Utilizing waste material in concrete production is an effective way to eliminate waste and develop environmentally friendly building materials. An experimental investigation was carried out to examine the physical, mechanical and durability characteristics of Polypropylene (PP) fibers foamed concrete (FC) that contains waste marble powder (WMP) as a substitute for conventional cement. Seven different FC mixtures were made utilizing WMD as a cement substitution at the rates of 0, 5, 10, 15, 20, 25 and 30%. Fresh properties of mixtures were investigated by performing slump and fresh density tests. Several tests that evaluated the mechanical strengths were conducted at 7 and 28 days. Moreover, the durability of FC specimens at high temperatures was also examined. Results indicate that by including WMD into FC mixtures, the desired physical, mechanical, and durability characteristics could be obtained. The highest compressive, splitting tensile, and flexural strength were 27.4, 2.34, and 4.2 MPa, respectively, with significant improvements of 39.01, 14.7, and 25% at 28 days. Furthermore, all FC specimens containing WMD showed satisfactory performance at temperatures of 300 ᵒ C and 600 ᵒ C.
{"title":"Experimental Investigation of Physical and Mechanical Characteristics of Structural Foamed Concrete Containing Waste Marble Dust","authors":"Aya Qatawna, Hamza Mobideen","doi":"10.30919/es906","DOIUrl":"https://doi.org/10.30919/es906","url":null,"abstract":"Utilizing waste material in concrete production is an effective way to eliminate waste and develop environmentally friendly building materials. An experimental investigation was carried out to examine the physical, mechanical and durability characteristics of Polypropylene (PP) fibers foamed concrete (FC) that contains waste marble powder (WMP) as a substitute for conventional cement. Seven different FC mixtures were made utilizing WMD as a cement substitution at the rates of 0, 5, 10, 15, 20, 25 and 30%. Fresh properties of mixtures were investigated by performing slump and fresh density tests. Several tests that evaluated the mechanical strengths were conducted at 7 and 28 days. Moreover, the durability of FC specimens at high temperatures was also examined. Results indicate that by including WMD into FC mixtures, the desired physical, mechanical, and durability characteristics could be obtained. The highest compressive, splitting tensile, and flexural strength were 27.4, 2.34, and 4.2 MPa, respectively, with significant improvements of 39.01, 14.7, and 25% at 28 days. Furthermore, all FC specimens containing WMD showed satisfactory performance at temperatures of 300 ᵒ C and 600 ᵒ C.","PeriodicalId":36059,"journal":{"name":"Engineered Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90329999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Herein, molecular dynamics simulations were performed to investigate the structure and slip behavior of ⟨𝑐 + 𝑎⟩ edge dislocations on the pyramidal-I (Pyr-I) plane in magnesium (Mg), which were compared to those on the pyramidal-II (Pyr-II) plane. ⟨𝑐 + 𝑎⟩ dislocations on pyramidal planes are metastable and transition into sessile, typically sessile 〈 c 〉 and glissile 〈 a 〉 basal dislocations (basal-dissociated ⟨𝑐⟩ + basal ⟨𝑎⟩ ), or a dissociated ⟨𝑐 + 𝑎⟩ dislocation along the basal plane (basal-dissociated ⟨𝑐 + 𝑎⟩ and its derivative structure). This transition occurs at temperatures of >100 and >400 K for Pyr-I and -II ⟨𝑐 + 𝑎⟩ edge dislocations, respectively, in the absence of shear deformation along the slip direction, except under large non-glide stresses. The critical resolved shear stress (CRSS) of the slip plane where Pyr-I ⟨𝑐 + 𝑎⟩ edge dislocations glide at 10 K increases with increasing compressive or tensile strains normal to the slip plane and exhibits a minimum value of ~486 MPa. Similarly, the CRSS for Pyr-II ⟨𝑐 + 𝑎⟩ edge dislocations decreases with increasing compressive strains normal to the slip plane and exhibits a maximum value of ~149 MPa at 10 K. Our findings provide insights into the design of ductile Mg alloys.
在此，进行分子动力学模拟以研究镁(Mg)中金字塔- i (Pyr-I)平面上的⟨𝑐+𝑎⟩边缘位错的结构和滑移行为，并将其与金字塔- ii (Pyr-II)平面上的位错进行比较。锥体面上的⟨𝑐+𝑎⟩错位是亚稳的，并且转变为无梗的，通常为无梗的< c >和滑裂的< a >基底错位(基底-dissociated⟨𝑐⟩+基底⟨𝑎⟩)，或沿基底平面的dissociated⟨𝑐+𝑎⟩错位(基底-dissociated⟨𝑐+𝑎⟩及其衍生结构)。这种转变分别发生在Pyr-I和-II⟨𝑐+𝑎⟩边位错的>100和>400 K的温度下，在沿着滑移方向没有剪切变形的情况下，除非在大的非滑动应力下。Pyr-I⟨𝑐+𝑎⟩边缘位错在10 K时滑动的滑移面的临界分解剪切应力(CRSS)随着向滑移面垂直的压缩或拉伸应变的增加而增加，并显示最小值为~486 MPa。类似地，Pyr-II⟨𝑐+𝑎⟩边位错的CRSS随着向滑移面垂直的压缩应变的增加而减少，并且在10 K时显示最大值为~149 MPa。我们的发现为延展性镁合金的设计提供了见解。
{"title":"Influence of Nonglide Stress on the Structure and Mobility of Pyramidal-I and -II ⟨c + a⟩ Edge Dislocations in Magnesium","authors":"S. Oyinbo, R. Matsumoto, D. Matsunaka, T. Jen","doi":"10.30919/es931","DOIUrl":"https://doi.org/10.30919/es931","url":null,"abstract":"Herein, molecular dynamics simulations were performed to investigate the structure and slip behavior of ⟨𝑐 + 𝑎⟩ edge dislocations on the pyramidal-I (Pyr-I) plane in magnesium (Mg), which were compared to those on the pyramidal-II (Pyr-II) plane. ⟨𝑐 + 𝑎⟩ dislocations on pyramidal planes are metastable and transition into sessile, typically sessile 〈 c 〉 and glissile 〈 a 〉 basal dislocations (basal-dissociated ⟨𝑐⟩ + basal ⟨𝑎⟩ ), or a dissociated ⟨𝑐 + 𝑎⟩ dislocation along the basal plane (basal-dissociated ⟨𝑐 + 𝑎⟩ and its derivative structure). This transition occurs at temperatures of >100 and >400 K for Pyr-I and -II ⟨𝑐 + 𝑎⟩ edge dislocations, respectively, in the absence of shear deformation along the slip direction, except under large non-glide stresses. The critical resolved shear stress (CRSS) of the slip plane where Pyr-I ⟨𝑐 + 𝑎⟩ edge dislocations glide at 10 K increases with increasing compressive or tensile strains normal to the slip plane and exhibits a minimum value of ~486 MPa. Similarly, the CRSS for Pyr-II ⟨𝑐 + 𝑎⟩ edge dislocations decreases with increasing compressive strains normal to the slip plane and exhibits a maximum value of ~149 MPa at 10 K. Our findings provide insights into the design of ductile Mg alloys.","PeriodicalId":36059,"journal":{"name":"Engineered Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89080493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Akhmetzhan, B. Bilashev, K. Ikhsanov, G. Kalesheva, Ainash Mukambetkaliyeva
{"title":"Optimization of Drill Winch Brake Cooling System for Improved Working Process Parameter","authors":"S. Akhmetzhan, B. Bilashev, K. Ikhsanov, G. Kalesheva, Ainash Mukambetkaliyeva","doi":"10.30919/es8d881","DOIUrl":"https://doi.org/10.30919/es8d881","url":null,"abstract":"","PeriodicalId":36059,"journal":{"name":"Engineered Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76699816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aparna Ashok, A. Desai, R. Mahadeva, S. Patole, Brajesh Pandey, Neeru Bhagat
Heusler alloys are an incredible class of inter-metallic materials with different compositions and over 1500 members. Though discovered a century back, they are an active area of physics and material science research. Novel properties and potential fields of applications materialize constantly. Even the alloy system is extensively investigated owing to its shape memory behavior and prospective relevance in the development of actuator devices, where strains are controlled by applying an external magnetic field. Heusler alloys are currently the material of interest due to their properties leading to their use as shape memory alloys and topological insulators. Hence, predicting and determining their composition and structure is imperative before synthesis. Utilizing the conventional method in determining the possible changes in the properties and the structure of the proposed compositions is tedious and time-consuming. In the current consumerism-driven environment, we require a faster method to predict the structure of the proposed alloy or compound or other parameters for the desired application. Once the prediction is made, it must be tested experimentally by synthesizing the material and characterizing its behavior. This analysis is focusing on network analysis with a supervised machine learning approach to study the properties of Heusler alloys with their application as shape memory alloys.
{"title":"Research Network Analysis and Machine Learning on Heusler Alloys","authors":"Aparna Ashok, A. Desai, R. Mahadeva, S. Patole, Brajesh Pandey, Neeru Bhagat","doi":"10.30919/es954","DOIUrl":"https://doi.org/10.30919/es954","url":null,"abstract":"Heusler alloys are an incredible class of inter-metallic materials with different compositions and over 1500 members. Though discovered a century back, they are an active area of physics and material science research. Novel properties and potential fields of applications materialize constantly. Even the alloy system is extensively investigated owing to its shape memory behavior and prospective relevance in the development of actuator devices, where strains are controlled by applying an external magnetic field. Heusler alloys are currently the material of interest due to their properties leading to their use as shape memory alloys and topological insulators. Hence, predicting and determining their composition and structure is imperative before synthesis. Utilizing the conventional method in determining the possible changes in the properties and the structure of the proposed compositions is tedious and time-consuming. In the current consumerism-driven environment, we require a faster method to predict the structure of the proposed alloy or compound or other parameters for the desired application. Once the prediction is made, it must be tested experimentally by synthesizing the material and characterizing its behavior. This analysis is focusing on network analysis with a supervised machine learning approach to study the properties of Heusler alloys with their application as shape memory alloys.","PeriodicalId":36059,"journal":{"name":"Engineered Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72619722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current work deals with the efficacy of the bionic-inspired airfoil of Tyto Alba (a barn owl) in noise reduction. Due to their distinct wing morphology, owls are noted for their quiet flying, which is astonishingly low-noise both when gliding and flapping. The low-noise operation of airfoil is inspired by these remarkable characteristics of owl flight. Research conducted in the past suggests that an airfoil that has an extensive sinusoidal profile can only reduce noise to a certain extent. As a coupling element, an owl-wing-inspired ridge is added to the trailing edge of airfoils with serrations in this work. This proposed method of noise reduction using trailing edge serrations showed the efficacy of a bionic-inspired airfoil with the existing approaches. A numerical study was performed using computational tools and has shown that the proposed bionic-inspired structure could reduce noise more effectively. The results show that wide wavelengths have less low-frequency tonal noise but more at high frequencies. This paper concludes that owl-inspired trailing edge serrations may be an effective aero-acoustic control device for wind turbines, aircraft, drones, and other fluid machines.
{"title":"Noise Reduction in a Bird Inspired Aero Foil using Trailing-Edge Serrations","authors":"Shiva Prasad U U, N. R","doi":"10.30919/es951","DOIUrl":"https://doi.org/10.30919/es951","url":null,"abstract":"The current work deals with the efficacy of the bionic-inspired airfoil of Tyto Alba (a barn owl) in noise reduction. Due to their distinct wing morphology, owls are noted for their quiet flying, which is astonishingly low-noise both when gliding and flapping. The low-noise operation of airfoil is inspired by these remarkable characteristics of owl flight. Research conducted in the past suggests that an airfoil that has an extensive sinusoidal profile can only reduce noise to a certain extent. As a coupling element, an owl-wing-inspired ridge is added to the trailing edge of airfoils with serrations in this work. This proposed method of noise reduction using trailing edge serrations showed the efficacy of a bionic-inspired airfoil with the existing approaches. A numerical study was performed using computational tools and has shown that the proposed bionic-inspired structure could reduce noise more effectively. The results show that wide wavelengths have less low-frequency tonal noise but more at high frequencies. This paper concludes that owl-inspired trailing edge serrations may be an effective aero-acoustic control device for wind turbines, aircraft, drones, and other fluid machines.","PeriodicalId":36059,"journal":{"name":"Engineered Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73613451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luxi Lu, Tianke Zhang, Xiaoliang Wang, Yu Han, D. Sridhar, Handong Li, B. Xu, Kuzin Victor Fedorovich, Xianmin Mai
{"title":"Evaluation and Analysis of the Architectural Environment of Traditional Folk Houses in Tibetan plateau, China","authors":"Luxi Lu, Tianke Zhang, Xiaoliang Wang, Yu Han, D. Sridhar, Handong Li, B. Xu, Kuzin Victor Fedorovich, Xianmin Mai","doi":"10.30919/es8d845","DOIUrl":"https://doi.org/10.30919/es8d845","url":null,"abstract":"","PeriodicalId":36059,"journal":{"name":"Engineered Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90931023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Divesh Kumar, P. Samui, Warit Wipulanusat, S. Keawsawasvong, Kongtawan Sangjinda, Wittaya Jitchaijaroen
A crucial characteristic of real-world engineering operations is a strip footing's bearing capacity on a rock mass subjected to incline and eccentric loading conditions. Many scientists have attempted to establish and implement artificial intelligence (AI) models for estimating strip footings’ bearing capacity. In this study, four data-driven models, namely, extreme gradient boosting (XGBoost), random forest (RF), deep neural network (DNN), and long short-term memory (LSTM), are developed and compared to calculate the strip footing's bearing capacity. The strip footing's bearing capacity is obtained numerically by performing a lower bound (LB) and upper bound (UB) finite element limit analysis (FELA) for the purpose of training machine learning models. A total of 5120 FELA solutions with six dimensionless input parameters, namely, the geological strength index ( GSI ), the yield parameter ( m i ), the dimensionless strength ( 𝛾 B/ 𝜎 ci ) , inclination angle ( 𝛽 ), the dimensionless eccentricity ( e/B ), and the adhesion factor ( a ), and one output parameter, the bearing capacity factor ( P/ 𝜎 ci B ), were utilized in the analysis. The results show that the efficiency of all the proposed models is sufficient for bearing capacity factor determination, with coefficient of determination ( R 2 ) values ranging from 0.87 to 0.997 in the training phase and 0.975 to 0.999 in the testing phase. The proposed XGBoost model outperforms other models, such as RF, DNN, and LSTM, and can be used accurately for estimating a strip footing's bearing capacity on rock mass subjected to incline and eccentric loading loads.
在实际工程操作中，条形基础在倾斜和偏心荷载条件下的承载能力是一个重要的特征。许多科学家试图建立和实现人工智能(AI)模型来估计条形基础的承载力。本文采用极端梯度增强(XGBoost)、随机森林(RF)、深度神经网络(DNN)和长短期记忆(LSTM)四种数据驱动模型进行了条形基础承载力计算，并进行了对比。通过进行下限(LB)和上限(UB)有限元极限分析(FELA)，对条形基础的承载力进行数值计算，以训练机器学习模型。采用6个无量纲输入参数(地质强度指数(GSI)、屈服参数(mi)、强度( B/ ci)、倾斜角()、离心率(e/)、黏附系数(A))和1个输出参数(承载系数(P/ ci B))共5120个FELA方案进行分析。结果表明，所有模型的效率都足以确定承载力系数，训练阶段的决定系数(r2)值在0.87 ~ 0.997之间，测试阶段的决定系数(r2)值在0.975 ~ 0.999之间。所提出的XGBoost模型优于RF、DNN和LSTM等其他模型，可以准确地用于估计倾斜和偏心荷载作用下岩体条形基础的承载能力。
{"title":"Bearing Capacity of Eccentrically Loaded Footings on Rock Masses Using Soft Computing Techniques","authors":"Divesh Kumar, P. Samui, Warit Wipulanusat, S. Keawsawasvong, Kongtawan Sangjinda, Wittaya Jitchaijaroen","doi":"10.30919/es929","DOIUrl":"https://doi.org/10.30919/es929","url":null,"abstract":"A crucial characteristic of real-world engineering operations is a strip footing's bearing capacity on a rock mass subjected to incline and eccentric loading conditions. Many scientists have attempted to establish and implement artificial intelligence (AI) models for estimating strip footings’ bearing capacity. In this study, four data-driven models, namely, extreme gradient boosting (XGBoost), random forest (RF), deep neural network (DNN), and long short-term memory (LSTM), are developed and compared to calculate the strip footing's bearing capacity. The strip footing's bearing capacity is obtained numerically by performing a lower bound (LB) and upper bound (UB) finite element limit analysis (FELA) for the purpose of training machine learning models. A total of 5120 FELA solutions with six dimensionless input parameters, namely, the geological strength index ( GSI ), the yield parameter ( m i ), the dimensionless strength ( 𝛾 B/ 𝜎 ci ) , inclination angle ( 𝛽 ), the dimensionless eccentricity ( e/B ), and the adhesion factor ( a ), and one output parameter, the bearing capacity factor ( P/ 𝜎 ci B ), were utilized in the analysis. The results show that the efficiency of all the proposed models is sufficient for bearing capacity factor determination, with coefficient of determination ( R 2 ) values ranging from 0.87 to 0.997 in the training phase and 0.975 to 0.999 in the testing phase. The proposed XGBoost model outperforms other models, such as RF, DNN, and LSTM, and can be used accurately for estimating a strip footing's bearing capacity on rock mass subjected to incline and eccentric loading loads.","PeriodicalId":36059,"journal":{"name":"Engineered Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87232191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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