Search results for: gel strength prediction

Authors: Sampada Katekar, Namdeo Hedaoo

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Cold Bituminous Emulsion Mixtures (CBEMs) are generally produced by mixing unheated aggregate, binder and filler at ambient temperature. Cold bituminous emulsion mixtures have several environmental and cost-effective benefits. But CBEMs offer poor early life properties too and they require long curing time to achieve maximum strength. The main focus of this study is to overcome inferiority of CBEMs by incorporating Rice Husk Ash (RHA) and Ordinary Portland Cement (OPC). In this study, RHA and OPC are substituted for conventional mineral filler in an increased percentage from 0 to 3% with an increment of 1%. Marshall stability, retained stability and tensile strength tests were conducted to evaluate the enhancement in performance of CBEMs. The experimental results have shown that Marshall stability and tensile strength of CBEMs increased significantly by replacing the conventional mineral filler with RHA and OPC. The addition of RHA and OPC in CBEMs result in a reduction in moisture induced damages. However, stability and tensile strength values of RHA modified CBEMs are higher than that of OPC modified CBEMs.

Keywords: cold bituminous emulsion mixtures, Marshall stability test, ordinary Portland cement, rice husk ash

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Authors: Omar M. Elmabrouk

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The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes.

Keywords: artificial neural network, hardness, prediction, titanium aluminium nitrate coating

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Authors: Lanre Oluwafemi Akinyemi

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The reduction of limestone to cement in Nigeria is expensive and requires huge amounts of energy. This significantly affects the cost of cement. Concrete is heavy: a cubic foot of it weighs about 150 lbs. and a cubic yard is about 4000 lbs. Thus a ready-mix truck with 9 cubic yards is carrying 36,000 lbs excluding the weight of the truck itself, thereby accumulating cost for also manufacturers. Therein lies the need to find a substitute for cement by using the polystyrene paste that benefits both the manufactures and the consumers. Polystyrene Paste Constructional Cement (PPCC), a patented material obtained by dissolving Waste EPS in volatile organic solvent, has recently been identified as a suitable binder/cement for construction and building material production. This paper illustrates the procedures of a test experiment undertaken to determine the splitting tensile strength of PPCC mortar compared to that of OPC (Ordinary Portland Cement). Expanded polystyrene was dissolved in gasoline to form a paste referred to as Polystyrene Paste Constructional Cement (PPCC). Mortars of mix ratios 1:4, 1:5, 1:6, 1:7 (PPCC: fine aggregate) batched by volume were used to produce 50mm x 100mm cylindrical PPCC mortar splitting tensile strength specimens. The control experiment was done by creating another series of cylindrical OPC mortar splitting tensile strength specimens following the same mix ratio used earlier. The PPCC cylindrical splitting tensile strength specimens were left to air-set, and the ones made with Ordinary Portland Cement (OPC) were demoded after 24 hours and cured in water. The cylindrical PPCC splitting tensile strength specimens were tested at 28 days and compared with those of the Ordinary Portland cement splitting tensile strength specimens. The result shows that hence for this two mixes, PPCC exhibits a better binding property than the OPC. With this my new invention I recommend the use of PPCC as a substitute for a Portland cement.

Keywords: polystyrene paste, Portland cement, construction, mortar

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Authors: N. Tabatadze

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The research aim is to study the physical and mechanical characteristics of hydraulic concrete in the surface active environment. The specific goal is to obtain high strength and low deformable concrete based on nano additives, resistant to the aggressive environment. As result of research, the alkali-silica reaction was improved (relative elongation 0,122 % of admixture instead of 0,126 % of basic concrete after 14 days). The aggressive environment impact on the strength of heavy concrete, fabricated on the basis of the hydraulic admixture with the penetrating waterproof additives also was improved (strength on compression R28=47,5 mPa of admixture instead of R28=35,8 mPa). Moreover, water absorption (W=0,59 % of admixture instead of W=1,41 %), water tightness (R14=37,9 mPa instead R14=28,7 mPa) and water-resistance (B=18 instead B=12). The basic parameters of concrete with admixture was improved in comparison with basic concrete.

Keywords: hydraulic concrete, alkali-silica reaction, water absorption, water-resistance

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Authors: S. R. M. Krishna, R. Santosh Kumar, V. Kamakshi Prasad

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Machine learning techniques are applied for the analysis of the impact of mental illness on the burden of disease. It is calculated using the disability-adjusted life year (DALY). DALYs for a disease is the sum of years of life lost due to premature mortality (YLLs) + No of years of healthy life lost due to disability (YLDs). The critical analysis is done based on the Data sources, machine learning techniques and feature extraction method. The reviewing is done based on major databases. The extracted data is examined using statistical analysis and machine learning techniques were applied. The prediction of the impact of mental illness on the population using machine learning techniques is an alternative approach to the old traditional strategies, which are time-consuming and may not be reliable. The approach makes it necessary for a comprehensive adoption, innovative algorithms, and an understanding of the limitations and challenges. The obtained prediction is a way of understanding the underlying impact of mental illness on the health of the people and it enables us to get a healthy life expectancy. The growing impact of mental illness and the challenges associated with the detection and treatment of mental disorders make it necessary for us to understand the complete effect of it on the majority of the population.

Keywords: ML, DAL, YLD, YLL

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Authors: Mehmet Ucar, Nuray Ucar

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Both thermal insulation and sound insulation play a key role in energy saving and the quality of life. In this study, the effects of different fillers, such as silica aerogel and polystyrene, on the tensile strength, thermal insulation, and sound insulation of epoxy composites have been analyzed. Results from the experimental studies show that both tensile strength and insulation properties (sound and thermal insulation) of the epoxy composite increased by the use of silica aerogel additive. Polystyrene additive significantly increases the sound absorption coefficient of the epoxy composite. Such composites offer great potential for many applications.

Keywords: epoxy composite, silica aerogel, polystyrene, tensile strength, thermal insulation, sound insulation

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Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

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Authors: Abbas S. Al-Ameeri Rawaa H. Issa

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The internal sulfate attack is considered as a very important problem of concrete manufacture in Iraq and Middle East countries. Sulfate drastically influences the properties of concrete. This experimental study is aimed at investigating the effect of internal sulfates on fresh and some of the hardened properties of self compacting concrete (SCC) made from locally available materials. Tests were conducted on five mixes, with five SO3 levels (3.9, 5, 6, 7 and 8) (% by wt. of cement). The last four SO3 levels are outside the limits of the Iraqi specifications (IQS NO.45/1984). The results indicated that sulfate passively influenced the fresh properties such as decreased workability, and effect on hardened properties of the self compacting concrete. Also, the result indicated the optimum SO3 content which gives maximum strength and little tendency to expanding, which showed up at a content equal to 5% (by wt of cement), is more than acceptable limits of Iraqi specifications. Further increase in sulfates content in concrete after this optimum value showed a considerable reduction in mechanical properties of self-compacting concrete, and increment in expansion of concrete. The percentages of reduction in compressive strength, splitting tensile strength, flexural strength, static modulus of elasticity and ultrasonic pulse velocity at their later age were ranged between 10.89-36.14%, 12.90-33.33%, 7.98-36.35%, 16.36 -38.37% and 1.03-10.88% respectively.

Keywords: self-compacting concrete, sulfate attack, internal sulfate attack, fresh properties, harden properties, optimum SO3 content

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: I. Miličević, M. Hadzima Nyarko, R. Bušić, J. Simonović Radosavljević, M. Prokopijević, K. Vojisavljević

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This paper investigates the effects of different treatment methods of rubber aggregates for self-compacting concrete (SCC) on compressive strength and modulus of elasticity. SCC mixtures with 10% replacement of fine aggregate with crumb rubber by total aggregate volume and with different aggregate treatment methods were investigated. The rubber aggregate was treated in three different methods: dry process, water-soaking, and NaOH treatment plus water soaking. Properties of SCC in a fresh and hardened state were tested and evaluated. Scanning electron microscope (SEM) analysis of three different SCC patches were made and discussed. It was observed that applying the proposed NaOH plus water soaking method resulted in the improvement of fresh and hardened concrete properties. It resulted in a more uniform distribution of rubber particles in the cement matrix, a better bond between rubber particles and the cement matrix, and higher compressive strength of SCC rubberized concrete.

Keywords: compressive strength, modulus of elasticity, NaOH treatment, rubber aggregate, self-compacting rubberized concrete, scanning electron microscope analysis

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Authors: Bal Deep Sharma, Suresh Ray Yadav

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Local soils are often chosen due to their widespread availability and low cost. However, these soils typically have poor durability, which can lead to significant limitations in their use for construction. To address this issue, various soil stabilization techniques have been developed and used over the years. This study investigates the viability of employing the mineral polymerization (MIP) technique to stabilize black soils, intending to enhance their suitability for construction applications. This technique involves the microstructural transformation of certain clay minerals into solid and stable compounds exhibiting characteristics similar to hydroxy sodalite, feldspathoid, or zeolite. This transformation occurs through the action of an alkaline reactant at atmospheric pressure and low temperature. The soil sample was characterized using grain size distribution, Atterberg limit test, organic content test, and pH-value tests. The unconfined compressive strength of the soil specimens, prepared with varying percentages of sodium hydroxide as an additive and sand as a filler by weight, was determined at the optimum moisture content. The unconfined compressive strength of the specimens was tested under three different conditions: dry, wet, and cycling. The maximum unconfined compressive strengths were 77.568 kg/cm², 38.85 kg/cm², and 56.3 kg/cm² for the dry, wet, and cycling specimens, respectively, while the unconfined compressive strength of the untreated soil was 7.38 kg/cm². The minimum unconfined compressive strength of the wet and cycling specimens was greater than that of the untreated soil. Based on these findings, it can be concluded that these soils can be effectively used as construction material after treatment with sodium hydroxide.

Keywords: soil stabilization technique, soft soil treatment, sodium hydroxide, unconfined compressive strength

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Authors: Jens Friedrich, Matthias A. Gebele, Armin Lechler, Alexander Verl

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Chatter vibrations and process instabilities are the most important factors limiting the productivity of the milling process. Chatter can leads to damage of the tool, the part or the machine tool. Therefore, the estimation and prediction of the process stability is very important. The process stability depends on the spindle speed, the depth of cut and the width of cut. In milling, the process conditions are defined in the NC-program. While the spindle speed is directly coded in the NC-program, the depth and width of cut are unknown. This paper presents a new simulation based approach for the prediction of the depth and width of cut of a milling process. The prediction is based on a material removal simulation with an analytically represented tool shape and a multi-dexel approach for the work piece. The new calculation method allows the direct estimation of the depth and width of cut, which are the influencing parameters of the process stability, instead of the removed volume as existing approaches do. The knowledge can be used to predict the stability of new, unknown parts. Moreover with an additional vibration sensor, the stability lobe diagram of a milling process can be estimated and improved based on the estimated depth and width of cut.

Keywords: dexel, process stability, material removal, milling

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Authors: Viviana Letelier, Ester Tarela, Bianca Lopez, Pedro Muñoz, Giacomo Moriconi

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The environmental impact has become a global concern during the last decades. Several alternatives have been proposed and studied to minimize this impact in different areas. The reuse of aggregates from old concretes to manufacture new ones not only can reduce this impact but is also a way to optimize the resource management. The effect of the origin of the reused aggregates from two different origin materials in recycled concrete pavement is studied here. Using the dosing applied by a pavement company, coarse aggregates in the 6.3-25 mm fraction are replaced by recycled aggregates with two different origins: old concrete pavements with similar origin strength to the one of the control concrete, and precast concrete pipes with smaller strengths than the one of the control concrete. The replacement percentages tested are 30%, 40% and 50% in both cases. The compressive strength tests are performed after 7, 14, 28 and 90 curing days, the flexural strength tests and the elasticity modulus tests after 28 and 90 curing days. Results show that the influence of the quality of the origin concrete in the mechanical properties of recycled concretes is not despicable. Concretes with up to a 50% of recycled aggregates from the concrete pavement have similar compressive strengths to the ones of the control concrete and slightly smaller flexural strengths that, however, in all cases exceed the minimum of 5MPa after 28 curing days stablished by the Chilean regulation for pavement concretes. On the other hand, concretes with recycled aggregates from precast concrete pipes show significantly lower compressive strengths after 28 curing days. The differences with the compressive strength of the control concrete increase with the percentage of replacement, reaching a 13% reduction when 50% of the aggregates are replaced. The flexural strength also suffers significant reductions that increase with the percentage of replacement, only obeying the Chilean regulation when 30% of the aggregates are recycled after 28 curing days. Nevertheless, after 90 curing days, all series obey the regulation requirements. Results show, not only the importance of the quality of the origin concrete, but also the significance of the curing days, that may allow the use of less quality recycled material without important strength losses.

Keywords: flexural strength of recycled concrete., mechanical properties of recycled concrete, recycled aggregates, recycled concrete pavements

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Authors: Diqin Qi, Jiaming Li, Siman Li

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Based on the use of the three-point smoothing method for selectively processing original data columns, this paper establishes a group of grey GM(1,1) models to predict the concentration ranges of four major air pollutants in Shanghai from 2023 to 2024. The results indicate that PM₁₀, SO₂, and NO₂ maintain the national Grade I standards, while the concentration of PM₂.₅ has decreased but still remains within the national Grade II standards. Combining the forecast results, recommendations are provided for the Shanghai municipal government's efforts in air pollution prevention and control.

Keywords: atmospheric pollutant prediction, Grey GM(1, 1), model group, three-point smoothing method

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Authors: Natia Tabatadze

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The research aim is to study the physical - mechanical characteristics of structural materials, in particular, hydraulic concrete in the surface active environment and receiving of high strength concrete, low-deformable, resistant to aggressive environment concrete due application of nano technologies. The obtained concrete with additives will by possible to apply in hydraulic structures. We used cement (compressive strength R28=39,42 mPa), sand (0- 5 mm), gravel (5-10 mm, 10-20 mm), admixture CHRYSO® Fuge B 1,5% dosage of cement. CHRYSO® Fuge B renders mortar and concrete highly resistant to capillary action and reduces, or even eliminates infiltration of water under pressure. The fine particles that CHRYSO® Fuge B contains combine with the lime in the cement to form water repellent particles. These obstruct the capillary action within concrete. CHRYSO® Fuge B does not significantly modify the characteristics of the fresh concrete and mortar, nor the compressive strength. As result of research, the alkali-silica reaction was improved (relative elongation 0,122 % of admixture instead of 0,126 % of basic concrete after 14 days). The aggressive environment impact on the strength of heavy concrete, fabricated on the basis of the hydraulic admixture with the penetrating waterproof additives also was improved (strength on compression R28=47,5 mPa of admixture instead of R28=35,8 mPa), as well as the mass water absorption (W=3,37 % of admixture instead of W=1,41 %), volume water absorption (W=1,41 % of admixture instead of W=0,59 %), water tightness (R14=37,9 mPa instead R14=28,7 mPa) and water-resistance (B=18 instead B=12). The basic parameters of concrete with admixture was improved in comparison with basic concrete.

Keywords: structural materials, hydraulic concrete, low-deformable, water absorption for mass, water absorption for volume

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Authors: Sefiu A. Bello, Nasirudeen K. Raji, Jeleel A. Adebisi, Sadiq A. Raji

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Pure metals are not used in most cases for structural applications because of their limited properties. Presently, high entropy alloys (HEAs) are emerging by mixing comparative proportions of metals with the aim of maximizing the entropy leading to enhancement in structural and mechanical properties. Aluminum Silicon Nickel Iron Vanadium (AlSiNiFeV) alloy was developed using stir cast technique and analysed. Results obtained show that the alloy grade G0 contains 44 percentage by weight (wt%) Al, 32 wt% Si, 9 wt% Ni, 4 wt% Fe, 3 wt% V and 8 wt% for minor elements with tensile strength and elongation of 106 Nmm^-2 and 2.68%, respectively. X-ray diffraction confirmed intermetallic compounds having hexagonal closed packed (HCP), orthorhombic and cubic structures in cubic dendritic matrix. This affirmed transformation from the cubic structures of elemental constituents of the HEAs to the precipitated structures of the intermetallic compounds. A maximum tensile strength of 188 Nmm^-2 with 4% elongation was noticed at 10wt% of silica addition to the G0. An increase in tensile strength with an increment in silica content could be attributed to different phases and crystal geometries characterizing each HEA.

Keywords: HEAs, phases model, aluminium, silicon, tensile strength, model

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Authors: Mary Joy B. Amoguis, Alonah Jane D. Labtic, Hyna Wary Namoca, Aira Jane V. Original

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Animal waste has been rapidly increasing due to the growing animal population and the lack of innovative waste management practices. In a country like the Philippines, animal waste is rampant. This study aims to minimize animal waste by producing garden bricks using cat stool as an additive. The research study analyzes different levels of concentration to determine the most efficient combination in terms of compressive strength and durability of cat stool as an additive to garden bricks. The researcher's first collects the cat stool and incinerates the different concentrations. The first concentration is 25% cat stool and 75% cement mixture. The second concentration is 50% cat stool and 50% cement mixture. And the third concentration is 75% cat stool and 25% cement mixture. The researchers analyze the statistical data using one-way ANOVA, and the statistical analysis revealed a significant difference compared to the controlled variable. The research findings show an inversely proportional relationship: the higher the concentration of cat stool additive, the lower the compressive strength of the bricks, and the lower the concentration of cat stool additive, the higher the compressive strength of the bricks.

Keywords: cat stool, garden bricks, cement, concentrations, animal wastes, compressive strength, durability, one-way ANOVA, additive, incineration, aggregates, stray cats

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Authors: Mahmoud M. Tash

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The present study was undertaken to investigate the effect of aging parameters (time and temperature) on the mechanical properties of Be-and/or Zr- treated Al-Mg-Zn (7075) alloys. Ultimate tensile strength, 0.5% offset yield strength and % elongation measurements were carried out on specimens prepared from cast and heat treated 7075 alloys containing Be and/or Zr. Different aging treatment were carried out for the as solution treated (SHT) specimens. The specimens were aged at different conditions; Natural and artificial aging was carried out at room temperature, 120C, 150C, 180C and 220C for different periods of time. Duplex aging was performed for SHT conditions (pre-aged at different time and temperature followed by high temperature aging). Ultimate tensile strength, yield strength and % elongation data results as a function of different aging parameters are analysed. A statistical design of experiments (DOE) approach using fractional factorial design is applied to acquire an understanding of the effects of these variables and their interactions on the mechanical properties of Be- and/or Zr- treated 7075 alloys. Mathematical models are developed to relate the alloy mechanical properties with the different aging parameters.

Keywords: casting aging treatment, mechanical properties, Al-Mg-Zn alloys, Be- and/or Zr-treatment, experimental correlation

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Authors: Amol Rakhunde, Namdeo Hedaoo

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Cold bituminous emulsion mixes (CBEM) are preferred due to their low cost for the construction of low volume roads in India. Due to the low strength of CBEM’s, the strength is generally increased by the addition of Ordinary Portland Cement (OPC) and hydrated lime. To improve the performance of CBEM’s, the use of industrial waste material is also an alternative. Steel slag is by product of steel industry which is sustainable construction material. Due to limited modes of practice of utilization steel slag, huge amount of steel slag dumped in yards of each steel industry and engaging of important agricultural land and gave pollution to whole environment. The effective use of steel slag as additives in CBEM’s has ultimate benefits such improvement in strength of CBEM’s, waste disposal steel slag, saving natural aggregate and lowering cost of roadways. Studies carried out in the past have shown a significant improvement in the strength of CBEM’s prepared with the replacement of natural aggregate with industrial waste materials such as fly ash and ground granulated blast furnace slag. In this study, effect of modified mix which is mixes prepared with steel slag compared with the control mix and the mixes prepared with OPC. Experimental work was carried out on the sample of control mix, OPC mix, and modified mix. For modified mix, aggregate was replaced with steel slag by 10%, 20%, 30% and 40% of weight of aggregate of same size as of steel slag in aggregate gradation. For OPC mix, filler was replaced by 1%, 2% and 3% of weight of total aggregate with OPC. Optimum emulsion content of each mix obtained by using Marshall stability test and comparison of stability values were carried out. Marshall stability, indirect tensile strength test, and retained stability tests are performed on control mixes, OPC mixes and modified mixes. Significant improvement in Marshall stability retained stability and indirect tensile strength of modified mix compared to control mix and OPC mix.

Keywords: CBEM, indirect tensile strength test, Marshall stability test, OPC, optimum emulsion content, retained stability test, steel slag

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Authors: Aidan J. Bowes, Reaz Hasan

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The automotive industry is continually aiming to develop the aerodynamics of car body design. This may be for a variety of beneficial reasons such as to increase speed or fuel efficiency by reducing drag. However recently there has been a greater amount of focus on wind noise produced while driving. Designers in this industry seek a combination of both simplicity of approach and overall effectiveness. This combined with the growing availability of commercial CFD (Computational Fluid Dynamics) packages is likely to lead to an increase in the use of RANS (Reynolds Averaged Navier-Stokes) based CFD methods. This is due to these methods often being simpler than other CFD methods, having a lower demand on time and computing power. In this investigation the effectiveness of turbulent flow and acoustic noise prediction using RANS based methods has been assessed for different wing mirror geometries. Three different RANS based models were used, standard k-ε, realizable k-ε and k-ω SST. The merits and limitations of these methods are then discussed, by comparing with both experimental and numerical results found in literature. In general, flow prediction is fairly comparable to more complex LES (Large Eddy Simulation) based methods; in particular for the k-ω SST model. However acoustic noise prediction still leaves opportunities for more improvement using RANS based methods.

Keywords: acoustics, aerodynamics, RANS models, turbulent flow

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: I. Goual, M. S. Goual, M. K. Gueddouda, Taïbi Saïd, Abou-Bekr Nabil, A. Ferhat

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The aim of the paper is to study the hydro-mechanical behavior of a tuff and calcareous sand mixture. A first experimental phase was carried out in order to find the optimal mixture. This showed that the material composed of 80% tuff and 20% calcareous sand provides the maximum mechanical strength. The second experimental phase concerns the study of the drying-wetting behavior of the optimal mixture was carried out on slurry samples and compacted samples at the MPO. Experimental results let to deduce the parameters necessary for the prediction of the hydro-mechanical behavior of pavement formulated from tuff and calcareous sand mixtures, related to moisture. This optimal mixture satisfies the regulation rules and hence constitutes a good local eco-material, abundantly available, for the conception of pavements.

Keywords: tuff, sandy calcareous, road engineering, hydro mechanical behaviour, suction

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Authors: Amira Abdelrasoul, Huu Doan, Ali Lohi

Abstract:

An efficient and well-planned ultrafiltration process is becoming a necessity for monetary returns in the industrial settings. The aim of the present study was to develop a mathematical model for an accurate prediction of ultrafiltration membrane fouling of latex effluent applied to homogeneous and heterogeneous membranes with uniform and non-uniform pore sizes, respectively. The models were also developed for an accurate prediction of power consumption that can handle the large-scale purposes. The model incorporated the fouling attachments as well as chemical and physical factors in membrane fouling for accurate prediction and scale-up application. Both Polycarbonate and Polysulfone flat membranes, with pore sizes of 0.05 µm and a molecular weight cut-off of 60,000, respectively, were used under a constant feed flow rate and a cross-flow mode in ultrafiltration of the simulated paint effluent. Furthermore, hydrophilic ultrafilic and hydrophobic PVDF membranes with MWCO of 100,000 were used to test the reliability of the models. Monodisperse particles of 50 nm and 100 nm in diameter, and a latex effluent with a wide range of particle size distributions were utilized to validate the models. The aggregation and the sphericity of the particles indicated a significant effect on membrane fouling.

Keywords: membrane fouling, mathematical modeling, power consumption, attachments, ultrafiltration

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Authors: Solmaz Eskandarion, Haniyeh Eftekhar, Amin Fallahi

Abstract:

Introduction: Nowadays cosmetic dentistry has gained greater attention because of the changing demands of dentistry patients. Composite resin restorations play an important role in the field of esthetic restorations. Due to the variation between the resin composites, it is important to be aware of their mechanical properties and surface roughness. So, the aim of this study was to compare the mechanical properties (surface hardness, compressive strength, diametral tensile strength) and surface roughness of four kinds of resin composites after thermal aging process. Materials and Method: 10 samples of each composite resins (Gradia-direct (GC), Filtek Z250 (3M), G-ænial (GC), Filtek Z350 (3M- filtek supreme) prepared for evaluation of each properties (totally 120 samples). Thermocycling (with temperature 5 and 55 degree of centigrade and 10000 cycles) were applied. Then, the samples were tested about their compressive strength and diametral tensile strength using UTM. And surface hardness was evaluated with Microhardness testing machine. Either surface roughness was evaluated with Scanning electron microscope after surface polishing. Result: About compressive strength (CS), Filtek Z250 showed the highest value. But there were not any significant differences between 4 groups about CS. Either Filtek Z250 detected as a composite with highest value of diametral tensile strength (DTS) and after that highest to lowest DTS was related to: Filtek Z350, G-ænial and Gradia-direct. And about DTS all of the groups showed significant differences (P<0.05). Vickers Hardness Number (VHN) of Filtek Z250 was the greatest. After that Filtek Z350, G-ænial and Gradia-direct followed it. The surface roughness of nano-filled composites was less than Microhybrid composites. Either the surface roughness of GC Ganial was a little greater than Filtek Z250. Conclusion: This study indicates that there is not any evident significant difference between the groups amoung their mechanical properties. But it seems that Filtek Z250 showed slightly better mechanical properties. About surface roughness, nanofilled composites were better that Microhybrid.

Keywords: mechanical properties, surface roughness, resin composite, compressive strength, thermal aging

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Authors: Esra Arici, Duygu Olmez, Murat Ozkan, Nurcan Topcu, Furkan Capraz, Gokhan Deniz, Arman Altinyay

Abstract:

Engineered quartz stone is a composite material comprising approximately 90 wt.% fine quartz aggregate with a variety of particle size ranges and `10 wt.% unsaturated polyester resin (UPR). In this study, the objective is to investigate the influence of particle size distribution on mechanical strength and physical properties of the engineered stone slabs. For this purpose, granular quartz with two particle size ranges of 63-200 µm and 100-300 µm were used individually and mixed with a difference in ratios of mixing. The void volume of each granular packing was measured in order to define the amount of filler; quartz powder with the size of less than 38 µm, and UPR required filling inter-particle spaces. Test slabs were prepared using vibration-compression under vacuum. The study reports that both impact strength and flexural strength of samples increased as the mix ratio of the particle size range of 63-200 µm increased. On the other hand, the values of water absorption rate, apparent density and abrasion resistance were not affected by the particle size distribution owing to vacuum compaction. It is found that increasing the mix ratio of the particle size range of 63-200 µm caused the higher porosity. This led to increasing in the amount of the binder paste needed. It is also observed that homogeneity in the slabs was improved with the particle size range of 63-200 µm.

Keywords: engineered quartz stone, fine quartz aggregate, granular packing, mechanical strength, particle size distribution, physical properties.

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Authors: P. Parthiban, J. Karthikeyan

Abstract:

This paper deals with the performance of semi-light weight concrete, prepared by using wood ash pellets as coarse aggregates which were improved by partial replacement of cement with alccofine. Alccofine is a mineral admixture which contains high glass content obtained through the process of controlled granulation. This is finer than cement which carries its own pozzolanic property. Therefore, cement could be replaced by alccofine as 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, and 70% to enhance the strength and durability properties of concrete. High range water reducing admixtures (HRWA) were used in these mixes which were dosed up to 1.5% weight of the total cementitious content (alccofine & cement). It also develops the weaker transition zone into more impermeable layer. Specimens were subjected in both the accelerated curing method as well as conventional curing method. Experimental results were compared and reported, in that the maximum compressive strength of 32.6 MPa was achieved on 28^th day with 30% replacement level in a density of 2200 kg/m³ to a conventional curing, while in the accelerated curing, maximum compressive strength was achieved at 40% replacement level. Rapid chloride penetration test (RCPT) output results for the conventional curing method at 0% and 70% give 3296.7 and 545.6 coulombs.

Keywords: Alccofine, compressive strength, RCPT, wood ash pellets

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Authors: R. Ziaie Moayed, M. Hamidzadeh

Abstract:

The use of waste rubber chips not only can be of great importance in terms of the environment, but also can be used to increase the shear strength of soils. The purpose of this study was to evaluate the variation of the internal friction angle of liquefiable sandy soil using waste rubber chips. For this purpose, the geotechnical properties of unmodified and modified soil samples by waste lining rubber chips have been evaluated and analyzed by performing the triaxial consolidated drained test. In order to prepare the laboratory specimens, the sandy soil in part of Rudsar shores in Gilan province, north of Iran with high liquefaction potential has been replaced by two percent of waste rubber chips. Samples have been compressed until reaching the two levels of density of 15.5 and 16.7 kN/m³. Also, in order to find the optimal length of chips in sandy soil, the rectangular rubber chips with the widths of 0.5 and 1 cm and the lengths of 0.5, 1, and 2 cm were used. The results showed that the addition of rubber chips to liquefiable sandy soil greatly increases the shear resistance of these soils. Also, it can be seen that decreasing the width and increasing the length-to-width ratio of rubber chips has a direct impact on the shear strength of the modified soil samples with rubber chips.

Keywords: improvement, shear strength, internal friction angle, sandy soil, rubber chip

Procedia PDF Downloads 145