Search results for: isotropic reinforcement solid

Authors: Adinarayana Gorajana, Venkata Srikanth Meka, Sanjay Garg, Lim Sue May

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This study was designed to evaluate and enhance the solubility of poorly soluble drug, docetaxel through solid dispersion (SD) technique prepared using freeze drying method. Docetaxel solid dispersions were formulated with Soluplus in different weight ratios. Freeze drying method was used to prepare the solid dispersions. Solubility of the solid dispersions were evaluated respectively and the optimized of drug-solubilizers ratio systems were characterized with different analytical methods like Differential scanning calorimeter (DSC), Scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) to confirm the formation of complexes between drug and solubilizers. The solubility data revealed an overall improvement in solubility for all SD formulations. The ternary combination 1:5:2 gave the highest increase in solubility that is approximately 3 folds from the pure drug, suggesting the optimum drug-solubilizers ratio system. This data corresponds with the DSC and SEM analyses, which demonstrates presence of drug in amorphous state and the dispersion in the solubilizers in molecular level. The solubility of the poorly soluble drug, docetaxel was enhanced through preparation of solid dispersion formulations employing freeze drying method. Solid dispersion with multiple carrier system shows better solubility compared to single carrier system.

Keywords: docetaxel, freeze drying, soluplus, solid dispersion technique

Procedia PDF Downloads 479

Authors: Lokes Parvatha Kumaran S., Sakthi Jay Mahenthar C., Sathyaprakash P., Jayakumar V., Shobanadevi A.

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With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network.

Keywords: quantum computing, quantum machine learning, variational quantum circuit, deep reinforcement learning, quantum information encoding scheme

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Authors: Muhammad Suradi, Sugiarto, Abdullah Latip

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Many roadside slope failures occur during the rainy season, particularly in the period of extreme rainfall along Connecting National Road of Salubatu-Mambi, West Sulawesi, Indonesia. These occurrences cause traffic obstacles and endanger people along and around the road. Research collaboration between P2JN (National Road Construction Board) West Sulawesi Province, who authorize to supervise the road condition, and Ujung Pandang State Polytechnic (Applied University) was established to cope with the landslide problem. This research aims to determine factors triggering roadside slope failures and their optimum stabilizing solution. To achieve this objective, site observation and soil investigation were carried out to obtain parameters for analyses of rainfall-induced slope instability and reinforcement design using the SV Flux and SV Slope software. The result of this analysis will be taken into account for the next analysis to get an optimum design of the slope reinforcement. The result indicates some factors such as steep slopes, sandy soils, and unvegetated slope surface mainly contribute to the slope failures during intense rainfall. With respect to the contributing factors as well as construction material and technology, cantilever/butressing retaining wall becomes the optimum solution for the roadside slope reinforcement.

Keywords: roadside slope, failure, rainfall, slope reinforcement, optimum solution

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Authors: Ebrahim Fataei, Seyed Ali Hosseini, Zahra Arabi, Habib farhadi, Mehdi Aalipour Erdi, Seiied Taghi Seiied Safavian

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Solid waste production is an integral part of human life and management of waste require full scientific approach and essential planning. The allocation of most management cost to collection and transportation and also the necessity of operational efficiency in this system, by limiting time consumption, and on the other hand optimum collection system and transportation is the base of waste design and management. This study was done to optimize the exits collection and transportation system of solid waste in Meshkinshahr city. So based on the analyzed data of municipal solid waste components in seven zones of Meshkinshahr city, and GIS software, applied to design storage place based on origin recycling and a route to collect and transport. It was attempted to represent an appropriate model to store, collect and transport municipal solid waste. The result shows that GIS can be applied to locate the waste container and determine a waste collection direction in an appropriate way.

Keywords: municipal solid waste management, transportation, optimizing, GIS, Iran

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Authors: Ali Nadi, Ali Edrissi

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Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: disaster management, real-time demand, reinforcement learning, relief demand

Procedia PDF Downloads 274

Authors: Nagasamy Venkatesh Dhandapani, Amged Awad El-Gied

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Cefixime, a BCS class II drug, is insoluble in water but freely soluble in acetone and in alcohol. The aqueous solubility of cefixime in water is poor and exhibits exceptionally slow and intrinsic dissolution rate. In the present study, cefixime and β-Cyclodextrin (β-CD) solid dispersions were prepared with a view to study the effect and influence of β-CD on the solubility and dissolution rate of this poorly aqueous soluble drug. Phase solubility profile revealed that the solubility of cefixime was increased in the presence of β-CD and was classified as A_L-type. Effect of variable, such as drug:carrier ratio, was studied. Physical characterization of the solid dispersion was characterized by Fourier transform infrared spectroscopy (FT-IR) and Differential scanning calorimetry (DSC). These studies revealed that a distinct loss of drug crystallinity in the solid molecular dispersions is ostensibly accounting for enhancement of dissolution rate in distilled water. The drug release from the prepared solid dispersion exhibited a first order kinetics. Solid dispersions of cefixime showed a 6.77 times fold increase in dissolution rate over the pure drug.

Keywords: β-cyclodextrin, cefixime, dissolution, Kneading method, solid dispersions, release kinetics

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Authors: M. Kotobuki, M. Koishi

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Li1.5Al0.5Ti1.5 (PO4)3(LATP) has received much attention as a solid electrolyte for lithium batteries. In this study, the LATP solid electrolyte is prepared by the co-precipitation method using Li3PO4 as a Li source. The LATP is successfully prepared and the Li ion conductivities of bulk (inner crystal) and total (inner crystal and grain boundary) are 1.1 × 10-3 and 1.1 × 10-4 S cm-1, respectively. These values are comparable to the reported values, in which Li2C2O4 is used as the Li source. It is conclude that the LATP solid electrolyte can be prepared by the co-precipitation method using Li3PO4 as the Li source and this procedure has an advantage in mass production over previous procedure using Li2C2O4 because Li3PO4 is lower price reagent compared with Li2C2O4.

Keywords: co-precipitation method, lithium battery, NASICON-type electrolyte, solid electrolyte

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Authors: Shweta Singh, Sudaman Katti

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The most well-known sequence models make use of complex recurrent neural networks in an encoder-decoder configuration. The model used in this research makes use of a transformer, which is based purely on a self-attention mechanism, without relying on recurrence at all. More specifically, encoders and decoders which make use of self-attention and operate based on a memory, are used. In this research work, results for various 3D visual and non-visual reinforcement learning tasks designed in Unity software were obtained. Convolutional neural networks, more specifically, nature CNN architecture, are used for input processing in visual tasks, and comparison with standard long short-term memory (LSTM) architecture is performed for both visual tasks based on CNNs and non-visual tasks based on coordinate inputs. This research work combines the transformer architecture with the proximal policy optimization technique used popularly in reinforcement learning for stability and better policy updates while training, especially for continuous action spaces, which are used in this research work. Certain tasks in this paper are long horizon tasks that carry on for a longer duration and require extensive use of memory-based functionalities like storage of experiences and choosing appropriate actions based on recall. The transformer, which makes use of memory and self-attention mechanism in an encoder-decoder configuration proved to have better performance when compared to LSTM in terms of exploration and rewards achieved. Such memory based architectures can be used extensively in the field of cognitive robotics and reinforcement learning.

Keywords: convolutional neural networks, reinforcement learning, self-attention, transformers, unity

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Authors: Ghulam Haider Haidaree, Nsenda Lukumwena

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Most developing nations face energy production and supply problems. This is also the case of Afghanistan whose generating capacity does not meet its energy demand. This is due in part to high security and risk caused by war which deters foreign investments and insufficient internal revenue. To address the issue above, this paper would like to suggest an alternative and affordable way to deal with the energy problem. That is by converting Solid Waste to energy. As a result, this approach tackles the municipal solid waste issue (potential cause of several diseases), contributes to the improvement of the quality of life, local economy, and so on. While addressing the solid waste problem in general, this paper samples specifically one municipality which is District-12, one of the 22 districts of Kabul city. Using geographic information system (GIS) technology, District-12 is divided into nine different zones whose municipal solid waste is respectively collected, processed, and converted into electricity and distributed to the closest area. It is important to mention that GIS has been used to estimate the amount of electricity to be distributed and to optimally position the production plant.

Keywords: energy problem, estimation of electricity, GIS zones, solid waste management system

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Authors: Peter Iyobosa Asemota

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This study on solid waste management practices addressed aspects of environmental and health impacts resulting from poor management of solid waste. The study was occasioned by the observed rate and volume of illegal and indiscriminate dumping of solid waste materials especially in informal settlements. The main focus of this study was to establish the impact of waste management practices on human health and the environment. The study, therefore, presents a critical analysis of the state of solid waste management in the study area and the implications for human health and the environment. The study was carried out in Kayamandi informal settlement within Stellenbosch municipality. The sustainable management of solid waste is very important in order to minimize the environmental and public health risks associated with improper solid waste management. There is no denying the fact that the problems of waste management will become critical as time goes on because of improper and inefficient waste management practices. Towns and cities exhibit the burdens of waste management which is a characteristics feature of most African cities. The study critically assess the implementation of waste management practices by the residents of the informal settlement; identify the factors affecting management issues in the operation of solid waste management system by the municipality; identify factors militating against the implementation of waste management policies and legislation. Furthermore, a waste assessment study was carried out to assess the generation; composition of the waste stream and also determine the attitudes and behavior of the residents with regard to waste management practices. Findings from the study revealed that Kayamandi is not different from other informal settlements with regards to waste management. People are of the opinion that solid waste management is the sole responsibility of municipal authorities and as such, the government should be responsible for bearing the cost of solid waste management.

Keywords: environment, waste, waste composition, waste stream, policy, waste categories, sanitary landfill, waste collection, integrated solid waste management

Procedia PDF Downloads 661

Authors: Y. Alrubyli

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Unmanned Arial Vehicles (UAVs), and more specifically, quadcopters need to be stable during their flights. Altitude stability is usually achieved by using a PID controller that is built into the flight controller software. Furthermore, the PID controller has gains that need to be tuned to reach optimal altitude stabilization during the quadcopter’s flight. For that, control system engineers need to tune those gains by using extensive modeling of the environment, which might change from one environment and condition to another. As quadcopters penetrate more sectors, from the military to the consumer sectors, they have been put into complex and challenging environments more than ever before. Hence, intelligent self-stabilizing quadcopters are needed to maneuver through those complex environments and situations. Here we show that by using online reinforcement learning with minimal background knowledge, the altitude stability of the quadcopter can be achieved using a model-free approach. We found that by using background knowledge instead of letting the online reinforcement learning algorithm wander for a while to tune the PID gains, altitude stabilization can be achieved faster. In addition, using this approach will accelerate development by avoiding extensive simulations before applying the PID gains to the real-world quadcopter. Our results demonstrate the possibility of using the trial and error approach of reinforcement learning combined with background knowledge to achieve faster quadcopter altitude stabilization in different environments and conditions.

Keywords: reinforcement learning, Q-leanring, online learning, PID tuning, unmanned aerial vehicle, quadcopter

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Authors: Haibin Zhou, Pingping Yao, Kunyang Fan

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Copper-based sintered friction materials are widely used in the brake system of different applications such as engineering machinery or high-speed train, due to the excellent mechanical, thermal and tribological performance. Considering the diversity of the working conditions of brake system, it is necessary to identify well and understand the tribological performance and wear mechanisms of friction materials for different conditions. Fe has been a preferred reinforcement for copper-based friction materials, due to its ability to improve the wear resistance and mechanical properties of material. Wear map is well accepted as a useful research method for evaluation of wear performances and wear mechanisms over a wider range of working conditions. Therefore, it is significantly important to construct a wear map which can give out the effects of work condition and Fe reinforcement on tribological performance of Cu-based friction materials. In this study, the copper-based sintered friction materials with the different addition of Fe reinforcement (0-20 vol. %) were studied. The tribological tests were performed against stainless steel in a ring-on-ring braking tester with varying braking energy density (0-5000 J/cm2). The linear wear and friction coefficient were measured. The worn surface, cross section and debris were analyzed to determine the dominant wear mechanisms for different testing conditions. On the basis of experimental results, the wear map and wear mechanism map were established, in terms of braking energy density and the addition of Fe. It was found that with low contents of Fe and low braking energy density, adhesive wear was the dominant wear mechanism of friction materials. Oxidative wear and abrasive wear mainly occurred under moderate braking energy density. In the condition of high braking energy density, with both high and low addition of Fe, delamination appeared as the main wear mechanism.

Keywords: Cu-based friction materials, Fe reinforcement, wear map, wear mechanism

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Authors: Faruk Elaldi

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There are lots of concrete columns and beams around in our living cities. Those columns are mostly open to aggressive environmental conditions and earthquakes. Mostly, they are deteriorated by sand, wind, humidity and other external applications at times. After a while, these beams and columns need to be repaired. Within the scope of this study, for reinforcement of concrete columns, samples were designed and fabricated to be strengthened with carbon fiber reinforced composite materials and conventional concrete encapsulation and followed by, and they were put into the axial compression test to determine load-carrying performance before column failure. In the first stage of this study, concrete column design and mold designs were completed for a certain load-carrying capacity. Later, the columns were exposed to environmental deterioration in order to reduce load-carrying capacity. To reinforce these damaged columns, two methods were applied, “concrete encapsulation” and the other one “wrapping with carbon fiber /epoxy” material. In the second stage of the study, the reinforced columns were applied to the axial compression test and the results obtained were analyzed. Cost and load-carrying performance comparisons were made and it was found that even though the carbon fiber/epoxy reinforced method is more expensive, this method enhances higher load-carrying capacity and reduces the reinforcement processing period.

Keywords: column reinforcement, composite, earth quake, carbon fiber reinforced

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Authors: Amandeep Singh, Niraj Bala

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In the last decades, light-weight materials such as magnesium matrix composites have become hot topic for material research due to their excellent mechanical and physical properties. However, relatively very less work has been done related to the wear behavior of these composites. Magnesium matrix composites have wide applications in automobile and aerospace sector. In this review, attempt has been done to collect the literature related to wear behavior of magnesium matrix composites fabricated through various processing techniques such as stir casting, powder metallurgy, friction stir processing etc. Effect of different reinforcements, reinforcement content, reinforcement size, wear load, sliding speed and time have been studied by different researchers in detail. Wear mechanism under different experimental condition has been reviewed in detail. The wear resistance of magnesium and its alloys can be enhanced with the addition of different reinforcements. Wear resistance can further be enhanced by increasing the percentage of added reinforcements. Increase in applied load during wear test leads to increase in wear rate of magnesium composites.

Keywords: hardness, magnesium matrix composites, reinforcement, wear

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Authors: Talebi Aliasghar, Ebrahimpour Komeleh Hooman, Maghsoudi Ali Akbar

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In reinforced concrete (RC) structures, beam-column connection region has a considerable effect on the behavior of structures. Using fiber reinforcement polymer (FRP) for the strengthening of connections in RC structures can be one of the solutions to retrofitting this zone which result in the enhanced behavior of structure. In this paper, these changes in behavior by using FRP for high strength concrete beam-column connection have been studied by finite element modeling. The concrete damage plasticity (CDP) model has been used to analyze the RC. The results illustrated a considerable development in load-bearing capacity but also a noticeable reduction in ductility. The study also assesses these qualities for several modes of strengthening and suggests the most effective mode of strengthening. Using FRP in flexural zone and FRP with 45-degree oriented fibers in shear zone of joint showed the most significant change in behavior.

Keywords: HSC, beam-column connection, Fiber Reinforcement Polymer, FRP, Finite Element Modeling, FEM

Procedia PDF Downloads 127

Authors: Boumaraf Malak

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The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

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Authors: Mikhail O. Eremin

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Present work is devoted to experimental study and numerical modelling of failure of rocks typical for Kuzbass coal basin (Russia). The main goal was to define strength and deformation characteristics of rocks on the base of uniaxial compression and three-point bending loadings and then to build a mathematical model of failure process for both types of loading. Depending on particular physical-mechanical characteristics typical rocks of Kuzbass coal basin (sandstones, siltstones, mudstones, etc. of different series – Kolchuginsk, Tarbagansk, Balohonsk) manifest brittle and quasi-brittle character of failure. The strength characteristics for both tension and compression are found. Other characteristics are also found from the experiment or taken from literature reviews. On the base of obtained characteristics and structure (obtained from microscopy) the mathematical and structural models are built and numerical modelling of failure under different types of loading is carried out. Effective characteristics obtained from modelling and character of failure correspond to experiment and thus, the mathematical model was verified. An Instron 1185 machine was used to carry out the experiments. Mathematical model includes fundamental conservation laws of solid mechanics – mass, impulse, energy. Each rock has a sufficiently anisotropic structure, however, each crystallite might be considered as isotropic and then a whole rock model has a quasi-isotropic structure. This idea gives an opportunity to use the Hooke’s law inside of each crystallite and thus explicitly accounting for the anisotropy of rocks and the stress-strain state at loading. Inelastic behavior is described in frameworks of two different models: von Mises yield criterion and modified Drucker-Prager yield criterion. The damage accumulation theory is also implemented in order to describe a failure process. Obtained effective characteristics of rocks are used then for modelling of rock mass evolution when mining is carried out both by an open-pit or underground opening.

Keywords: damage accumulation, Drucker-Prager yield criterion, failure, mathematical modelling, three-point bending, uniaxial compression

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Authors: Anuj Suhag, Rahul Dayal

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Aluminum matrix composites (AMCs) refer to the class of metal matrix composites that are lightweight but high performance aluminum centric material systems. The reinforcement in AMCs could be in the form of continuous/discontinuous fibers, whisker or particulates, in volume fractions. Properties of AMCs can be altered to the requirements of different industrial applications by suitable combinations of matrix, reinforcement and processing route. This work focuses on the fabrication of aluminum alloy (Al6061) matrix composites (AMCs) reinforced with 5 and 3 wt% Al2O3 particulates of 45µm using stir casting route. The aim of the present work is to investigate the effects of process parameters, determined by design of experiments, on microhardness, microstructure, Charpy impact strength, surface roughness and tensile properties of the AMC.

Keywords: aluminium matrix composite, Charpy impact strength test, composite materials, matrix, metal matrix composite, surface roughness, reinforcement

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Authors: Krista Mae F. Ramos, Lance Micaiah C. Dauz, Gylza Nicole D. Bautista, Rua R. Galang, Jeric Xyrus G. Karganilla

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With the recent proclamation of the possible reinforcement of Compulsory Military Service in the Philippines, debates and societal talks rose and circulated as opinions and perceptions regarding the topic continue to clash. This study aims to determine the perception of the youth on its reimplementation and identify various advantages and disadvantages based on their perspective. The responses were gathered through a virtual call interview, underwent the process of thematization, and were categorized into different themes. Results reflect that the students perceive compulsory military service as a necessity for national defense but requires a long time that can hinder their education and needs a strong foundation to be implemented and sustained. The participants acknowledged that the practice would instill discipline, patriotism, and nationalism, develop an individual’s physical abilities, provide skills and knowledge and improve a person’s self-defense. However, there are also concerns regarding the prominent military shaping and abuse, their loss of freedom of choice, and the chances of health deterioration.

Keywords: compulsory, military, service, reinforcement, perception

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Authors: V. K. Arora, Amit Rastogi

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To understand the anchor pile behaviour and to predict the capacity of piles under uplift loading are important concerns in foundation analysis. Experimental model tests have been conducted on single anchor pile embedded in cohesionless soil and subjected to pure uplift loading. A gravel-filled geogrid layer was located around the enlarged pile base. The experimental tests were conducted on straight-shafted vertical steel piles with an outer diameter of 20 mm in a steel soil tank. The tested piles have embedment depth-to-diameter ratios (L/D) of 2, 3, and 4. The sand bed is prepared at three different values of density of 1.67, 1.59, and 1.50gm/cc. Single piles embedded in sandy soil were tested and the results are presented and analysed in this paper. The influences of pile embedment ratio, reinforcement, relative density of soil on the uplift capacity of piles were investigated. The study revealed that the behaviour of single piles under uplift loading depends mainly on both the pile embedment depth-to-diameter ratio and the soil density. It is believed that the experimental results presented in this study would be beneficial to the professional understanding of the soil–pile-uplift interaction problem.

Keywords: flat under-reamer anchor pile, geogrid, pullout reinforcement, soil reinforcement

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: Lei Zhang, Tingting Xu, Jun He, Zhenyu Yan

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Email marketing is one of the most important segments of online marketing. It has been proved to be the most effective way to acquire and retain customers. The email content is vital to customers. Different customers may have different familiarity with a product, so a successful marketing strategy must personalize email content based on individual customers’ product affinity. In this study, we build our personalized email marketing strategy with three types of emails: nurture, promotion, and conversion. Each type of email has a different influence on customers. We investigate this difference by analyzing customers’ open rates, click rates and opt-out rates. Feature importance from response models is also analyzed. The goal of the marketing strategy is to improve the click rate on conversion-type emails. To build the personalized strategy, we formulate the problem as a reinforcement learning problem and adopt a Q-learning algorithm with variations. The simulation results show that our model-based strategy outperforms the current marketer’s strategy.

Keywords: email marketing, email content, reinforcement learning, machine learning, Q-learning

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Authors: Sateesh Kumar Pisini, Swetha Priya Darshini Thammadi, Sanjay Kumar Shukla

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Granular piles are a popular ground improvement technique in soft cohesive soils as well as for loose non-cohesive soils. The present experimental study has been carried out on granular piles in loose (Relative density = 30%) and medium dense (Relative density = 60%) sands with geogrid reinforcement within the sand bed over the granular piles. A group of five piles were installed in the sand at different spacing, s = 2d, 3d and 4d, d being the diameter of the pile. The length (L = 0.4 m) and diameter (d = 50 mm) of the piles were kept constant for all the series of experiments. The load-settlement behavior of reinforced sand bed and granular piles system was studied by applying the load on a square footing. The results show that the effect of reinforcement increases the load bearing capacity of the piles. It is also found that an increase in spacing between piles decreases the settlement for both loose and medium dense soil.

Keywords: granular pile, load-carrying capacity, settlement, geogrid reinforcement, sand

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

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We propose the use of reinforcement learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making makes it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and statistical analysis of the results. We study the generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: inventory management, reinforcement learning, supply chain optimization, uncertainty

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Authors: Ocotlán Díaz-Parra, Jorge A. Ruiz-Vanoye, Alejandro Fuentes-Penna, Beatriz Bernabe-Loranca, Patricia Ambrocio-Cruz, José J. Hernández-Flores

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The Maritime Platform Transport Problem of Solid, Special and Dangerous Waste consist of to minimize the monetary value of carry different types of waste from one location to another location using ships. We offer a novel mathematical, the characterization of the problem and the use CPLEX to find the optimal values to solve the Solid, Special and Hazardous Waste Transportation Problem of offshore platforms instances of Mexican state-owned petroleum company (PEMEX). The set of instances used are WTPLib real instances and the tool CPLEX solver to solve the MPTPSSDW problem.

Keywords: oil platform, transport problem, waste, solid waste

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Authors: Rafiuddin

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Solid polymer electrolytes have emerged as an area of interest in the field of solid state chemistry owing to their facile and cost-effective synthesis and number of applications in different areas of chemistry, extending over a wide range of temperatures. In the present work, polymer composite solid electrolyte comprising of Polyaniline (PANI) as polymer and potassium silver iodide (KAg4I5) using alumina (Al2O3) of different compositions having the formula (1-x) PANI- KAg4I5. x Al2O3 with x ranging from 0.0 to 0.5 was prepared by solid state reaction method. The structural elucidation and characterization was done by X- Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetric- Differential Thermal Analysis (TG-DTA) and Impedance Spectroscopy. The thermal analysis shows a phase transition at 147°C attributed to β-α phase transition of AgI due to the disproportionation of KAg4I5 to AgI and KAg2I3 at temperatures higher than 36°C. The X Ray diffraction analysis also confirms the presence of both AgI and KAg2I3 in the samples. The conductivities recorded over a temperature range of 40-250° C lie in the range of 10-1 to 10-3 S cm-1. Maximum conductivity was seen in the compositon x = 0.4 i.e. 1.84 × 10-2 Scm-1 at 313 K and 1.38 × 10-1 Scm-1 at 513 K, with a minimum activation energy of 0.14 eV.

Keywords: polymer solid electrolytes, XRD, DTA, electrical conductivity, impedance spectroscopy

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Authors: Alexander A. Karabutov, Natalia B. Podymova, Elena B. Cherepetskaya, Vladimir A. Makarov, Yulia G. Sokolovskaya

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The laser-ultrasonic method is realized for quantifying the influence of porosity on the local Young’s modulus of isotropic composite materials. The method is based on a laser generation of ultrasound pulses combined with measurement of the phase velocity of longitudinal and shear acoustic waves in samples. The main advantage of this method compared with traditional ultrasonic research methods is the efficient generation of short and powerful probing acoustic pulses required for reliable testing of ultrasound absorbing and scattering heterogeneous materials. Using as an example samples of a metal matrix composite with reinforcing microparticles of silicon carbide in various concentrations, it is shown that to provide an effective increase in Young’s modulus with increasing concentration of microparticles, the porosity of the final sample should not exceed 2%.

Keywords: laser ultrasonic, longitudinal and shear ultrasonic waves, porosity, composite, local elastic moduli

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Authors: Nükhet Konuk, N. Gamze Turan, Yüksel Ardalı

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Solid waste management is the most environmental problem in Turkey as a result of the rapid increase in solid waste generation caused by the rapid population growth, urbanization, rapid industrialization and economic development. The large quantity of waste generated necessitates system of collection, transportation and disposal. The landfill method for the ultimate disposal of solid waste continues to be widely accepted and used due to its economic advantages. In Turkey, most of the disposal sites open dump areas. Open dump sites may result in serious urban, sanitary and environmental problems such as an unpleasant odor and the risk of explosion as well as groundwater contamination because of leachate percolation. Unsuitable management practices also result in the loss of resources and energy, which could be recycled and produced from a large part of the solid waste. Therefore, over the past few decades, particular attention has been drawn to the sustainable solid waste management as a response to the increase in environmental problems related to the disposal of waste. The objective of this paper is to assess the situation of landfilling practices in Turkey as a developing country and to identify any gaps in the system as currently applied. The results show that approximately 25 million tons of MSW are generated annually in Turkey. The percentage of MSW disposed to sanitary landfill is only 45% whereas more than 50% of MSW is disposed without any control.

Keywords: developing countries, open dumping, solid waste management, sustainable landfilling, sustainable solid waste management

Procedia PDF Downloads 274

Authors: Seyed Abolhasan Naeini, Javad Shamsi Soosahab

Abstract:

The use of geosynthetic reinforcement within the footing soils is a very effective and useful method to avoid the construction of costly deep foundations. This study investigated the use of geosynthetics for soil improvement based on numerical modeling using FELA software. Pressure settlement behavior and bearing capacity ratio of foundation on geogrid reinforced sand is investigated and the effect of different parameters like as number of geogrid layers and vertical distance between elements in three different relative density soil is studied. The effects of geometrical parameters of reinforcement layers were studied for determining the optimal values to reach to maximum bearing capacity. The results indicated that the optimum range of the distance ratio between the reinforcement layers was achieved at 0.5 to 0.6 and after number of geogrid layers of 4, no significant effect on increasing the bearing capacity of footing on reinforced sandy with geogrid

Keywords: geogrid, reinforced sand, FELA software, distance ratio, number of geogrid layers

Procedia PDF Downloads 122

Authors: Irena Basaric Ikodinovic, Dragoslav Rakic, Mirjana Vukicevic, Sanja Jockovic, Jovana Jankovic Pantic

Abstract:

Large long-term settlement occurs at the municipal solid waste landfills over an extended period of time which may lead to breakage of the geomembrane, damage of the cover systems, other protective systems or facilities constructed on top of a landfill. Also, municipal solid waste is an extremely heterogeneous material and its properties vary over location and time within a landfill. These material characteristics require the formulation of a new constitutive model to predict the long-term settlement of municipal solid waste. The paper presents a new constitutive model which is formulated to describe the mechanical behavior of municipal solid waste. Model is based on Modified Cam Clay model and the critical state soil mechanics framework incorporating time-dependent components: mechanical creep and biodegradation of municipal solid waste. The formulated constitutive model is optimized and defined with eight input parameters: five Modified Cam Clay parameters, one parameter for mechanical creep and two parameters for biodegradation of municipal solid waste. Thereafter, the constitutive model is implemented in the software suite for finite element analysis (ABAQUS) and numerical analysis of the experimental landfill settlement is performed. The proposed model predicts the total settlement which is in good agreement with field measured settlement at the experimental landfill.

Keywords: constitutive model, finite element analysis, municipal solid waste, settlement

Procedia PDF Downloads 200