Hao Liu, Suleman Ayub Khan, Muhammad Nasir Amin, Fadi Althoey, Muhammad Tahir Qadir
The cementitious composite’s resistance to the introduction of harmful ions is the primary criterion that is used to evaluate its durability. The efficacy of glass and eggshell powder in cement mortar exposed to 5% sulfuric acid solutions was investigated in this study using artificial intelligence (AI)-aided approaches. Prediction models based on AI were built using experimental datasets with multi-expression programming (MEP) and gene expression programming (GEP) to forecast the percentage decrease in compressive strength (CS) after acid exposure. Furthermore, SHapley Additive exPlanations (SHAP) analysis was used to examine the significance of prospective constituents. The results of the experiments substantiated these models. High coefficient of determination (R2) values (MEP: 0.950 and GEP: 0.913) indicated statistical significance, meaning that test results and anticipated outcomes were consistent with each other and with the MEP and GEP models, respectively. According to SHAP analysis, the amount of eggshell and glass powder (GP) had the most significant link with CS loss after acid deterioration, showing a positive and negative correlation, respectively. In order to optimize efficiency and cost-effectiveness, the created models possess the capability to theoretically assess the decline in CS of GP-modified mortar across various input parameter values.
{"title":"Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions","authors":"Hao Liu, Suleman Ayub Khan, Muhammad Nasir Amin, Fadi Althoey, Muhammad Tahir Qadir","doi":"10.1515/rams-2024-0042","DOIUrl":"https://doi.org/10.1515/rams-2024-0042","url":null,"abstract":"The cementitious composite’s resistance to the introduction of harmful ions is the primary criterion that is used to evaluate its durability. The efficacy of glass and eggshell powder in cement mortar exposed to 5% sulfuric acid solutions was investigated in this study using artificial intelligence (AI)-aided approaches. Prediction models based on AI were built using experimental datasets with multi-expression programming (MEP) and gene expression programming (GEP) to forecast the percentage decrease in compressive strength (CS) after acid exposure. Furthermore, SHapley Additive exPlanations (SHAP) analysis was used to examine the significance of prospective constituents. The results of the experiments substantiated these models. High coefficient of determination (<jats:italic>R</jats:italic> <jats:sup>2</jats:sup>) values (MEP: 0.950 and GEP: 0.913) indicated statistical significance, meaning that test results and anticipated outcomes were consistent with each other and with the MEP and GEP models, respectively. According to SHAP analysis, the amount of eggshell and glass powder (GP) had the most significant link with CS loss after acid deterioration, showing a positive and negative correlation, respectively. In order to optimize efficiency and cost-effectiveness, the created models possess the capability to theoretically assess the decline in CS of GP-modified mortar across various input parameter values.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"6 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779430","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}
With the continuous upgrading of infrastructure construction and the gradual development of theoretical research about engineering construction, higher performance requirements have been put forward for concrete materials. Therefore, to meet the engineering quality requirements of various concrete structures, the research direction of engineering materials has shifted towards developing new concrete with high strength, high ductility, high toughness, and other multifunctional properties. Mixing two or more types of fibers with conductive properties with the cement matrix material allows various fibers to leverage their strengths and weaknesses, thereby utilizing their respective characteristics. This results in the formation of a complex-phase conductive fiber cementitious material (CFCM), which enhances the safety, durability, and toughness of the structure. It enables the engineering structure to exhibit intelligence and resourcefulness, thereby improving its service life and reducing the full life cycle cost of the cementitious material structure. Additionally, this approach relatively eases the demand for concrete materials and reduces material consumption. This method represents one of the research directions for new concrete. Complex-phase CFCMs are essentially smart materials capable of sensing not only compressive or tensile stresses but also temperature. The emergence of CFCM represents a significant step forward in enhancing the mechanics, functionality, and sustainability of modern infrastructure. In this experiment, an orthogonal test involving 16 working conditions with three factors and four levels was designed, with steel fiber (SF) type, SF content, and carbon fiber (CF) content as the factors. The study focused on the physical and mechanical properties of composite conductive fiber cement-based materials containing both SF and CF. Performance indicators such as flexural strength, volume resistivity, and energized temperature rise of the composite conductive fiber cement-based materials were tested. The analysis of orthogonal tests produced the following results regarding the degree of influence of each factor on the mechanical and physical properties: the order of influence on flexural strength was SF doping > SF type > CF doping. Further analysis revealed that the best combination was <jats:italic>A</jats:italic>4<jats:italic>B</jats:italic>4<jats:italic>C</jats:italic>4. The relationship between the effect of each factor on resistivity is as follows: carbon fiber doping > SF doping > SF type. Comparing the weights between the levels, it can be observed that the optimal combination of conductivity schemes is also <jats:italic>A</jats:italic>3<jats:italic>B</jats:italic>4<jats:italic>C</jats:italic>4. SF and CFs, respectively, enhanced the mechanical and physical properties of complex-phase conductive fiber cementitious materials. The results of the temperature rise test on cementitious materials concluded that there
随着基础设施建设水平的不断提高和工程建设理论研究的逐步发展,对混凝土材料的性能提出了更高的要求。因此,为满足各种混凝土结构的工程质量要求,工程材料的研究方向已转向开发具有高强度、高延展性、高韧性等多功能性能的新型混凝土。将两种或两种以上具有导电性能的纤维与水泥基体材料混合,可以使各种纤维扬长避短,从而发挥各自的特性。这样就形成了复相导电纤维水泥基材料(CFCM),从而提高了结构的安全性、耐久性和韧性。它使工程结构表现出智能性和资源性,从而提高其使用寿命,降低水泥基材料结构的全生命周期成本。此外,这种方法还能相对缓解对混凝土材料的需求,减少材料消耗。这种方法是新型混凝土的研究方向之一。复相 CFCM 本质上是一种智能材料,不仅能感应压应力或拉应力,还能感应温度。CFCM 的出现标志着在增强现代基础设施的力学、功能和可持续性方面向前迈出了重要一步。在本实验中,以钢纤维(SF)类型、SF 含量和碳纤维(CF)含量为因素,设计了一个涉及 16 种工作条件、三个因素和四个水平的正交试验。研究重点是含有 SF 和 CF 的复合导电纤维水泥基材料的物理和机械性能。测试了复合导电纤维水泥基材料的抗弯强度、体积电阻率和通电温升等性能指标。正交试验分析得出了各因素对力学和物理性能的影响程度:对抗弯强度的影响顺序为 SF 掺杂> SF 类型> CF 掺杂。进一步分析发现,最佳组合是 A4B4C4。各因素对电阻率的影响关系如下:碳纤维掺杂> SF掺杂> SF类型。比较各层次之间的权重,可以发现导电率方案的最佳组合也是 A3B4C4。SF 和 CF 分别提高了复相导电纤维水泥基材料的力学和物理性能。水泥基材料的温升试验结果表明,温升与导电率之间存在一定的关系。具体来说,导电率越高,观察到的温升就越大。通过对导电率的正交分析,不考虑不显著的影响因素 SF 类型对导电加热试验的影响,在 16 组条件下研究了 CF 掺杂和 SF 掺杂两个因素对加热试验的影响,并对数据进行了直观分析。通过正交分析和直观分析的综合优化,确定了试验的最佳混合比为 A3B4C4。这意味着,使用镀铜 SF(SF 用量为 1.25%,CF 用量为 0.48%)可获得最佳物理机械性能。作为智能混凝土领域的前沿研究,本实验探索了智能混凝土的研究路径,对后续更复杂的研究工作具有积极意义。
{"title":"Study on physical and mechanical properties of complex-phase conductive fiber cementitious materials","authors":"Jiuyang Li, Zhenwei Wang, Jinpeng Guo, Jingwei Luo, Xinmei Fan, Yuepeng Zhu","doi":"10.1515/rams-2024-0041","DOIUrl":"https://doi.org/10.1515/rams-2024-0041","url":null,"abstract":"With the continuous upgrading of infrastructure construction and the gradual development of theoretical research about engineering construction, higher performance requirements have been put forward for concrete materials. Therefore, to meet the engineering quality requirements of various concrete structures, the research direction of engineering materials has shifted towards developing new concrete with high strength, high ductility, high toughness, and other multifunctional properties. Mixing two or more types of fibers with conductive properties with the cement matrix material allows various fibers to leverage their strengths and weaknesses, thereby utilizing their respective characteristics. This results in the formation of a complex-phase conductive fiber cementitious material (CFCM), which enhances the safety, durability, and toughness of the structure. It enables the engineering structure to exhibit intelligence and resourcefulness, thereby improving its service life and reducing the full life cycle cost of the cementitious material structure. Additionally, this approach relatively eases the demand for concrete materials and reduces material consumption. This method represents one of the research directions for new concrete. Complex-phase CFCMs are essentially smart materials capable of sensing not only compressive or tensile stresses but also temperature. The emergence of CFCM represents a significant step forward in enhancing the mechanics, functionality, and sustainability of modern infrastructure. In this experiment, an orthogonal test involving 16 working conditions with three factors and four levels was designed, with steel fiber (SF) type, SF content, and carbon fiber (CF) content as the factors. The study focused on the physical and mechanical properties of composite conductive fiber cement-based materials containing both SF and CF. Performance indicators such as flexural strength, volume resistivity, and energized temperature rise of the composite conductive fiber cement-based materials were tested. The analysis of orthogonal tests produced the following results regarding the degree of influence of each factor on the mechanical and physical properties: the order of influence on flexural strength was SF doping > SF type > CF doping. Further analysis revealed that the best combination was <jats:italic>A</jats:italic>4<jats:italic>B</jats:italic>4<jats:italic>C</jats:italic>4. The relationship between the effect of each factor on resistivity is as follows: carbon fiber doping > SF doping > SF type. Comparing the weights between the levels, it can be observed that the optimal combination of conductivity schemes is also <jats:italic>A</jats:italic>3<jats:italic>B</jats:italic>4<jats:italic>C</jats:italic>4. SF and CFs, respectively, enhanced the mechanical and physical properties of complex-phase conductive fiber cementitious materials. The results of the temperature rise test on cementitious materials concluded that there ","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"61 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740480","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}
Fe nanoparticle-functionalized ordered mesoporous carbon (Fe0/OMC) was synthesized using triblock copolymers as templates and through solvent evaporation self-assembly, followed by a carbothermal reduction. Fe0/OMC had three microstructures of two-dimensional hexagonal (space group, p6mm, Fe0/OMC-1), body centered cubic (Im3̄m, Fe0/OMC-2), and face centered cubic (Fm3̄m, Fe0/OMC-3) which were controlled by simply adjusting the template. All Fe0/OMC displayed paramagnetic characteristics, with a maximum saturation magnetization of 50.1 emu·g−1. This high magnetization is advantageous for the swift extraction of the adsorbent from the solution following the adsorption process. Fe0/OMC was used as an adsorbent for the removal of silver ions (Ag(i)) from aqueous solutions, and the adsorption capacity of Fe0/OMC-1 was enhanced by the functionalization of Fe0. Adsorption property of Fe0/OMC-1 was significantly higher than that of Fe0/OMC-2 and Fe0/OMC-3, indicating that the long and straight ordered pore channels were more favorable for adsorption, and the adsorption capacity of Ag(i) on Fe0/OMC-1 was 233 mg·g−1. The adsorption process exhibited conformity with both the pseudo-second-order kinetic model and the Freundlich model, suggesting that the dominant mechanism of adsorption involved multilayer adsorption on heterogeneous surfaces.
{"title":"Fe nanoparticle-functionalized ordered mesoporous carbon with tailored mesostructures and their applications in magnetic removal of Ag(i)","authors":"Wenjuan Zhang, Yuheng Li, Mengyu Ran, Youliang Wang, Yezhi Ding, Bobo Zhang, Qiancheng Feng, Qianqian Chu, Yongqian Shen, Wang Sheng","doi":"10.1515/rams-2024-0007","DOIUrl":"https://doi.org/10.1515/rams-2024-0007","url":null,"abstract":"Fe nanoparticle-functionalized ordered mesoporous carbon (Fe<jats:sup>0</jats:sup>/OMC) was synthesized using triblock copolymers as templates and through solvent evaporation self-assembly, followed by a carbothermal reduction. Fe<jats:sup>0</jats:sup>/OMC had three microstructures of two-dimensional hexagonal (space group, p6mm, Fe<jats:sup>0</jats:sup>/OMC-1), body centered cubic (Im3̄m, Fe<jats:sup>0</jats:sup>/OMC-2), and face centered cubic (Fm3̄m, Fe<jats:sup>0</jats:sup>/OMC-3) which were controlled by simply adjusting the template. All Fe<jats:sup>0</jats:sup>/OMC displayed paramagnetic characteristics, with a maximum saturation magnetization of 50.1 emu·g<jats:sup>−1</jats:sup>. This high magnetization is advantageous for the swift extraction of the adsorbent from the solution following the adsorption process. Fe<jats:sup>0</jats:sup>/OMC was used as an adsorbent for the removal of silver ions (Ag(<jats:sc>i</jats:sc>)) from aqueous solutions, and the adsorption capacity of Fe<jats:sup>0</jats:sup>/OMC-1 was enhanced by the functionalization of Fe<jats:sup>0</jats:sup>. Adsorption property of Fe<jats:sup>0</jats:sup>/OMC-1 was significantly higher than that of Fe<jats:sup>0</jats:sup>/OMC-2 and Fe<jats:sup>0</jats:sup>/OMC-3, indicating that the long and straight ordered pore channels were more favorable for adsorption, and the adsorption capacity of Ag(<jats:sc>i</jats:sc>) on Fe<jats:sup>0</jats:sup>/OMC-1 was 233 mg·g<jats:sup>−1</jats:sup>. The adsorption process exhibited conformity with both the pseudo-second-order kinetic model and the Freundlich model, suggesting that the dominant mechanism of adsorption involved multilayer adsorption on heterogeneous surfaces.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"19 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717650","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}
Karina Kruse, Wolfgang Sauerwein, Jörn Lübben, Richard Dodel
Demographic change is causing society to age. At the same time, technological progress is changing the way ageing individuals are cared for and medically treated. Several smart wearables and garments have recently been developed for this purpose. Based on previous research, we see a research gap in the use of smart clothing in the care and support of elderly people, especially with regard to concrete application potentials and example products. The aim of this study was to provide an overview of the latest studies and developments in smart clothing with a focus on usability and acceptance for an elderly individuals. A systematic literature search was performed in five databases using a predefined set of keyword. A total of 169 articles published between 1/2000 and 2/2023 were identified and assessed. The literature search followed a previously prepared research protocol according to the criteria of a systematic literature search. The research field of smart clothing is expanding with smart shirts being a major focus; however other products are also being investigated, each with specific capabilities. In particular, vital parameters are constantly optimized; representative products are described and assessed according to their potential applicability to elderly people. The future applications of smart clothing in health care are promising. Many studies on basic applications of smart textiles have been done, and some studies have already involved older people. Furthermore, newly developed suggestions for possible categorizations of smart wearables as well as smart clothing as a subtype are presented based on the researched literature. We found an overall positive impression of the development and application of smart clothing, especially in geriatric settings. However, aspects such as data collection, skin compatibility, wearing comfort, and integration of geriatric factors into known acceptance models need further investigation. Over the last two decades, there have been many developments in the field of smart clothing. For the care and support of elderly people, smart clothing is an important development with great potential. Continued advancement in these products is needed to adequately address the special needs of older people.
{"title":"Smart technologies and textiles and their potential use and application in the care and support of elderly individuals: A systematic review","authors":"Karina Kruse, Wolfgang Sauerwein, Jörn Lübben, Richard Dodel","doi":"10.1515/rams-2023-0174","DOIUrl":"https://doi.org/10.1515/rams-2023-0174","url":null,"abstract":"Demographic change is causing society to age. At the same time, technological progress is changing the way ageing individuals are cared for and medically treated. Several smart wearables and garments have recently been developed for this purpose. Based on previous research, we see a research gap in the use of smart clothing in the care and support of elderly people, especially with regard to concrete application potentials and example products. The aim of this study was to provide an overview of the latest studies and developments in smart clothing with a focus on usability and acceptance for an elderly individuals. A systematic literature search was performed in five databases using a predefined set of keyword. A total of 169 articles published between 1/2000 and 2/2023 were identified and assessed. The literature search followed a previously prepared research protocol according to the criteria of a systematic literature search. The research field of smart clothing is expanding with smart shirts being a major focus; however other products are also being investigated, each with specific capabilities. In particular, vital parameters are constantly optimized; representative products are described and assessed according to their potential applicability to elderly people. The future applications of smart clothing in health care are promising. Many studies on basic applications of smart textiles have been done, and some studies have already involved older people. Furthermore, newly developed suggestions for possible categorizations of smart wearables as well as smart clothing as a subtype are presented based on the researched literature. We found an overall positive impression of the development and application of smart clothing, especially in geriatric settings. However, aspects such as data collection, skin compatibility, wearing comfort, and integration of geriatric factors into known acceptance models need further investigation. Over the last two decades, there have been many developments in the field of smart clothing. For the care and support of elderly people, smart clothing is an important development with great potential. Continued advancement in these products is needed to adequately address the special needs of older people.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"28 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612046","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}
Santhosh Nagaraja, Praveena Bindiganavile Anand, Madhusudhan Mariswamy, Meshel Q. Alkahtani, Saiful Islam, Mohammad Amir Khan, Wahaj Ahmad Khan, Javed Khan Bhutto
Friction stir welding (FSW) is increasingly utilized in aerospace for welding dissimilar Al–Mg alloys without melting, overcoming fusion welding challenges. This summary highlights FSW’s key aspects for dissimilar Al–Mg alloys and its aerospace relevance. These alloys are widely used in aerospace due to their beneficial properties, but fusion welding faces issues like brittle intermetallic compounds (IMC) and decreased mechanical properties. FSW addresses these challenges by using a rotating tool to generate frictional heat, plasticizing the material for solid-state joining without melting. This reduces IMC formation, enhancing joint strength and mechanical properties. Critical parameters like rotational speed, traverse speed, tool design, and process variables are emphasized for optimal FSW of dissimilar Al–Mg alloys. Joining these alloys is crucial in aerospace for applications such as aircraft structures, engine components, and fuel tanks. FSW offers advantages like weight reduction, improved fuel efficiency, and structural integrity enhancement. It allows welding dissimilar Al–Mg alloys with varying compositions for tailored material combinations meeting specific needs. In conclusion, FSW of dissimilar aluminum alloys is promising for aerospace, creating defect-free joints with improved mechanical properties. However, further research is needed to optimize parameters, explore tool designs, and validate long-term performance in aerospace environments.
{"title":"Friction stir welding of dissimilar Al–Mg alloys for aerospace applications: Prospects and future potential","authors":"Santhosh Nagaraja, Praveena Bindiganavile Anand, Madhusudhan Mariswamy, Meshel Q. Alkahtani, Saiful Islam, Mohammad Amir Khan, Wahaj Ahmad Khan, Javed Khan Bhutto","doi":"10.1515/rams-2024-0033","DOIUrl":"https://doi.org/10.1515/rams-2024-0033","url":null,"abstract":"Friction stir welding (FSW) is increasingly utilized in aerospace for welding dissimilar Al–Mg alloys without melting, overcoming fusion welding challenges. This summary highlights FSW’s key aspects for dissimilar Al–Mg alloys and its aerospace relevance. These alloys are widely used in aerospace due to their beneficial properties, but fusion welding faces issues like brittle intermetallic compounds (IMC) and decreased mechanical properties. FSW addresses these challenges by using a rotating tool to generate frictional heat, plasticizing the material for solid-state joining without melting. This reduces IMC formation, enhancing joint strength and mechanical properties. Critical parameters like rotational speed, traverse speed, tool design, and process variables are emphasized for optimal FSW of dissimilar Al–Mg alloys. Joining these alloys is crucial in aerospace for applications such as aircraft structures, engine components, and fuel tanks. FSW offers advantages like weight reduction, improved fuel efficiency, and structural integrity enhancement. It allows welding dissimilar Al–Mg alloys with varying compositions for tailored material combinations meeting specific needs. In conclusion, FSW of dissimilar aluminum alloys is promising for aerospace, creating defect-free joints with improved mechanical properties. However, further research is needed to optimize parameters, explore tool designs, and validate long-term performance in aerospace environments.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"52 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141584934","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}
Syed M. Hussain, Rujda Parveen, Nek Muhammad Katbar, Sadique Rehman, Assmaa Abd-Elmonem, Nesreen Sirelkhtam Elmki Abdalla, Hijaz Ahmad, Muhammad Amer Qureshi, Wasim Jamshed, Ayesha Amjad, Rabha W. Ibrahim
This work examines the behaviour of flow and heat transmission in the presence of hybrid nanofluid in thermal radiation, heat generation, and magnetohydrodynamics. The hybrid state in this model is represented by two different fluids, TiO2 (titanium dioxide) and Ag (silver). The enclosure is wavy and slanted, with curving walls on the left and right. The finite difference approximation method was utilized to resolve the fundamental equations after they were non-dimensionalized, which are further reduced to a fourth-order bi-harmonic equation and are numerically solved based on the biconjugate gradient-stabilized approach method. The simulations are performed with various Rayleigh numbers, Hartmann numbers, an inclination angle of the enclosure, radiation parameters, heat generation parameters, inclination angle of the magnetic field, and volume fraction of hybrid nanoparticles. The streamlines, isotherms, and average Nusselt number contours are used to depict the thermo-fluid patterns. The findings show that the average Nusselt number relies on ϕ and increases as ϕ rises. The investigation’s findings demonstrated that the transfer of heat on the heated bottom wall significantly increases with the Rayleigh number (Ra = 105 and 106). At a cavity inclination of 45°, interesting multi-vortex structures are observed. The results of this study may enhance the effectiveness of solar collectors, heat exchangers, and other similar systems that depend on convective heat transfer in nature.
{"title":"Entropy generation analysis of MHD convection flow of hybrid nanofluid in a wavy enclosure with heat generation and thermal radiation","authors":"Syed M. Hussain, Rujda Parveen, Nek Muhammad Katbar, Sadique Rehman, Assmaa Abd-Elmonem, Nesreen Sirelkhtam Elmki Abdalla, Hijaz Ahmad, Muhammad Amer Qureshi, Wasim Jamshed, Ayesha Amjad, Rabha W. Ibrahim","doi":"10.1515/rams-2024-0037","DOIUrl":"https://doi.org/10.1515/rams-2024-0037","url":null,"abstract":"This work examines the behaviour of flow and heat transmission in the presence of hybrid nanofluid in thermal radiation, heat generation, and magnetohydrodynamics. The hybrid state in this model is represented by two different fluids, TiO<jats:sub>2</jats:sub> (titanium dioxide) and Ag (silver). The enclosure is wavy and slanted, with curving walls on the left and right. The finite difference approximation method was utilized to resolve the fundamental equations after they were non-dimensionalized, which are further reduced to a fourth-order bi-harmonic equation and are numerically solved based on the biconjugate gradient-stabilized approach method. The simulations are performed with various Rayleigh numbers, Hartmann numbers, an inclination angle of the enclosure, radiation parameters, heat generation parameters, inclination angle of the magnetic field, and volume fraction of hybrid nanoparticles. The streamlines, isotherms, and average Nusselt number contours are used to depict the thermo-fluid patterns. The findings show that the average Nusselt number relies on <jats:italic>ϕ</jats:italic> and increases as <jats:italic>ϕ</jats:italic> rises. The investigation’s findings demonstrated that the transfer of heat on the heated bottom wall significantly increases with the Rayleigh number (Ra = 10<jats:sup>5</jats:sup> and 10<jats:sup>6</jats:sup>). At a cavity inclination of 45°, interesting multi-vortex structures are observed. The results of this study may enhance the effectiveness of solar collectors, heat exchangers, and other similar systems that depend on convective heat transfer in nature.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"27 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568174","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}
Shuailong Lian, Wen Wan, Yanlin Zhao, Wenqing Peng, Can Du, Hao Hu
Investigating the damage degradation of rock during the freezing and thawing process is more consistent with the actual engineering environment, considering its internal initial damage. In this study, the effects of initial damage from preloading and subzero-temperature freezing–thawing on microscopic and macroscopic mechanical properties of sandstone were studied based on the nuclear magnetic resonance (NMR) technique. The results show that the P-wave velocity of the sample decreased, while the porosity increased as the initial damage level increased. The distribution of T2 signal intensity exposed to the low-temperature freezing–thawing–saturation treatment was rather larger than that under normal temperature conditions for samples with different levels of initial damage from preloading, indicating that the low-temperature freezing–thawing condition would promote the porosity and have an obvious increase in damage. A continuum damage model considering subzero-temperature freezing–thawing damage from different pore sizes was finally introduced to describe the damage evolution mechanism of sandstone. The research results can be used to quantitatively evaluate the damage evolution mechanism of sandstone treated by subzero-temperature freezing–thawing without mechanical tests. Furthermore, the analysis and research results show that the damage variables of sandstone obtained by the NMR theory were lower than those of classical rock mechanics theory.
考虑到岩石内部的初始损伤,研究冻融过程中岩石的损伤退化更符合实际工程环境。本研究基于核磁共振(NMR)技术,研究了预加载和零下温度冻融的初始损伤对砂岩微观和宏观力学性能的影响。结果表明,随着初始损伤程度的增加,样品的 P 波速度降低,而孔隙率增加。对于不同预加载初始损伤程度的样品,经低温冻融-饱和处理后的 T2 信号强度分布比常温条件下的信号强度分布要大,表明低温冻融条件会促进孔隙率的增加,并有明显的损伤加剧。最后引入了考虑不同孔隙大小的亚零度冻融损伤的连续损伤模型来描述砂岩的损伤演化机制。研究结果可用于定量评估未经力学试验的亚零度冻融处理砂岩的损伤演变机制。此外,分析和研究结果表明,核磁共振理论得到的砂岩损伤变量低于经典岩石力学理论。
{"title":"Study on the damage mechanism and evolution model of preloaded sandstone subjected to freezing–thawing action based on the NMR technology","authors":"Shuailong Lian, Wen Wan, Yanlin Zhao, Wenqing Peng, Can Du, Hao Hu","doi":"10.1515/rams-2024-0034","DOIUrl":"https://doi.org/10.1515/rams-2024-0034","url":null,"abstract":"Investigating the damage degradation of rock during the freezing and thawing process is more consistent with the actual engineering environment, considering its internal initial damage. In this study, the effects of initial damage from preloading and subzero-temperature freezing–thawing on microscopic and macroscopic mechanical properties of sandstone were studied based on the nuclear magnetic resonance (NMR) technique. The results show that the P-wave velocity of the sample decreased, while the porosity increased as the initial damage level increased. The distribution of T<jats:sub>2</jats:sub> signal intensity exposed to the low-temperature freezing–thawing–saturation treatment was rather larger than that under normal temperature conditions for samples with different levels of initial damage from preloading, indicating that the low-temperature freezing–thawing condition would promote the porosity and have an obvious increase in damage. A continuum damage model considering subzero-temperature freezing–thawing damage from different pore sizes was finally introduced to describe the damage evolution mechanism of sandstone. The research results can be used to quantitatively evaluate the damage evolution mechanism of sandstone treated by subzero-temperature freezing–thawing without mechanical tests. Furthermore, the analysis and research results show that the damage variables of sandstone obtained by the NMR theory were lower than those of classical rock mechanics theory.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"19 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552032","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 place of Portland cement concrete, alkali-activated materials (AAMs) are becoming more popular because of their widespread use and low environmental effects. Unfortunately, reliable property predictions have been impeded by the restrictions of conventional materials science methods and the large compositional variability of AAMs. A support vector machine (SVM), a bagging regressor (BR), and a random forest regressor (RFR) were among the machine learning models developed in this study to assess the compressive strength (CS) of AAMs in an effort to gain an answer to this topic. Improving predictions in this crucial area was the goal of this study, which used a large dataset with 381 points and eight input factors. Also, the relevance of contributing components was assessed using a shapley additive explanations (SHAP) approach. In terms of predicting AAMs CS, RFR outperformed BR and SVM. Compared to the RFR model’s 0.96 R2, the SVM and BR models’ R2-values were 0.89 and 0.93, respectively. In addition, the RFR model’s greater accuracy was indicated by an average absolute error value of 4.08 MPa compared to the SVM’s 6.80 MPa and the BR’s 5.83 MPa, which provided further proof of their validity. According to the outcomes of the SHAP research, the two factors that contributed the most beneficially to the strength were aggregate volumetric ratio and reactivity. The factors that contributed the most negatively were specific surface area, silicate modulus, and sodium hydroxide concentration. Using the produced models to find the CS of AAMs for various input parameter values can help cut down on costly and time-consuming laboratory testing. In order to find the best amounts of raw materials for AAMs, academics and industries could find this SHAP study useful.
{"title":"Promoting low carbon construction using alkali-activated materials: A modeling study for strength prediction and feature interaction","authors":"Xiaofeng Liu, Yanli Wang, Chengyuan Lu","doi":"10.1515/rams-2024-0038","DOIUrl":"https://doi.org/10.1515/rams-2024-0038","url":null,"abstract":"In place of Portland cement concrete, alkali-activated materials (AAMs) are becoming more popular because of their widespread use and low environmental effects. Unfortunately, reliable property predictions have been impeded by the restrictions of conventional materials science methods and the large compositional variability of AAMs. A support vector machine (SVM), a bagging regressor (BR), and a random forest regressor (RFR) were among the machine learning models developed in this study to assess the compressive strength (CS) of AAMs in an effort to gain an answer to this topic. Improving predictions in this crucial area was the goal of this study, which used a large dataset with 381 points and eight input factors. Also, the relevance of contributing components was assessed using a shapley additive explanations (SHAP) approach. In terms of predicting AAMs CS, RFR outperformed BR and SVM. Compared to the RFR model’s 0.96 <jats:italic>R</jats:italic> <jats:sup>2</jats:sup>, the SVM and BR models’ <jats:italic>R</jats:italic> <jats:sup>2</jats:sup>-values were 0.89 and 0.93, respectively. In addition, the RFR model’s greater accuracy was indicated by an average absolute error value of 4.08 MPa compared to the SVM’s 6.80 MPa and the BR’s 5.83 MPa, which provided further proof of their validity. According to the outcomes of the SHAP research, the two factors that contributed the most beneficially to the strength were aggregate volumetric ratio and reactivity. The factors that contributed the most negatively were specific surface area, silicate modulus, and sodium hydroxide concentration. Using the produced models to find the CS of AAMs for various input parameter values can help cut down on costly and time-consuming laboratory testing. In order to find the best amounts of raw materials for AAMs, academics and industries could find this SHAP study useful.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"130 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552027","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}
Ahmed A. Alawi Al-Naghi, Muhammad Nasir Amin, Suleman Ayub Khan, Muhammad Tahir Qadir
The mechanical strength of geopolymer concrete incorporating corncob ash and slag (SCA-GPC) was estimated by means of three distinct AI methods: a support vector machine (SVM), two ensemble methods called bagging regressor (BR), and random forest regressor (RFR). The developed models were validated using statistical tests, absolute error assessment, and the coefficient of determination (R2). The importance of various modeling factors was determined by means of interaction diagrams. When estimating the flexural strength and compressive strength of SCA-GPC, R2 values of over 0.85 were measured between the actual and predicted findings using both individual and ensemble AI models. Statistical testing and k-fold analysis for error evaluation revealed that the RFR model outperformed the SVM and BR models in terms of accuracy. As demonstrated by the interaction graphs, the mechanical characteristics of SCA-GPC were found to be extremely responsive to the mix proportions of ground granulated blast furnace slag, fine aggregate, and corncob ash. This was the case for all three components. This study demonstrated that highly precise estimations of mechanical properties for SCA-GPC can be made using ensemble AI techniques. Improvements in geopolymer concrete performance can be achieved by the implementation of such practices.
通过三种不同的人工智能方法估算了掺入玉米芯灰和矿渣的土工聚合物混凝土(SCA-GPC)的机械强度:支持向量机(SVM)、两种称为袋式回归器(BR)和随机森林回归器(RFR)的集合方法。使用统计测试、绝对误差评估和判定系数(R 2)对所开发的模型进行了验证。通过交互图确定了各种建模因素的重要性。在估算 SCA-GPC 的抗弯强度和抗压强度时,使用单个和组合人工智能模型测得的实际结果和预测结果之间的 R 2 值均超过 0.85。用于误差评估的统计测试和 k 倍分析表明,RFR 模型的准确性优于 SVM 和 BR 模型。如交互图所示,SCA-GPC 的机械特性对磨细高炉矿渣、细骨料和玉米芯灰的混合比例反应极快。所有三种成分都是如此。这项研究表明,使用集合人工智能技术可以对 SCA-GPC 的机械性能进行高度精确的估算。采用这种方法可以改善土工聚合物混凝土的性能。
{"title":"Modeling the strength parameters of agro waste-derived geopolymer concrete using advanced machine intelligence techniques","authors":"Ahmed A. Alawi Al-Naghi, Muhammad Nasir Amin, Suleman Ayub Khan, Muhammad Tahir Qadir","doi":"10.1515/rams-2024-0035","DOIUrl":"https://doi.org/10.1515/rams-2024-0035","url":null,"abstract":"The mechanical strength of geopolymer concrete incorporating corncob ash and slag (SCA-GPC) was estimated by means of three distinct AI methods: a support vector machine (SVM), two ensemble methods called bagging regressor (BR), and random forest regressor (RFR). The developed models were validated using statistical tests, absolute error assessment, and the coefficient of determination (<jats:italic>R</jats:italic> <jats:sup>2</jats:sup>). The importance of various modeling factors was determined by means of interaction diagrams. When estimating the flexural strength and compressive strength of SCA-GPC, <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> values of over 0.85 were measured between the actual and predicted findings using both individual and ensemble AI models. Statistical testing and <jats:italic>k</jats:italic>-fold analysis for error evaluation revealed that the RFR model outperformed the SVM and BR models in terms of accuracy. As demonstrated by the interaction graphs, the mechanical characteristics of SCA-GPC were found to be extremely responsive to the mix proportions of ground granulated blast furnace slag, fine aggregate, and corncob ash. This was the case for all three components. This study demonstrated that highly precise estimations of mechanical properties for SCA-GPC can be made using ensemble AI techniques. Improvements in geopolymer concrete performance can be achieved by the implementation of such practices.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"58 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507009","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}
Geopolymer concrete (GPC) serves as an environmentally conscious alternative to traditional concrete, offering a sustainable solution for construction needs. The ability to make on-site changes is dependent on the concrete’s strength after casting, which must be higher than the target value. To anticipate the concrete’s strength before it is poured is, thus, quite helpful. Three ensemble machine learning (ML) approaches, including gradient boosting, AdaBoost regressor, and extreme gradient boosting, are presented in this work as potential methods for forecasting GPC’s mechanical strength that incorporates corncob ash. To determine which modeling parameters are crucial, sensitivity analysis was employed. When the compressive strength and split-tensile strength of GPC were tested with ensemble ML models, R2 values of more than 90% were discovered between the predicted and actual results. Statistics and a k-fold analysis based on the error and coefficient of determination were used to verify the developed models. Slag amount, curing age, and fine aggregate quantity were the three mix proportions that had the most impact on GPC’s mechanical strength, as shown in the sensitivity analysis. The results of this study demonstrated that ensemble boosting approaches could reliably estimate GPC mechanical strength. Incorporating such procedures into GPC quality control can yield significant improvements.
土工聚合物混凝土(GPC)是一种具有环保意识的传统混凝土替代品,可为建筑需求提供可持续的解决方案。现场改造的能力取决于混凝土浇筑后的强度,而混凝土的强度必须高于目标值。因此,在浇筑前预测混凝土的强度非常有用。本研究提出了三种集合机器学习(ML)方法,包括梯度提升法、AdaBoost 回归法和极端梯度提升法,作为预测含有玉米芯灰的 GPC 机械强度的潜在方法。为确定哪些建模参数至关重要,采用了敏感性分析。当使用集合 ML 模型测试 GPC 的抗压强度和劈裂拉伸强度时,发现预测结果和实际结果之间的 R 2 值超过 90%。统计和基于误差和判定系数的 k 倍分析被用来验证所开发的模型。如敏感性分析所示,炉渣量、固化龄期和细集料量是对 GPC 机械强度影响最大的三种混合比例。这项研究的结果表明,集合提升方法可以可靠地估算 GPC 的机械强度。将此类程序纳入 GPC 质量控制可带来显著改善。
{"title":"Compressive and tensile strength estimation of sustainable geopolymer concrete using contemporary boosting ensemble techniques","authors":"Ji Zhou, Qiong Tian, Ayaz Ahmad, Jiandong Huang","doi":"10.1515/rams-2024-0014","DOIUrl":"https://doi.org/10.1515/rams-2024-0014","url":null,"abstract":"Geopolymer concrete (GPC) serves as an environmentally conscious alternative to traditional concrete, offering a sustainable solution for construction needs. The ability to make on-site changes is dependent on the concrete’s strength after casting, which must be higher than the target value. To anticipate the concrete’s strength before it is poured is, thus, quite helpful. Three ensemble machine learning (ML) approaches, including gradient boosting, AdaBoost regressor, and extreme gradient boosting, are presented in this work as potential methods for forecasting GPC’s mechanical strength that incorporates corncob ash. To determine which modeling parameters are crucial, sensitivity analysis was employed. When the compressive strength and split-tensile strength of GPC were tested with ensemble ML models, <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> values of more than 90% were discovered between the predicted and actual results. Statistics and a <jats:italic>k</jats:italic>-fold analysis based on the error and coefficient of determination were used to verify the developed models. Slag amount, curing age, and fine aggregate quantity were the three mix proportions that had the most impact on GPC’s mechanical strength, as shown in the sensitivity analysis. The results of this study demonstrated that ensemble boosting approaches could reliably estimate GPC mechanical strength. Incorporating such procedures into GPC quality control can yield significant improvements.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"42 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141197864","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}