Usama Asif, Muhammad Faisal Javed, Deema Mohammed Alsekait, Diaa Salama AbdElminaam, Hisham Alabduljabbar
Plastic waste (PW) poses a significant threat as a hazardous material, while the production of cement raises environmental concerns. It is imperative to urgently address and reduce both PW and cement usage in concrete products. Recently, several experimental studies have been performed to incorporate PW into paver blocks (PBs) as a substitute for cement. However, the experimental testing is not enough to optimize the use of waste plastic in pavers due to resource and time limitations. This study proposes an innovative approach, integrating experimental testing with machine learning to optimize PW ratios in PBs efficiently. Initially, experimental investigations are performed to examine the compressive strength (CS) of plastic sand paver blocks (PSPBs). Varied mix proportions of plastic and sand with different sizes of sand are employed. Moreover, to enhance the CS and meet the minimum requirements of ASTM C902-15 for light traffic, basalt fibers, a sustainable industrial material, are also utilized in the manufacturing process of environmentally friendly PSPB. The highest CS of 17.26 MPa is achieved by using the finest-size sand particles with a plastic-to-sand ratio of 30:70. Additionally, the inclusion of 0.5% basalt fiber, measuring 4 mm in length, yields further enhancement in outcome by significantly improving CS by 25.4% (21.65 MPa). Following that, an extensive experimental record is established, and multi-expression programming (MEP) is used to forecast the CS of PSPB. The model’s projected results are confirmed by using various statistical procedures and external validation methods. Furthermore, comprehensive parametric and sensitivity studies are conducted to assess the effectiveness of the MEP-based proposed models. The sensitivity analysis demonstrates that the size of the sand particles and the fiber content are the primary factors contributing to more than 50% of the CS in PSPB. The parametric analysis confirmed the model’s accuracy by demonstrating a comparable pattern to the experimental results. Furthermore, the results indicate that the proposed MEP-based formulation exhibits high precision with an R2 of 0.89 and possesses a strong ability to predict. The study also provides a graphical user interface to increase the significance of ML in the practical application of handling waste management. The main aim of this research is to enhance the reuse of PW to promote sustainability and economic benefits, particularly in producing green environments with integration of machine learning and experimental investigations.
{"title":"Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste","authors":"Usama Asif, Muhammad Faisal Javed, Deema Mohammed Alsekait, Diaa Salama AbdElminaam, Hisham Alabduljabbar","doi":"10.1515/rams-2024-0051","DOIUrl":"https://doi.org/10.1515/rams-2024-0051","url":null,"abstract":"Plastic waste (PW) poses a significant threat as a hazardous material, while the production of cement raises environmental concerns. It is imperative to urgently address and reduce both PW and cement usage in concrete products. Recently, several experimental studies have been performed to incorporate PW into paver blocks (PBs) as a substitute for cement. However, the experimental testing is not enough to optimize the use of waste plastic in pavers due to resource and time limitations. This study proposes an innovative approach, integrating experimental testing with machine learning to optimize PW ratios in PBs efficiently. Initially, experimental investigations are performed to examine the compressive strength (CS) of plastic sand paver blocks (PSPBs). Varied mix proportions of plastic and sand with different sizes of sand are employed. Moreover, to enhance the CS and meet the minimum requirements of ASTM C902-15 for light traffic, basalt fibers, a sustainable industrial material, are also utilized in the manufacturing process of environmentally friendly PSPB. The highest CS of 17.26 MPa is achieved by using the finest-size sand particles with a plastic-to-sand ratio of 30:70. Additionally, the inclusion of 0.5% basalt fiber, measuring 4 mm in length, yields further enhancement in outcome by significantly improving CS by 25.4% (21.65 MPa). Following that, an extensive experimental record is established, and multi-expression programming (MEP) is used to forecast the CS of PSPB. The model’s projected results are confirmed by using various statistical procedures and external validation methods. Furthermore, comprehensive parametric and sensitivity studies are conducted to assess the effectiveness of the MEP-based proposed models. The sensitivity analysis demonstrates that the size of the sand particles and the fiber content are the primary factors contributing to more than 50% of the CS in PSPB. The parametric analysis confirmed the model’s accuracy by demonstrating a comparable pattern to the experimental results. Furthermore, the results indicate that the proposed MEP-based formulation exhibits high precision with an <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> of 0.89 and possesses a strong ability to predict. The study also provides a graphical user interface to increase the significance of ML in the practical application of handling waste management. The main aim of this research is to enhance the reuse of PW to promote sustainability and economic benefits, particularly in producing green environments with integration of machine learning and experimental investigations.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"5 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
By collecting engineered cementitious composite (ECC) uniaxial tensile experimental research data, aiming at the multiple cracking characteristics of the strain hardening stage of the ECC stress–strain curve, a theoretical model describing the constitutive relationship of the ECC uniaxial tensile stress–strain – the multiple cracking sawtooth model – is proposed. Several model parameters were obtained with the fitting analysis of many ECC uniaxial tensile stress–strain curves. The application conditions and influencing factors of the three-order multi-crack “sawtooth” model of polyvinyl alcohol (PVA)-ECC and polyethylene (PE)-ECC and the four-order multi-crack “sawtooth” model of PVA-ECC are studied. The result shows that the higher the fiber reinforcement index, the better the tensile properties of ECC. The fiber reinforcement index is linearly correlated with the initial crack stress and ultimate tensile stress of PVA-ECC and with the ultimate tensile stress and ultimate tensile strain of PE-ECC. The characteristic points of PVA-ECC in the multi-crack cracking stage are as follows: the greater the initial cracking strain, the smaller the ultimate tensile strain, showing an exponential correlation; The greater the initial cracking stress is, the greater the ultimate tensile stress is, and the two are linearly correlated.
{"title":"A sawtooth constitutive model describing strain hardening and multiple cracking of ECC under uniaxial tension","authors":"Lingyu Li, Hongkang Chen, Hongfa Yu, Haiyan Ma, Haotian Fan, Xiaoqing Chen, Yuning Gao","doi":"10.1515/rams-2024-0048","DOIUrl":"https://doi.org/10.1515/rams-2024-0048","url":null,"abstract":"By collecting engineered cementitious composite (ECC) uniaxial tensile experimental research data, aiming at the multiple cracking characteristics of the strain hardening stage of the ECC stress–strain curve, a theoretical model describing the constitutive relationship of the ECC uniaxial tensile stress–strain – the multiple cracking sawtooth model – is proposed. Several model parameters were obtained with the fitting analysis of many ECC uniaxial tensile stress–strain curves. The application conditions and influencing factors of the three-order multi-crack “sawtooth” model of polyvinyl alcohol (PVA)-ECC and polyethylene (PE)-ECC and the four-order multi-crack “sawtooth” model of PVA-ECC are studied. The result shows that the higher the fiber reinforcement index, the better the tensile properties of ECC. The fiber reinforcement index is linearly correlated with the initial crack stress and ultimate tensile stress of PVA-ECC and with the ultimate tensile stress and ultimate tensile strain of PE-ECC. The characteristic points of PVA-ECC in the multi-crack cracking stage are as follows: the greater the initial cracking strain, the smaller the ultimate tensile strain, showing an exponential correlation; The greater the initial cracking stress is, the greater the ultimate tensile stress is, and the two are linearly correlated.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"41 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Waqas Ashraf, Adnan Khan, Yongming Tu, Chao Wang, Nabil Ben Kahla, Muhammad Faisal Javed, Safi Ullah, Jawad Tariq
Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including cement consumption and mitigating environmental effects. The feasibility of RHA on concrete strength was investigated in this research by predicting the split tensile strength (SPT) and flexural strength (FS) of RHA concrete (RHAC). The study used machine learning (ML) methods such as ensemble stacking and gene expression programming (GEP). The stacking model was improved using base learner configurations ML models, such as, random forest (RF), support vector regression, and gradient boosting regression. The proposed models were validated by statistical tests and external validation criteria. Moreover, the effect of input parameters was investigated using Shapley adaptive exPlanations (SHAP) for RF and parametric analysis for GEP-based models. The analysis revealed that the stacking ensemble integrates base learner predictions and demonstrated superior performance, with R values greater than 0.98 and 0.96. Mean absolute error and root mean square error values for both SPT and FS were 0.23, 0.3, 0.5, and 0.7 MPA, respectively. The SHAP analysis demonstrated water, cement, superplasticizer, and age as influential parameters for the RHAC strength. Furthermore, the SPT and FS of RHAC can be predicted with an acceptable error using the GEP expressions in the standard design procedure.
{"title":"Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming","authors":"Muhammad Waqas Ashraf, Adnan Khan, Yongming Tu, Chao Wang, Nabil Ben Kahla, Muhammad Faisal Javed, Safi Ullah, Jawad Tariq","doi":"10.1515/rams-2024-0050","DOIUrl":"https://doi.org/10.1515/rams-2024-0050","url":null,"abstract":"Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including cement consumption and mitigating environmental effects. The feasibility of RHA on concrete strength was investigated in this research by predicting the split tensile strength (SPT) and flexural strength (FS) of RHA concrete (RHAC). The study used machine learning (ML) methods such as ensemble stacking and gene expression programming (GEP). The stacking model was improved using base learner configurations ML models, such as, random forest (RF), support vector regression, and gradient boosting regression. The proposed models were validated by statistical tests and external validation criteria. Moreover, the effect of input parameters was investigated using Shapley adaptive exPlanations (SHAP) for RF and parametric analysis for GEP-based models. The analysis revealed that the stacking ensemble integrates base learner predictions and demonstrated superior performance, with <jats:italic>R</jats:italic> values greater than 0.98 and 0.96. Mean absolute error and root mean square error values for both SPT and FS were 0.23, 0.3, 0.5, and 0.7 MPA, respectively. The SHAP analysis demonstrated water, cement, superplasticizer, and age as influential parameters for the RHAC strength. Furthermore, the SPT and FS of RHAC can be predicted with an acceptable error using the GEP expressions in the standard design procedure.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"8 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hua Si, Daoming Shen, Muhammad Nasir Amin, Siyab Ul Arifeen, Muhammad Tahir Qadir, Kaffayatullah Khan
This study explores the possibilities of a new binding material, i.e., marble cement (MC) made from recycled marble. It will assess how well it performs when mixed with ash from rice husks and fly ash. This research analyzes flexural strength of marble cement mortar (FR-MCM), a mortar that incorporates MC, fly ash, and rice husk ash. A set of machine learning models capable of predicting CS and FS (flexural and compressive strengths) were developed. Gene expression programming (GEP) and multi-expression programming (MEP) are crucial in creating these types of models. Statistics, Taylor’s diagrams, R2 values, and comparisons of experimental and theoretical results were used to evaluate the models. Stress testing also showed how different input features affected the model’s outputs. The accuracy of all GEP models was shown to fall within the acceptable range (R2 = 0.952 for CS and R2 = 0.920 for FS), and all MEP prediction models were determined to be exceptionally accurate (R2 = 0.970 for CS and R2 = 0.935 for FS). The statistical testing for error validation also verified that MEP models were more accurate than GEP models. According to sensitivity analysis, curing age and rice husk ash exerted the most significant influence on the prediction of CS and FS, followed by fly ash and MC.
本研究探讨了一种新型粘结材料的可能性，即由回收大理石制成的大理石水泥（MC）。研究将评估它与稻壳灰和粉煤灰混合后的性能如何。本研究分析了大理石水泥砂浆（FR-MCM）的抗弯强度，这是一种混合了 MC、粉煤灰和稻壳灰的砂浆。开发了一套能够预测 CS 和 FS（抗折和抗压强度）的机器学习模型。基因表达程序设计（GEP）和多重表达程序设计（MEP）是创建这类模型的关键。统计、泰勒图、R 2 值以及实验和理论结果的比较被用来评估模型。压力测试还显示了不同输入特征对模型输出的影响。所有 GEP 模型的精确度都在可接受范围内（CS 的 R 2 = 0.952，FS 的 R 2 = 0.920），所有 MEP 预测模型的精确度都非常高（CS 的 R 2 = 0.970，FS 的 R 2 = 0.935）。误差验证的统计测试也验证了 MEP 模型比 GEP 模型更准确。根据敏感性分析，固化龄期和稻壳灰对 CS 和 FS 的预测影响最大，其次是粉煤灰和 MC。
{"title":"Producing sustainable binding materials using marble waste blended with fly ash and rice husk ash for building materials","authors":"Hua Si, Daoming Shen, Muhammad Nasir Amin, Siyab Ul Arifeen, Muhammad Tahir Qadir, Kaffayatullah Khan","doi":"10.1515/rams-2024-0049","DOIUrl":"https://doi.org/10.1515/rams-2024-0049","url":null,"abstract":"This study explores the possibilities of a new binding material, <jats:italic>i.e.</jats:italic>, marble cement (MC) made from recycled marble. It will assess how well it performs when mixed with ash from rice husks and fly ash. This research analyzes flexural strength of marble cement mortar (FR-MCM), a mortar that incorporates MC, fly ash, and rice husk ash. A set of machine learning models capable of predicting CS and FS (flexural and compressive strengths) were developed. Gene expression programming (GEP) and multi-expression programming (MEP) are crucial in creating these types of models. Statistics, Taylor’s diagrams, <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> values, and comparisons of experimental and theoretical results were used to evaluate the models. Stress testing also showed how different input features affected the model’s outputs. The accuracy of all GEP models was shown to fall within the acceptable range (<jats:italic>R</jats:italic> <jats:sup>2</jats:sup> = 0.952 for CS and <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> = 0.920 for FS), and all MEP prediction models were determined to be exceptionally accurate (<jats:italic>R</jats:italic> <jats:sup>2</jats:sup> = 0.970 for CS and <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> = 0.935 for FS). The statistical testing for error validation also verified that MEP models were more accurate than GEP models. According to sensitivity analysis, curing age and rice husk ash exerted the most significant influence on the prediction of CS and FS, followed by fly ash and MC.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"41 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The processing of γ-TiAl intermetallic compound (Ti–45Al–2Mn–2Nb) is essential for manufacturing aircraft engine components, known for their challenging machinability. This study delved into the machining performance of γ-TiAl intermetallic compound through ultrasonically assisted grinding experiments. Various grinding parameters, such as wheel rotation speed (vs), feed rate (vw), depth of grinding (ap), and ultrasonic amplitude (A), were investigated to understand their effects on grinding forces, temperatures, and surface quality. Gray relational analysis (GRA) and analysis of variance were used to analyze experimental data and ascertain the optimal machining parameters for ultrasonically assisted grinding of γ-TiAl intermetallic compound. Additionally, post-processing surface integrity, encompassing surface roughness, morphology, and residual stresses, was evaluated. The optimal grinding parameter combination was determined as Fn = 3.22 N, Ft = 1.08 N, and T = 174°C through GRA. Under the selected machining conditions, the depth of cut exerted the most significant influence on the grinding force and temperature, while the effect of wheel speed was the weakest. The surface roughness (Ra) of the workpiece increased with increasing feed rate and depth of the cut but decreased gradually with increasing wheel speed. Upon applying ultrasonic vibration, there was a notable decrease in surface roughness, ranging from 20.12 to 7.67%. However, the increase in the wheel speed, depth of cut, and feed rate inhibited the reduction of roughness due to ultrasonic vibration. Ultrasonic vibration effectively reduced the profile height of the workpiece surface, with a maximum reduction of 1.94 μm within the selected range. Nonetheless, as the wheel speed, depth of cut, and feed rate increased, the effectiveness of this reduction gradually diminished.
γ-TiAl金属间化合物（Ti-45Al-2Mn-2Nb）的加工对飞机发动机部件的制造至关重要，而众所周知，这些部件的加工性极具挑战性。本研究通过超声辅助磨削实验对γ-TiAl 金属间化合物的加工性能进行了深入研究。研究了各种磨削参数，如砂轮转速（v s）、进给率（v w）、磨削深度（a p）和超声波振幅（A），以了解它们对磨削力、温度和表面质量的影响。采用灰色关系分析法（GRA）和方差分析法分析实验数据，确定超声辅助磨削γ-TiAl 金属间化合物的最佳加工参数。此外，还评估了加工后的表面完整性，包括表面粗糙度、形貌和残余应力。通过 GRA 确定的最佳磨削参数组合为 F n = 3.22 N，F t = 1.08 N，T = 174°C。在选定的加工条件下，切削深度对磨削力和温度的影响最大，而砂轮速度的影响最小。工件表面粗糙度（Ra）随进给速度和切削深度的增加而增加，但随砂轮速度的增加而逐渐降低。施加超声波振动后，表面粗糙度明显下降，降幅在 20.12% 至 7.67% 之间。然而，砂轮速度、切削深度和进给量的增加抑制了超声波振动导致的粗糙度降低。超声波振动有效降低了工件表面的轮廓高度，在所选范围内最大降低了 1.94 μm。然而，随着砂轮速度、切削深度和进给量的增加，这种降低的效果逐渐减弱。
{"title":"Parameter optimization for ultrasonic-assisted grinding of γ-TiAl intermetallics: A gray relational analysis approach with surface integrity evaluation","authors":"Song Yang, Guangjin Zhang, Guoqing Xiao","doi":"10.1515/rams-2024-0045","DOIUrl":"https://doi.org/10.1515/rams-2024-0045","url":null,"abstract":"The processing of γ-TiAl intermetallic compound (Ti–45Al–2Mn–2Nb) is essential for manufacturing aircraft engine components, known for their challenging machinability. This study delved into the machining performance of γ-TiAl intermetallic compound through ultrasonically assisted grinding experiments. Various grinding parameters, such as wheel rotation speed (<jats:italic>v</jats:italic> <jats:sub>s</jats:sub>), feed rate (<jats:italic>v</jats:italic> <jats:sub>w</jats:sub>), depth of grinding (<jats:italic>a</jats:italic> <jats:sub>p</jats:sub>), and ultrasonic amplitude (<jats:italic>A</jats:italic>), were investigated to understand their effects on grinding forces, temperatures, and surface quality. Gray relational analysis (GRA) and analysis of variance were used to analyze experimental data and ascertain the optimal machining parameters for ultrasonically assisted grinding of γ-TiAl intermetallic compound. Additionally, post-processing surface integrity, encompassing surface roughness, morphology, and residual stresses, was evaluated. The optimal grinding parameter combination was determined as <jats:italic>F</jats:italic> <jats:sub>n</jats:sub> = 3.22 N, <jats:italic>F</jats:italic> <jats:sub>t</jats:sub> = 1.08 N, and <jats:italic>T</jats:italic> = 174°C through GRA. Under the selected machining conditions, the depth of cut exerted the most significant influence on the grinding force and temperature, while the effect of wheel speed was the weakest. The surface roughness (Ra) of the workpiece increased with increasing feed rate and depth of the cut but decreased gradually with increasing wheel speed. Upon applying ultrasonic vibration, there was a notable decrease in surface roughness, ranging from 20.12 to 7.67%. However, the increase in the wheel speed, depth of cut, and feed rate inhibited the reduction of roughness due to ultrasonic vibration. Ultrasonic vibration effectively reduced the profile height of the workpiece surface, with a maximum reduction of 1.94 μm within the selected range. Nonetheless, as the wheel speed, depth of cut, and feed rate increased, the effectiveness of this reduction gradually diminished.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"16 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent decades, with the advancement of micro-electro-mechanical systems technology, traditional materials have become insufficient to meet the demands of cutting-edge technology for various material properties. Composites have attracted widespread attention as an effective and viable solution. Silicon carbide whiskers (SiCw) have emerged as excellent reinforcements due to their high thermal conductivity, low thermal expansion coefficient, high melting point, superior mechanical properties, and high chemical stability. This article provides a comprehensive review of the reinforcement mechanisms and current research state of SiCw-reinforced composites. The reinforcement mechanisms include mainly grain refinement, load transfer, and crack bridging. The composites are categorized based on the type of the matrix: ceramic-based, metal-based, and polymer-based composites. The influence and parameter performance of the reinforcement mechanism on SiCw-reinforced composite materials with different matrices vary. However, the key to improving SiCw-reinforced composites lies in understanding the interplay of properties between the matrix and the reinforcement, as well as the ordered and regular distribution and binding at the interface. Finally, the current state and limitations of SiCw-reinforced composites are summarized, and future development trends are discussed. This article represents a great contribution to the future applications of SiCw-reinforced composite materials.
{"title":"Reinforcement mechanisms and current research status of silicon carbide whisker-reinforced composites: A comprehensive review","authors":"Liyan Lai, Yuxiao Bi, Bing Niu, Guanliang Yu, Yigui Li, Guifu Ding, Qiu Xu","doi":"10.1515/rams-2024-0047","DOIUrl":"https://doi.org/10.1515/rams-2024-0047","url":null,"abstract":"In recent decades, with the advancement of micro-electro-mechanical systems technology, traditional materials have become insufficient to meet the demands of cutting-edge technology for various material properties. Composites have attracted widespread attention as an effective and viable solution. Silicon carbide whiskers (SiCw) have emerged as excellent reinforcements due to their high thermal conductivity, low thermal expansion coefficient, high melting point, superior mechanical properties, and high chemical stability. This article provides a comprehensive review of the reinforcement mechanisms and current research state of SiCw-reinforced composites. The reinforcement mechanisms include mainly grain refinement, load transfer, and crack bridging. The composites are categorized based on the type of the matrix: ceramic-based, metal-based, and polymer-based composites. The influence and parameter performance of the reinforcement mechanism on SiCw-reinforced composite materials with different matrices vary. However, the key to improving SiCw-reinforced composites lies in understanding the interplay of properties between the matrix and the reinforcement, as well as the ordered and regular distribution and binding at the interface. Finally, the current state and limitations of SiCw-reinforced composites are summarized, and future development trends are discussed. This article represents a great contribution to the future applications of SiCw-reinforced composite materials.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"169 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To improve the utilization rate of municipal solid waste incineration (MSWI) ash and achieve resource recycling, this article conducted research on grinding MSWI ash into fine powder for use as a concrete admixture. Initially, the physical and chemical properties of the MSWI ash micro-powder were tested. Subsequently, different amounts of MSWI ash powder concrete were prepared. The macro and micro properties of the concrete were then tested. Finally, a life cycle assessment was utilized to evaluate and compare ordinary concrete with MSWI ash micro-powder concrete. The results indicate that the chemical composition of the MSWI ash micro-powder is similar to that of cement clinker. It exhibits potential hydraulicity and a slow hydration reaction, making it an active admixture suitable for concrete raw materials. With the increasing proportion of MSWI ash micro-powder, the rate of hydration reaction in concrete slows down, resulting in decreased mechanical properties. The microhardness value of the hardened cement paste in MSWI ash micro-powder concrete is lower than that of ordinary concrete. Moreover, the addition of MSWI ash micro-powder helps mitigate the environmental impact of concrete in terms of non-biological energy loss and CO2 emissions.
{"title":"Experimental study on municipal solid waste incineration ash micro-powder as concrete admixture","authors":"Li Hanghang, Shi Dongsheng, Li Hao, Ren Dongdong","doi":"10.1515/rams-2024-0027","DOIUrl":"https://doi.org/10.1515/rams-2024-0027","url":null,"abstract":"To improve the utilization rate of municipal solid waste incineration (MSWI) ash and achieve resource recycling, this article conducted research on grinding MSWI ash into fine powder for use as a concrete admixture. Initially, the physical and chemical properties of the MSWI ash micro-powder were tested. Subsequently, different amounts of MSWI ash powder concrete were prepared. The macro and micro properties of the concrete were then tested. Finally, a life cycle assessment was utilized to evaluate and compare ordinary concrete with MSWI ash micro-powder concrete. The results indicate that the chemical composition of the MSWI ash micro-powder is similar to that of cement clinker. It exhibits potential hydraulicity and a slow hydration reaction, making it an active admixture suitable for concrete raw materials. With the increasing proportion of MSWI ash micro-powder, the rate of hydration reaction in concrete slows down, resulting in decreased mechanical properties. The microhardness value of the hardened cement paste in MSWI ash micro-powder concrete is lower than that of ordinary concrete. Moreover, the addition of MSWI ash micro-powder helps mitigate the environmental impact of concrete in terms of non-biological energy loss and CO<jats:sub>2</jats:sub> emissions.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"7 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qing Tao Guan, Zhong Ling Tong, Muhammad Nasir Amin, Bawar Iftikhar, Muhammad Tahir Qadir, Kaffayatullah Khan
Self-compacting concrete (SCC) is well-known for its capacity to flow under its own weight, which eliminates the need for mechanical vibration and provides benefits such as less labor and faster construction time. Nevertheless, the increased cement content of SCC results in an increase in both costs and carbon emissions. These challenges are resolved in this research by utilizing waste marble and glass powder as cement substitutes. The main objective of this study is to create machine learning models that can predict the compressive strength (CS) of SCC using gene expression programming (GEP) and multi-expression programming (MEP) that produce mathematical equations to capture the correlations between variables. The models’ performance is assessed using statistical metrics, and hyperparameter optimization is conducted on an experimental dataset consisting of eight independent variables. The results indicate that the MEP model outperforms the GEP model, with an R2 value of 0.94 compared to 0.90. Moreover, the sensitivity and SHapley Additive exPlanations analysis revealed that the most significant factor influencing CS is curing time, followed by slump flow and cement quantity. A sustainable approach to SCC design is presented in this study, which improves efficacy and minimizes the need for testing.
{"title":"Analyzing the efficacy of waste marble and glass powder for the compressive strength of self-compacting concrete using machine learning strategies","authors":"Qing Tao Guan, Zhong Ling Tong, Muhammad Nasir Amin, Bawar Iftikhar, Muhammad Tahir Qadir, Kaffayatullah Khan","doi":"10.1515/rams-2024-0043","DOIUrl":"https://doi.org/10.1515/rams-2024-0043","url":null,"abstract":"Self-compacting concrete (SCC) is well-known for its capacity to flow under its own weight, which eliminates the need for mechanical vibration and provides benefits such as less labor and faster construction time. Nevertheless, the increased cement content of SCC results in an increase in both costs and carbon emissions. These challenges are resolved in this research by utilizing waste marble and glass powder as cement substitutes. The main objective of this study is to create machine learning models that can predict the compressive strength (CS) of SCC using gene expression programming (GEP) and multi-expression programming (MEP) that produce mathematical equations to capture the correlations between variables. The models’ performance is assessed using statistical metrics, and hyperparameter optimization is conducted on an experimental dataset consisting of eight independent variables. The results indicate that the MEP model outperforms the GEP model, with an <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> value of 0.94 compared to 0.90. Moreover, the sensitivity and SHapley Additive exPlanations analysis revealed that the most significant factor influencing CS is curing time, followed by slump flow and cement quantity. A sustainable approach to SCC design is presented in this study, which improves efficacy and minimizes the need for testing.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"413 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, the effects of fly ash (FA) on the setting time, compressive strength, and hydration evolution of calcium sulphoaluminate (CSA) cement-based materials with high water content were investigated, targeting the design of a modified high-water material to delay excessively rapid setting time and enhance later-age strength. This was investigated using a combination of X-ray diffraction (XRD), Fourier transform infrared resonance (FTIR) spectroscopy, and Thermogravimetric Analysis (TGA). The results showed that the setting time of the high-water materials was delayed by increasing the FA content, with 15% being the optimal dosage for the setting time. A 5–10% content of FA is conducive to the development of later-age compressive strength and has a slight adverse effect on the early-age compressive strength of high-water materials. The microscopic test results show that FA mainly acts as a microaggregate in the early-age hydration process, whereas in the later-age hydration process, it promotes gypsum consumption and C2S hydration to form ettringite. The incorporation of FA effectively promotes ettringite formation in CSA cement-based materials with high water content. Therefore, the addition of FA can enhance the overall performance of high-water materials to a certain extent, and the long-term strength development of the material can satisfy engineering requirements.
本研究调查了粉煤灰（FA）对高含水量硫铝酸钙（CSA）水泥基材料的凝结时间、抗压强度和水化演化的影响，旨在设计一种改良的高含水量材料，以延缓过快的凝结时间并提高后期强度。研究结合使用了 X 射线衍射 (XRD)、傅立叶变换红外共振 (FTIR) 光谱和热重分析 (TGA)。结果表明，随着 FA 含量的增加，高水材料的凝固时间被延缓，15% 是凝固时间的最佳用量。5-10% 的 FA 含量有利于后期抗压强度的发展，对高水材料的早期抗压强度略有不利影响。显微试验结果表明，FA 在早期水化过程中主要起微集料作用，而在后期水化过程中则促进石膏消耗和 C2S 水化形成乙长石。在含水率较高的 CSA 水泥基材料中掺入 FA 能有效促进乙长石的形成。因此，掺加 FA 能在一定程度上提高高水材料的综合性能，材料的长期强度发展也能满足工程要求。
{"title":"Effect of fly ash on properties and hydration of calcium sulphoaluminate cement-based materials with high water content","authors":"Meng Gao, Mengying Li, Jiahao Wang, Pengfei Yang, Mengge Xu","doi":"10.1515/rams-2024-0046","DOIUrl":"https://doi.org/10.1515/rams-2024-0046","url":null,"abstract":"In this study, the effects of fly ash (FA) on the setting time, compressive strength, and hydration evolution of calcium sulphoaluminate (CSA) cement-based materials with high water content were investigated, targeting the design of a modified high-water material to delay excessively rapid setting time and enhance later-age strength. This was investigated using a combination of X-ray diffraction (XRD), Fourier transform infrared resonance (FTIR) spectroscopy, and Thermogravimetric Analysis (TGA). The results showed that the setting time of the high-water materials was delayed by increasing the FA content, with 15% being the optimal dosage for the setting time. A 5–10% content of FA is conducive to the development of later-age compressive strength and has a slight adverse effect on the early-age compressive strength of high-water materials. The microscopic test results show that FA mainly acts as a microaggregate in the early-age hydration process, whereas in the later-age hydration process, it promotes gypsum consumption and C<jats:sub>2</jats:sub>S hydration to form ettringite. The incorporation of FA effectively promotes ettringite formation in CSA cement-based materials with high water content. Therefore, the addition of FA can enhance the overall performance of high-water materials to a certain extent, and the long-term strength development of the material can satisfy engineering requirements.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"62 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vishal Thakur, Rupinder Singh, Ranvijay Kumar, Shubham Sharma, Sunpreet Singh, Changhe Li, Yanbin Zhang, Sayed M. Eldin, Sondos Abdullah Alqarni
3D printing is one of the plastic recycling processes that deliver a mechanically sustainable product and may be used for 4D printing applications, such as self-assembly, sensors, actuators, and other engineering applications. The success and implementation of 4D printing are dependent on the tendency of the shape memory with the action of external stimuli, such as heat, force, fields, light, and pH. Acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) are the most common materials for fused filament fabrication-based 3D printing processes. However, the low-shaped memory tendency on heating and weaker and less rigidity of ABS limit the application domains. PLA is an excellent responsive behavior when the action of heat has high stiffness. The incorporation of PLA into ABS is one of the solutions to tune the shape memory effect for better applicability in the 4D printing domain. In this study, the primary recycled PLA was incorporated into the primary recycled ABS matrix from 5 to 40% (weight%), and composites were made by extrusion in the form of cylindrical filaments for 4D printing. The tensile and shape memory properties of the recycled ABS–PLA composites were investigated to select the best combination. The results of the study were supported by fracture analysis by shape memory analysis, scanning electron microscopy, and optical microscopy. This study revealed that the prepared ABS–PLA-based composites have the potential to be applied in self-assembly applications.
{"title":"Mechanically sustainable and primary recycled thermo-responsive ABS–PLA polymer composites for 4D printing applications: Fabrication and studies","authors":"Vishal Thakur, Rupinder Singh, Ranvijay Kumar, Shubham Sharma, Sunpreet Singh, Changhe Li, Yanbin Zhang, Sayed M. Eldin, Sondos Abdullah Alqarni","doi":"10.1515/rams-2023-0149","DOIUrl":"https://doi.org/10.1515/rams-2023-0149","url":null,"abstract":"3D printing is one of the plastic recycling processes that deliver a mechanically sustainable product and may be used for 4D printing applications, such as self-assembly, sensors, actuators, and other engineering applications. The success and implementation of 4D printing are dependent on the tendency of the shape memory with the action of external stimuli, such as heat, force, fields, light, and pH. Acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) are the most common materials for fused filament fabrication-based 3D printing processes. However, the low-shaped memory tendency on heating and weaker and less rigidity of ABS limit the application domains. PLA is an excellent responsive behavior when the action of heat has high stiffness. The incorporation of PLA into ABS is one of the solutions to tune the shape memory effect for better applicability in the 4D printing domain. In this study, the primary recycled PLA was incorporated into the primary recycled ABS matrix from 5 to 40% (weight%), and composites were made by extrusion in the form of cylindrical filaments for 4D printing. The tensile and shape memory properties of the recycled ABS–PLA composites were investigated to select the best combination. The results of the study were supported by fracture analysis by shape memory analysis, scanning electron microscopy, and optical microscopy. This study revealed that the prepared ABS–PLA-based composites have the potential to be applied in self-assembly applications.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"44 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：