Search results for: deep soil

Authors: Hossein Motaghedi, Kaveh Arkani, Siavash Salamatpoor

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Usually the piles are important tools for safety and economical design of high and heavy structures. For this aim the response of single pile under dynamic load is so effective. Also, the agents which have influence on single pile response are properties of pile geometrical, soil and subjected loads. In this study the finite difference numerical method and by using FLAC 3D software is used for evaluation of single pile behavior under peak ground acceleration (PGA) of El Centro earthquake record in California (1940). The results of this models compared by experimental results of other researchers and it will be seen that the results of this models are approximately coincide by experimental data's. For example the maximum moment and displacement in top of the pile is corresponding to the other experimental results of pervious researchers. Furthermore, in this paper is tried to evaluate the effective properties between soil and pile. The results is shown that by increasing the pile diagonal, the pile top displacement will be decreased. As well as, by increasing the length of pile, the top displacement will be increased. Also, by increasing the stiffness ratio of pile to soil, the produced moment in pile body will be increased and the taller piles have more interaction by soils and have high inertia. So, these results can help directly to optimization design of pile dimensions.

Keywords: pile seismic response, interaction between soil and pile, numerical analysis, FLAC 3D

Procedia PDF Downloads 381

Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

Procedia PDF Downloads 129

Authors: M. Mlhem

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Geotechnical engineers face many problematic soils upon construction and they have the choice for replacing these soils with more appropriate soils or attempting to improve the engineering properties of the soil through a suitable soil stabilization technique. Mostly, improving soils is environmental, easier and more economical than other solutions. Stabilization soils technique is applied by introducing a cementing agent or by injecting a substance to fill the pore volume. Chemical stabilizers are divided into two groups: traditional agents such as cement or lime and non-traditional agents such as polymers. This paper studies the effect of epoxy additives on the compression strength of four types of soil and then compares with the effect of cement on the compression strength for the same soils. Overall, the epoxy additives are more effective in increasing the strength for different types of soils regardless its classification. On the other hand, there was no clear relation between studied parameters liquid limit, passing No.200, unit weight and between the strength of samples for different types of soils.

Keywords: additives, clay, compression strength, epoxy, stabilization

Procedia PDF Downloads 118

Authors: Arya Prakash Padhi, Anupam Chakrabarti, Rajib Chowdhury

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Topology or layout optimization often gives better performing economic structures and is very helpful in the conceptual design phase. But traditionally it is being done in finite element-based optimization schemes which, although gives a good result, is very time-consuming especially in 3D structures. Among other alternatives machine learning, especially deep learning-based methods, have a very good potential in resolving this computational issue. Here convolutional neural network (3D-CNN) based variational auto encoder (VAE) is trained using a dataset generated from commercially available topology optimization code ABAQUS Tosca using solid isotropic material with penalization (SIMP) method for compliance minimization. The encoded data in latent space is then fed to a 3D generative adversarial network (3D-GAN) to generate the outcome in 64x64x64 size. Here the network consists of 3D volumetric CNN with rectified linear unit (ReLU) activation in between and sigmoid activation in the end. The proposed network is seen to provide almost optimal results with significantly reduced computational time, as there is no iteration involved.

Keywords: 3D generative adversarial network, deep learning, structural topology optimization, variational auto encoder

Procedia PDF Downloads 167

Authors: Kwestan Salimi, Jenna Jacoby, Michelle Basham, Amy Cerato

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The extreme annual weather patterns of the central United States have increased the need for underground shelters for protection from destructive tornadic activity. However, very few residential homes have basements due to the added construction expense and the prevalence of expansive soils covering the central portion of the United States. These expansive soils shrink and swell, increasing earth pressure on basement walls. To mitigate the effect of expansive soils on basement walls, this study performed bench-scale tests using a common natural expansive soil mitigated with a backfill layer of crumb rubber. The results revealed that at 80% soil compaction, a 1:6 backfill height to total height ratio produced a 66% reduction in swell pressure. However, this percent reduction decreased to 27% for 90% soil compaction. It was also found that there is a strong linear correlation between compaction percentage and reduction in swell pressure when using the same backfill height to total height ratio. Using this correlation and extrapolating to 95% compaction, the percent reduction in swell pressure was approximately 12%.

Keywords: expansive soils, swell/shrink, swell pressure, stabilization, crumb rubber

Procedia PDF Downloads 153

Authors: Saeed Delara, Kendra MacKay

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Varying methodologies are considered for pile design for both Russian and Western approaches. Although both approaches rely on toe and side frictional resistances, different calculation methods are proposed to estimate pile capacity. The Western approach relies on compactness (internal friction angle) of soil for cohesionless soils and undrained shear strength for cohesive soils. The Russian approach relies on grain size for cohesionless soils and liquidity index for cohesive soils. Though most recommended methods in the Western approaches are relatively simple methods to predict pile settlement, the Russian approach provides a detailed method to estimate single pile and pile group settlement. Details to calculate pile axial capacity and settlement using the Russian and Western approaches are discussed and compared against field test results.

Keywords: pile capacity, pile settlement, Russian approach, western approach

Procedia PDF Downloads 159

Authors: Yaofei Jiang, Liangqing Wang, Yanjun Xu

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In recent years, geological explorations have been carried out on the Rumei hydropower station, China. After preliminary analysis of results, the mainly problem of slope in reservoir area is about the stability of accumulation body. It is found that there are 23 accumulations in various sizes in the reservoir area, and most of them are unfavorable geological bodies. Three typical (No. 1, 7, 17) accumulation body slopes were selected as subjects to investigate the stability of the slopes. Take No. 1 accumulation body slope as an example and basic geological condition investigation and formation mechanism analysis were carried out to study the stability and geological analysis of engineering influence of the slope. The accumulation body in the research area distributes along the river with natural slope of 32° ~ 37° which is the natural angle of repose of gravel. The formation mechanism is analyzed based on the composition and structure of the accumulation body. The middle and lower part of the body is dense full of gravel soil mixed with a small amount of sand gravel which is stable. In the upper part, gravel soil is interbedded with bad cemented gravel which as a weak surface is not conducive to slope stability. Under the natural condition before storing water, the underground water level is deep buried, mainly distributed in the bedrock, and the surface and groundwater discharge conditions of the accumulation body are good, which is beneficial to the stability of slope. The safety coefficient calculated by the limit equilibrium method is 1.14, which indicates the slope is basically stable. However, the safety coefficient drops to 1.02 when the normal storage level is 2895m, which is in a dangerous state. The accumulation body will be destabilized by a small-area instability to large-scale or overall instability.

Keywords: accumulation body slope, stability evaluation, geological engineering investigation, effect of storing water

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Authors: Ksenia Meshkova

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With increasing amount of multimedia data, it is very important to automate and speed up the process of obtaining meta. This process means not just recognition of some object or its movement, but recognition of the entire scene versus separate frames and having timeline segmentation as a final result. Labeling datasets is time consuming, besides, attributing characteristics to particular scenes is clearly difficult due to their nature. In this article, we will consider autoencoders application to unsupervised scene recognition and clusterization based on interpretable features. Further, we will focus on particular types of auto encoders that relevant to our study. We will take a look at the specificity of deep learning related to information theory and rate-distortion theory and describe the solutions empowering poor interpretability of deep learning in media content processing. As a conclusion, we will present the results of the work of custom framework, based on autoencoders, capable of scene recognition as was deeply studied above, with highlights generation resulted out of this recognition. We will not describe in detail the mathematical description of neural networks work but will clarify the necessary concepts and pay attention to important nuances.

Keywords: neural networks, computer vision, representation learning, autoencoders

Procedia PDF Downloads 116

Authors: William Li

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Lane-change path planning is a crucial and yet complex task in autonomous driving. The traditional path planning approach based on a system of carefully-crafted rules to cover various driving scenarios becomes unwieldy as more and more rules are added to deal with exceptions and corner cases. This paper proposes to divide the entire path planning to two stages. In the first stage the ego vehicle travels longitudinally in the source lane to reach a safe state. In the second stage the ego vehicle makes lateral lane-change maneuver to the target lane. The paper derives the safe state conditions based on lateral lane-change maneuver calculation to ensure collision free in the second stage. To determine the acceleration sequence that minimizes the time to reach a safe state in the first stage, the paper proposes three schemes, namely, kinetic model based optimization, deep reinforcement learning, and 5G vehicle-to-vehicle (V2V) communications. The paper investigates these schemes via simulation. The model-based optimization is sensitive to the model assumptions. The deep reinforcement learning is more flexible in handling scenarios beyond the model assumed by the optimization. The 5G V2V eliminates uncertainty in predicting future behaviors of surrounding vehicles by sharing driving intents and enabling cooperative driving.

Keywords: lane change, path planning, autonomous driving, deep reinforcement learning, 5G, V2V communications, connected vehicles

Procedia PDF Downloads 229

Authors: Youji Miyake

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In aircraft assembly, a large number of preparatory holes are required for screw and rivet joints. Currently, many holes are drilled manually because it is difficult to machine the holes using conventional computerized numerical control(CNC) machines. The application of industrial robots to drill the hole has been considered as an alternative to the CNC machines. However, the rigidity of robot arms is so low that vibration is likely to occur during drilling. In this study, it is proposed constant-load feed machining as a method to perform high-precision drilling while minimizing the thrust force, which is considered to be the cause of vibration. In this method, the drill feed is realized by a constant load applied onto the tool so that the thrust force is theoretically kept below the applied load. The performance of the proposed method was experimentally examined through the deep hole drilling of plastic and simultaneous drilling of metal/plastic stack plates. It was confirmed that the deep hole drilling and simultaneous drilling could be performed without generating vibration by controlling the tool feed rate in the appropriate range.

Keywords: constant load feed machining, robotic drilling, deep hole, simultaneous drilling

Procedia PDF Downloads 188

Authors: Nataliya Apanovich, Mark Wright

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Uganda is among the poorest but fastest growing populations in the world. Its annual population growth of 3% puts additional stress through land fragmentation, agricultural intensification, and deforestation on already highly weathered tropical (Ferralsol) soils. All of these factors lead to decreased agricultural yields and consequently diminished food security. The central region of Uganda, Buganda Kingdom, is especially vulnerable in terms of food security as its high population density coupled with mismanagement of natural resources led to gradual loss of its soil and even changes in microclimate. These changes are negatively affecting livelihoods of smallholder farmers who comprise 80% of all population in Uganda. This research focuses on biochar for soil remediation in Masaka District, Uganda. If produced on a small scale from locally sourced materials, biochar can increase the quality of soil in a cost and time effective manner. To assess biochar potential, 151 smallholder farmers were interviewed on the types of crops grown, agricultural residues produced and their use, as well as on attitudes towards biochar use and its production on a small scale. The interviews were conducted in 7 sub-counties, 32 parishes, and 92 villages. The total farmland covered by the study was 606.2 kilometers. Additional information on the state of agricultural development and environmental degradation in the district was solicited from four local government officials via informal interviews. This project has been conducted in collaboration with the international agricultural research institution, Makerere University in Kampala, Uganda. The results of this research can have implications on the way farmers perceive the value of their agricultural residues and what they decide to do with them. The underlying objective is to help smallholders in degraded soils increase their agricultural yields through the use of biochar without diverting the already established uses of agricultural residues to a new soil management practice.

Keywords: agricultural residues, biochar, central Uganda, food security, soil erosion, soil remediation

Procedia PDF Downloads 278

Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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This study presents the design and implementation of an experimental chassis-level system for various control applications. Specifically, the height level of the chassis is controlled using proportional integrated derivative, fuzzy logic, and deep learning control methods. Real-time data obtained from height and pressure sensors installed in a 6x2 truck chassis, in combination with pulse-width modulation signal values, are utilized during the tests. A prototype pneumatic system of a 6x2 truck is added to the setup, which enables the Smart Pneumatic Actuators to function as if they were in a real-world setting. To obtain real-time signal data from height sensors, an Arduino Nano is utilized, while a Raspberry Pi processes the data using Matlab/Simulink and provides the correct output signals to control the Smart Pneumatic Actuator in the truck chassis. The objective of this research is to optimize the time it takes for the chassis to level down and up under various loads. To achieve this, proportional integrated derivative control, fuzzy logic control, and deep learning techniques are applied to the system. The results show that the deep learning method is superior in optimizing time for a non-linear system. Fuzzy logic control with a triangular membership function as the rule base achieves better outcomes than proportional integrated derivative control. Traditional proportional integrated derivative control improves the time it takes to level the chassis down and up compared to an uncontrolled system. The findings highlight the superiority of deep learning techniques in optimizing the time for a non-linear system, and the potential of fuzzy logic control. The proposed approach and the experimental results provide a valuable contribution to the field of control, automation, and systems engineering.

Keywords: automotive, chassis level control, control systems, pneumatic system control

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Authors: John Pomerat, Aviv Segev

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Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.

Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning

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Authors: Huseyn Nuraddin Qurbanov

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The reliable food supply of the population of the republic is one of the main directions of the state's economic policy. Grain growing, which is the basis of agriculture, is important in this area. In the cultivation of cereals on the slopes, the application of equal amounts of mineral fertilizers the under the soil before sowing is a very important technological process. The low level of technical equipment in this area prevents producers from providing the country with the necessary quality cereals. Experience in the operation of modern technical means has shown that, at present, there is a need to provide an equal amount of fertilizer on the slopes to under the soil, fully meeting the agro-technical requirements. No fundamental changes have been made to the industrial machines that fertilize the under the soil, and unequal application of fertilizers under the soil on the slopes has been applied. This technological process leads to the destruction of new seedlings and reduced productivity due to intolerance to frost during the winter for the plant planted in the fall. In special climatic conditions, there is an optimal fertilization rate for each agricultural product. The application of fertilizers to the soil is one of the conditions that increase their efficiency in the field. As can be seen, the development of a new technical proposal for fertilizing and plowing the slopes in equal amounts on the slopes, improving the technological and design parameters, and taking into account the physical and mechanical properties of fertilizers is very important. Taking into account the above-mentioned issues, a combined plough was developed in our laboratory. Combined plough carries out pre-sowing technological operation in the cultivation of cereals, providing a smooth equal amount of mineral fertilizers under the soil on the slopes. Mathematical models of a smooth spreader that evenly distributes fertilizers in the field have been developed. Thus, diagrams and graphs obtained without distribution on the 8 partitions of the smooth spreader are constructed under the inclined angles of the slopes. Percentage and productivity of equal distribution in the field were noted by practical and theoretical analysis.

Keywords: combined plough, mineral fertilizer, equal sowing, fertilizer norm, grain-crops, sowing fertilizer

Procedia PDF Downloads 133

Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

Procedia PDF Downloads 138

Authors: Namik Kemal Sonmez, Sahriye Sonmez, Hasan Rasit Turkkan, Hatice Tuba Selcuk

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In this study, the spatial variation of plant and soil nutrition contents of tomato plants grown in greenhouses was investigated in Elmalı region of Antalya. For this purpose, total of 19 sampling points were determined. Coordinates of each sampling points were recorded by using a hand-held GPS device and were transferred to satellite data in GIS. Soil samples were collected from two different depths, 0-20 and 20-40 cm, and leaf were taken from different tomato greenhouses. The soil and plant samples were analyzed for N, P and K. Then, attribute tables were created with the analyses results by using GIS. Data were analyzed and semivariogram models and parameters (nugget, sill and range) of variables were determined by using GIS software. Kriged maps of variables were created by using nugget, sill and range values with geostatistical extension of ArcGIS software. Kriged maps of the N, P and K contents of plant and soil samples showed patchy or a relatively smooth distribution in the study areas. As a result, the N content of plants were sufficient approximately 66% portion of the tomato productions. It was determined that the P and K contents were sufficient of 70% and 80% portion of the areas, respectively. On the other hand, soil total K contents were generally adequate and available N and P contents were found to be highly good enough in two depths (0-20 and 20-40 cm) 90% portion of the areas.

Keywords: Elmali, nutrients, springs greenhouses, spatial variation, tomato

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Authors: Dhanesh Sing Das, Bharat Tadikonda Venkata

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Collapsible soils undergo significant volume reduction upon wetting under the pre-existing mechanically applied normal stress (inundation pressure). These soils exhibit a very high strength in air-dried conditions and can carry up to a considerable magnitude of normal stress without undergoing significant volume change. The soil strength is, however, lost upon saturation and results in a sudden collapse of the soil structure under the existing mechanical stress condition. The intrusion of water into the dry deposits of such soil causes ground subsidence leading to damages in the overlying buildings/structures. A study on the wetting-induced volume change behavior of collapsible soils is essential in dealing with the ground subsidence problems in various geotechnical engineering practices. The collapse of loosely compacted Kaolin soil upon wetting under various inundation pressures has been reported in recent studies. The collapse in the Kaolin soil is attributed to the alteration in the soil particle-particle association (fabric) resulting due to the changes in the various inter-particle (microscale) forces induced by the water saturation. The inundation pressure plays a significant role in the fabric evolution during the wetting process, thus controls the collapse potential of the compacted soil. A microstructural study is useful to understand the collapse mechanisms at various pore-fabric levels under different inundation pressure. Kaolin soil compacted to a dry density of 1.25 g/cc was used in this work to study the wetting-induced volume change behavior under different inundation pressures in the range of 10-1600 kPa. The compacted specimen of Kaolin soil exhibited a consistent collapse under all the studied inundation pressure. The collapse potential was observed to be increasing with an increase in the inundation pressure up to a maximum value of 13.85% under 800 kPa and then decreased to 11.7% under 1600 kPa. Microstructural analysis was carried out based on the fabric images and the pore size distributions (PSDs) obtained from FESEM analysis and mercury intrusion porosimetry (MIP), respectively. The PSDs and the soil fabric images of ‘as-compacted’ specimen and post-collapse specimen under 400 kPa were analyzed to understand the changes in the soil fabric and pores due to wetting. The pore size density curve for the post-collapse specimen was found to be on the finer side with respect to the ‘as-compacted’ specimen, indicating the reduction of the larger pores during the collapse. The inter-aggregate pores in the range of 0.1-0.5μm were identified as the major contributing pore size classes to the macroscopic volume change. Wetting under an inundation pressure results in the reduction of these pore sizes and lead to an increase in the finer pore sizes. The magnitude of inundation pressure influences the amount of reduction of these pores during the wetting process. The collapse potential was directly related to the degree of reduction in the pore volume contributed by these pore sizes.

Keywords: collapse behavior, inundation pressure, kaolin, microstructure

Procedia PDF Downloads 133

Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Daniel Osieko Okach, Joseph Otieno Ondier, Gerhard Rambold, John Tenhunen, Bernd Huwe, Dennis Otieno

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Increased human activities such as grazing, logging, and agriculture alongside unpredictable rainfall patterns have been detrimental to the ecosystem service delivery, therefore compromising its productivity potential. This study aimed at simulating the impact of drought (50%) and enhanced rainfall (150%) on the future herbaceous CO2 uptake, biomass production and soil C:N dynamics in a humid savanna ecosystem influenced by livestock grazing. Rainfall pattern was predicted using manipulation experiments set up to reduce (50%) and increase (150%) ambient (100%) rainfall amounts in grazed and non-grazed plots. The impact of manipulated rainfall regime on herbaceous CO2 fluxes, biomass production and soil C:N dynamics was measured against volumetric soil water content (VWC) logged every 30 minutes using the 5TE (Decagon Devices Inc., Washington, USA) soil moisture sensors installed (at 20 cm soil depth) in every plots. Herbaceous biomass was estimated using destructive method augmented by standardized photographic imaging. CO2 fluxes were measured using the ecosystem chamber method and the gas analysed using LI-820 gas analyzer (USA). C:N ratio was calculated from the soil carbon and Nitrogen contents (analyzed using EA2400CHNS/O and EA2410 N elemental analyzers respectively) of different plots under study. The patterning of VWC was directly influenced by the rainfall amount with lower VWC observed in the grazed compared to the non-grazed plots. Rainfall variability, grazing and their interaction significantly affected changes in VWC (p < 0.05) and subsequently total biomass and CO2 fluxes. VWC had a strong influence on CO2 fluxes under 50% rainfall reduction in the grazed (r2 = 0.91; p < 0.05) and ambient rainfall in the ungrazed (r2 = 0.77; p < 0.05). The dependence of biomass on VWC across plots was enhanced under grazed (r2 = 0.78 - 0.87; p < 0.05) condition as compared to ungrazed (r2 = 0.44 - 0.85; p < 0.05). The C:N ratio was however not correlated to VWC across plots. This study provides insight on how the predicted trends in humid savanna will respond to changes influenced by rainfall variability and livestock grazing and consequently the sustainable management of such ecosystems.

Keywords: CO2 fluxes, rainfall manipulation, soil properties, sustainability

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Authors: S. Amirhossein Tabatabaei, Abdulghader A. Aldaeef, Mohammad T. Rayhani

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As the permafrost continues to melt in the northern regions due to global warming, a soil-water mixture is left behind with drastically lower strength; a phenomenon that directly impacts the resilience of existing structures and infrastructure systems. The frozen soil-structure interaction, which in ice-poor soils is controlled by both interface shear and ice-bonding, changes its nature into a sole frictional state. Adfreeze, the controlling mechanism in frozen soil-structure interaction, diminishes as the ground temperature approaches zero. The main purpose of this paper is to capture the altered behaviour of frozen interface with respect to rising temperature, especially near melting states. A series of pull-out tests are conducted on model piles inside a cold room to study how the strength parameters are influenced by the phase change in ice-poor soils. Steel model piles, embedded in artificially frozen cohesionless soil, are subjected to both sustained pull-out forces and constant rates of displacement to observe the creep behaviour and acquire load-deformation curves, respectively. Temperature, as the main variable of interest, is increased from a lower limit of -10°C up to the point of melting. During different stages of the temperature rise, both skin deformations and temperatures are recorded at various depths along the pile shaft. Significant reduction of pullout capacity and accelerated creep behaviour is found to be the primary consequences of rising temperature. By investigating the different pull-out capacities and deformations measured during step-wise temperature change, characteristics of the transition from frozen to unfrozen soil-structure interaction are studied.

Keywords: Adfreeze, frozen soil-structure interface, ice-poor soils, pull-out capacity, warming permafrost

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: Masood Varshosaz, Kamyar Hasanpour

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In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Oustani Mabrouka, Halilat Md Tahar, Hannachi Slimane

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This study was conducted on representative and contrasting soils of arid regions. It focuses on the compared influence of two organic fertilizers: poultry manure (PM) and bovine manure (BM) on improving the microbial functioning of non-saline (SS) and saline (SSS) soils, in particularly, the process of mineralization of nitrogen and carbon. The microbiological activity was estimated by respirometric test (CO2–C emissions) and the extraction of two forms of mineral nitrogen (NH4+-N and NO3--N). Thus, after 56 days of incubation under controlled conditions (28 degrees and 80 per cent of the field capacity), the two types of manures showed that the mineralization activity varies according to type of soil and the organic substrate itself. However, the highest cumulative quantities of CO2–C, NH4+–N and NO3-–N obtained at the end of incubation were recorded in non-saline (SS) soil treated with poultry manure with 1173.4, 4.26 and 8.40 mg/100 g of dry soil, respectively. The reductions in rates of release of CO2–C and of nitrification under saline conditions were 21 and 36, 78 %, respectively. The influence of organic substratum on the microbial density shows a stimulating effect on all microbial groups studied. The whole results show the usefulness of two types of manures for the improvement of the microbiological functioning of arid soils.

Keywords: Salinity, Organic matter, Microorganisms, Mineralization, Nitrogen, Carbon, Arid regions

Procedia PDF Downloads 273

Authors: Chahrazed Salhi, Ayoub Zeroual, Yasmina Hamitouche

Abstract:

Soil water erosion poses a significant threat to the watersheds in Algeria today. The degradation of storage capacity in large dams over the past two decades, primarily due to erosion, necessitates a comprehensive understanding of the factors that contribute to soil erosion. The Isser watershed, located in the Northwestern region of Algeria, faces additional challenges such as recurrent droughts and the presence of delicate marl and clay outcrops, which amplify its susceptibility to water erosion. This study aims to employ advanced techniques such as Geographic Information Systems (GIS) and Remote Sensing (RS), in conjunction with the Canonical Correlation Analysis (CCA) method and Soil Water Assessment Tool (SWAT) model, to predict specific erosion patterns and analyze the key factors influencing erosion in the Isser basin. To accomplish this, an array of data sources including rainfall, climatic, hydrometric, land use, soil, digital elevation, and satellite data were utilized. The application of the SWAT model to the Isser basin yielded an average annual soil loss of approximately 16 t/ha/year. Particularly high erosion rates, exceeding 12 T/ha/year, were observed in the central and southern parts of the basin, encompassing 41% of the total basin area. Through Canonical Correlation Analysis, it was determined that vegetation cover and topography exerted the most substantial influence on erosion. Consequently, the study identified significant and spatially heterogeneous erosion throughout the study area. The impact of land topography on soil loss was found to be directly proportional, while vegetation cover exhibited an inverse proportional relationship. Modeling specific erosion for the Ladrat dam sub-basin estimated a rate of around 39 T/ha/year, thus accounting for the recorded capacity loss of 17.80% compared to the bathymetric survey conducted in 2019. The findings of this research provide valuable decision-support tools for soil conservation managers, empowering them to make informed decisions regarding soil conservation measures.

Keywords: Isser watershed, RS, CCA, SWAT, vegetation cover, topography

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Authors: Višnjica Vučetić, Mislav Anić, Jelena Bašić, Petra Sviličić, Ivana Tomašević

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The Agroclimatic Atlas of Croatia (Atlas) for the periods 1981–2010 and 1991–2020 is monograph of six chapters in digital form. Detailed descriptions of particular agroclimatological data are given in separate chapters as follows: agroclimatic indices based on air temperature (degree days, Huglin heliothermal index), soil temperature, water balance components (precipitation, potential evapotranspiration, actual evapotranspiration, soil moisture content, runoff, recharge and soil moisture loss) and fire weather indices. The last chapter is a description of the digital methods for the spatial interpolations (R and GIS). The Atlas comprises textual description of the relevant climate characteristic, maps of the spatial distribution of climatological elements at 109 stations (26 stations for soil temperature) and tables of the 30-year mean monthly, seasonal and annual values of climatological parameters at 24 stations. The Atlas was published in 2021, on the seventieth anniversary of the agrometeorology development at the Meteorological and Hydrological Service of Croatia. It is intended to support improvement of sustainable system of agricultural production and forest protection from fire and as a rich source of information for agronomic and forestry experts, but also for the decision-making bodies to use it for the development of strategic plans.

Keywords: agrometeorology, agroclimatic indices, soil temperature, water balance components, fire weather index, meteorological and hydrological service of Croatia

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Authors: Swapneel U. Naphade, Dushyant A. Patil, Satyabodh M. Kulkarni

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In this work, we propose the concept and geometrical design of a soil moisture control system (SMCS) module by following the product development approach to develop an inexpensive, easy to use and quick to install product targeted towards agriculture practitioners. The module delivers water to the agricultural land efficiently by sensing the soil moisture and activating the delivery valve. We start with identifying the general needs of the potential customer. Then, based on customer needs we establish product specifications and identify important measuring quantities to evaluate our product. Keeping in mind the specifications, we develop various conceptual solutions of the product and select the best solution through concept screening and selection matrices. Then, we develop the product architecture by integrating the systems into the final product. In the end, the geometric design is done using human factors engineering concepts like heuristic analysis, task analysis, and human error reduction analysis. The result of human factors analysis reveals the remedies which should be applied while designing the geometry and software components of the product. We find that to design the best grip in terms of comfort and applied force, for a power-type grip, a grip-diameter of 35 mm is the most ideal.

Keywords: agriculture, human factors, product design, soil moisture control

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Authors: V. Ivanov, J. Chu, V. Stabnikov

Abstract:

Low cost, sustainable, and environmentally friendly microbial cements, grouts, polysaccharides and bioplastics are useful in construction and geotechnical engineering. Construction-related biotechnologies are based on activity of different microorganisms: urease-producing, acidogenic, halophilic, alkaliphilic, denitrifying, iron- and sulphate-reducing bacteria, cyanobacteria, algae, microscopic fungi. The bio-related materials and processes can be used for the bioaggregation, soil biogrouting and bioclogging, biocementation, biodesaturation of water-satured soil, bioencapsulation of soft clay, biocoating, and biorepair of the concrete surface. Altogether with the most popular calcium- and urea based biocementation, there are possible and often are more effective such methods of ground improvement as calcium- and magnesium based biocementation, calcium phosphate strengthening of soil, calcium bicarbonate biocementation, and iron- or polysaccharide based bioclogging. The construction-related microbial biotechnologies have a lot of advantages over conventional construction materials and processes.

Keywords: ground improvement, biocementation, biogrouting, microorganisms

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Authors: Harison Masih, Jyotsna Kiran Peter, Sundara Singh, Geetha Singh

Abstract:

The present investigation deals with diversity of dermatophytes and keratinophilic fungi from different tourist spots such as Agra Fort, Akbar tomb, It-Mat-Ud-Daulah, Mariam tomb, Radha Swami Bagh, and Taj Mahal of Agra City. These fungi are medically important which causes various infections and diseases in humans and animals. The main reservoir of these pathogens are the keratinous substances that increases due to birds and animal activities in the vicinity of monuments, where thousands (5413266) annual visitors from all over the world are visiting. The soil samples were subjected to isolate the pathogenic fungi through bait technique (buffalo skin, chicken feathers, human hair and goat tail hair). Baits were spread over the soil samples and incubated at room temperature for 30-35 days and pure culture isolates were maintained in SDA medium, stored at 4°C. Highest number of visitors were (3906453) from Taj Mahal, minimum 10785 at Mariam tomb annually, the total 271 isolates were encountered from soil samples out of these 18 genera and 38 species were found in different season. Highest incidence was 4.79% frequency shown by Chrysosporium keratinophilum while least 738% frequency occurrence by Trichophyton simii in soil samples. From the present study it was concluded that the incidence of pathogenic fungal isolates were the common in tourists soil that are etiological agents of superficial mycosis. Thus, both human and animal activity seemed to play an important role in occurrence and distribution of keratinophilic and related dermatophytes at various tourist places of Agra city.

Keywords: dermatophytic fungal diversity, bait technique, visitors at tourist spots, human and animal activities, soil samples

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Authors: Radheshyam Yadav, Ramakrishna Wusirika

Abstract:

Plant Growth Promoting Bacteria (PGPB) and Arbuscular Mycorrhizal (AM) Fungi are ubiquitous in soil and often very critical for crop yield and agriculture sustainability, and this has motivated the agricultural practices to support and promote PGPB and AM Fungi in agriculture. PGPB can be involved in a range of processes that affect Nitrogen (N) and Phosphorus (P) transformations in soil and thus influence nutrient availability and uptake to the plants. A field study with two wheat cultivars, HD-3086, and HD-2967 was performed in Malwa region, Bathinda of Punjab, India, to evaluate the effect of native and non-native PGPB alone and in combination with AM fungi as an inoculant on wheat grain yield, nutrient uptake and soil health parameters (dehydrogenase, urease, β‐glucosidase). Our results showed that despite an early insignificant increase in shoot length, plants treated with PGPB (Bacillus sp.) and AM Fungi led to a significant increase in shoot growth at maturity, aboveground biomass, nitrogen (45% - 40%) and phosphorus (40% - 34%) content in wheat grains relative to untreated control plants. Similarly, enhanced grain yield and nutrients uptake i.e. copper (27.15% - 36.25%) iron (43% - 53%) and zinc (44% - 47%) was recorded in PGPB and AM Fungi treated plants relative to untreated control. Overall, inoculation with native PGPB alone and in combination with AM Fungi provided benefits to enhance grain yield, wheat biofortification, and improved soil fertility, despite this effect varied depending on different PGPB isolates and wheat cultivars. These field study results provide evidence of the benefits of agricultural practices involving native PGPB and AM Fungi to the plants. These native strains and AM Fungi increased accumulations of copper, iron, and zinc in wheat grains, enhanced grain yield, and soil fertility.

Keywords: AM Fungi, biofortification, PGPB, soil microbial enzymes

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Authors: N. N. Lingaraju, A. Sathish, K. N. Geetha, C. A. Srinivasamurthy, S. Bhaskar

Abstract:

Studies on spent wash utilization as a nutrient source through 'Effect of distillery spentwash application on soil properties and yield of maize (Zea may L.) and finger millet (Eleusine coracana (L.) G)' was carried out in Malavalli Taluk, Mandya District, Karnataka State, India. The study was conducted in fourteen different locations of Malavalli (12) and Maddur taluk (2) involving maize and finger millet as a test crop. The spentwash was characterized for various parameters like pH, EC, total NPK, Na, Ca, Mg, SO₄, Fe, Zn, Cu, Mn and Cl content. It was observed from the results that the pH was slightly alkaline (7.45), EC was excess (23.3 dS m⁻¹), total NPK was 0.12, 0.02, and 1.31 percent respectively, Na, Ca, Mg and SO₄ concentration was 664, 1305, 745 and 618 (mg L⁻¹) respectively, total solid content was quite high (6.7%), Fe, Zn, Cu, Mn, values were 23.5, 5.70, 3.64, 4.0 mg L⁻¹, respectively. The crops were grown by adopting different crop management practices after application of spentwash at 100 m³ ha⁻¹ to the identified farmer fields. Soil samples were drawn at three stages i.e., before sowing of crop, during crop growth stage and after harvest of the crop at 2 depths (0-30 and 30-60 cm) and analyzed for pH, EC, available K and Na parameters by adopting standard procedures. The soil analysis showed slightly acidic reaction (5.93), normal EC (0.43 dS m⁻¹), medium available potassium (267 kg ha⁻¹) before application of spentwash. Application of spentwash has enhanced pH level of soil towards neutral (6.97), EC 0.25 dS m⁻¹, available K2O to 376 kg ha⁻¹ and sodium content of 0.73 C mol (P+) kg⁻¹ during the crop growth stage. After harvest of the crops soil analysis data indicated a decrease in pH to 6.28, EC of 0.22 dS m⁻¹, available K₂O to 316 kg ha⁻¹ and Na 0.52 C mol (P⁺) kg⁻¹ compared with crop growth stage. The study showed that, there will be enhancement of potassium levels if the spentwash is applied once to dryland. The yields of both the crops were quantified and found to be in the range of 35.65 to 65.55 q ha⁻¹ and increased yield to the extent of 13.36-22.36 percent as compared to control field (11.36-22.33 q ha⁻¹) in maize crop. Also, finger millet yield was increased with the spentwash application to the extent of 14.21-20.49 percent (9.5-17.73 q ha⁻¹) higher over farmers practice (8.15-14.15 q ha⁻¹).

Keywords: distillery spentwash, finger millet, maize, waste water

Procedia PDF Downloads 348