University, P.O. Box 380, Al-Hofuf, Al-Ahsa 31982, Saudi Arabia;
| | - Anas Abdulalim Alabdullah
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Hofuf, Al-Ahsa 31982, Saudi Arabia; (K.K.); (A.A.A.)
| | - Muhammad Imran
- School of Civil and Environmental Engineering (SCEE), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan;
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Investigating the Mechanical Property and Enhanced Mechanism of Modified Pisha Sandstone Geopolymer via Ion Exchange Solidification. Gels 2022; 8:gels8050300. [PMID: 35621598 PMCID: PMC9141581 DOI: 10.3390/gels8050300] [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: 04/14/2022] [Revised: 05/09/2022] [Accepted: 05/10/2022] [Indexed: 02/06/2023] Open
Abstract
The Yellow River has the highest sediment concentration in the world, and the Yellow River coarse sediment mainly comes from a particular kind of argillaceous sandstone, Pisha sandstone. This paper reports an investigation of the possibility of development of low-cost engineering materials using Pisha sandstone via ion exchange modification. The effect of modifiers with different concentration on the inhibition of volume expansion and the strength enhancement of modified Pisha sandstone were studied via ion exchange solidification. The effects of the concentration of ten types of modifier solutions and curing age were considered. The hydration of the mineral components, particle surface potential and reaction products were studied, respectively, by XRD, zeta potential, TG/DTG and SEM. Expansion volume and shear strength tests were conducted to assess the volume stability and mechanical property of modified Pisha sandstone. It showed that the expansion of Pisha sandstone was controlled and that the volume stability and shear strength were improved via ion exchange modification. The results of XRD, TG/DTG and SEM showed that the spacing of the crystal layers of the Pisha sandstone clay mineral and the mass lost had decreased significantly. When the concentration of the modifier was 0.05 mol/L, the volume reduced by 54.55% maximum and the shear strength reached the peak of 138 kPa.
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Amin MN, Khan K, Javed MF, Aslam F, Qadir MG, Faraz MI. Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques. MATERIALS 2022; 15:ma15103478. [PMID: 35629515 PMCID: PMC9147112 DOI: 10.3390/ma15103478] [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: 03/29/2022] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 01/25/2023]
Abstract
The emission of greenhouse gases and natural-resource depletion caused by the production of ordinary Portland cement (OPC) have a detrimental effect on the environment. Thus, an alternative means is required to produce eco-friendly concrete such as geopolymer concrete (GPC). However, GPC has a complex cementitious matrix and an ambiguous mix design. Aside from that, the composition and proportions of materials utilized may have an impact on the compressive strength. Similarly, the use of robust and efficient machine-learning (ML) approaches is now required to forecast the strength of such a composite cementitious matrix. As a result, this study anticipated the compressive strength of GPC with waste resources using ensemble and non-ensemble ML algorithms. This was accomplished through the use of Anaconda (Python). To build a strong ensemble learner by integrating weak learners, adaptive boosting, random forest (RF), and ensemble learner bagging were employed. Furthermore, ensemble learners were utilized on non-ensemble or weak learners, such as decision trees (DT) and support vector machines (SVM) via regression. The data encompassed 156 statistical samples in which nine variables, namely superplasticizer (kg/m3), fly ash (kg/m3), ground granulated blast-furnace slag (GGBS), temperature (°C), coarse and fine aggregate (kg/m3), sodium silicate (Na2SiO3), and sodium hydroxide (NaOH), were chosen to anticipate the results. Exploring it in depth, twenty sub-models with ensemble boosting and bagging approaches were trained, and tuning was performed to achieve the highest possible coefficient of determination (R2). Moreover, cross K-Fold validation analysis and statistical checks were performed via indicators for the evaluation of the models. The result revealed that ensemble approaches yielded robust performance compared to non-ensemble algorithms. Generally, an ensemble learner with the RF and bagging approach on a DT yielded robust performance by achieving a better R2 as 0.93, and with the lowest statistical errors. The communal model in artificial-intelligence analysis, on average, improved the accuracy of the model.
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Affiliation(s)
- Muhammad Nasir Amin
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia;
- Correspondence: ; Tel.: +966-13-589-5431; Fax: +966-13-581-7068
| | - Kaffayatullah Khan
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia;
| | - Muhammad Faisal Javed
- Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan;
| | - Fahid Aslam
- Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Muhammad Ghulam Qadir
- Department of Environmental Sciences, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan;
| | - Muhammad Iftikhar Faraz
- Department of Mechanical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
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Khan K, Ahmad W, Amin MN, Aslam F, Ahmad A, Al-Faiad MA. Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete. MATERIALS 2022; 15:ma15103430. [PMID: 35629456 PMCID: PMC9147385 DOI: 10.3390/ma15103430] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 01/24/2023]
Abstract
Numerous tests are used to determine the performance of concrete, but compressive strength (CS) is usually regarded as the most important. The recycled aggregate concrete (RAC) exhibits lower CS compared to natural aggregate concrete. Several variables, such as the water-cement ratio, the strength of the parent concrete, recycled aggregate replacement ratio, density, and water absorption of recycled aggregate, all impact the RAC’s CS. Many studies have been carried out to ascertain the influence of each of these elements separately. However, it is difficult to investigate their combined effect on the CS of RAC experimentally. Experimental investigations entail casting, curing, and testing samples, which require considerable work, expense, and time. It is vital to adopt novel methods to the stated aim in order to conduct research quickly and efficiently. The CS of RAC was predicted in this research utilizing machine learning techniques like decision tree, gradient boosting, and bagging regressor. The data set included eight input variables, and their effect on the CS of RAC was evaluated. Coefficient correlation (R2), the variance between predicted and experimental outcomes, statistical checks, and k-fold evaluations, were carried out to validate and compare the models. With an R2 of 0.92, the bagging regressor technique surpassed the decision tree and gradient boosting in predicting the strength of RAC. The statistical assessments also validated the superior accuracy of the bagging regressor model, yielding lower error values like mean absolute error (MAE) and root mean square error (RMSE). MAE and RMSE values for the bagging model were 4.258 and 5.693, respectively, which were lower than the other techniques employed, i.e., gradient boosting (MAE = 4.956 and RMSE = 7.046) and decision tree (MAE = 6.389 and RMSE = 8.952). Hence, the bagging regressor is the best suitable technique to predict the CS of RAC.
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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;
- Correspondence:
| | - Waqas Ahmad
- 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;
| | - Fahid Aslam
- Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Ayaz Ahmad
- MaREI Centre, Ryan Institute and School of Engineering, College of Science and Engineering, National University of Ireland Galway, H91 HX31 Galway, Ireland;
| | - Majdi Adel Al-Faiad
- Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
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