Search results for: soil texture prediction
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 5621

Search results for: soil texture prediction

4001 Comparing Machine Learning Estimation of Fuel Consumption of Heavy-Duty Vehicles

Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

Abstract:

Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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4000 Characterization of the Microbial Induced Carbonate Precipitation Technique as a Biological Cementing Agent for Sand Deposits

Authors: Sameh Abu El-Soud, Zahra Zayed, Safwan Khedr, Adel M. Belal

Abstract:

The population increase in Egypt is urging for horizontal land development which became a demand to allow the benefit of different natural resources and expand from the narrow Nile valley. However, this development is facing challenges preventing land development and agriculture development. Desertification and moving sand dunes in the west sector of Egypt are considered the major obstacle that is blocking the ideal land use and development. In the proposed research, the sandy soil is treated biologically using Bacillus pasteurii bacteria as these bacteria have the ability to bond the sand partials to change its state of loose sand to cemented sand, which reduces the moving ability of the sand dunes. The procedure of implementing the Microbial Induced Carbonate Precipitation Technique (MICP) technique is examined, and the different factors affecting on this process such as the medium of bacteria sample preparation, the optical density (OD600), the reactant concentration, injection rates and intervals are highlighted. Based on the findings of the MICP treatment for sandy soil, conclusions and future recommendations are reached.

Keywords: soil stabilization, biological treatment, microbial induced carbonate precipitation (MICP), sand cementation

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3999 Numerical Analysis and Parametric Study of Granular Anchor Pile on Expansive Soil Using Finite Element Method: Case of Addis Ababa, Bole Sub-City

Authors: Abdurahman Anwar Shfa

Abstract:

Addis Ababa is among the fastest-growing urban areas in the country. There are many new constructions of public and private condominiums and large new low rising residential buildings for residents. But the wide range of heaving problems of expansive soil in the city become a major difficulty for the construction sector, especially in low rising buildings, by causing different problems such as distortion and cracking of floor slabs, cracks in grade beams, and walls, jammed or misaligned Doors and Windows; failure of blocks supporting grade beams. Hence an attractive and economical design solution may be required for such type of problem. Therefore, this research works to publicize a recent innovation called the Granular Anchor Pile system for the reduction of the heave effect of expansive soil. This research is written for the objective of numerical investigation of the behavior of Granular Anchor Pile under the heave using Finite element analysis PLAXIS 3D program by means of studying the effect of different parameters like length of the pile, diameter of pile, and pile group by applying prescribed displacement of 10% of pile diameter at the center of granular pile anchor. An additional objective is examining the suitability of Granular Anchor Pile as an alternative solution for heave problems in expansive soils mostly for low rising buildings found in Addis Ababa City, especially in Bole Sub-City, by considering different factors such as the local availability of construction materials, economy for the construction, installation process condition, environmental benefit, time consumption and performance of the pile. Accordingly, the performance of the pile improves when the length of the pile increases. This is due to an increase in the self-weight of the pile and friction mobilized between the pile and soil interface. Additionally, the uplift capacity of the pile decreases when increasing the pile diameter and spacing between the piles in the group due to a reduction in the number of piles in the group. But, few cases show that the uplift capacity of the pile increases with increasing the pile diameter for a constant number of piles in the group and increasing the spacing between the pile and in the case of single pile capacity. This is due to the increment of piles' self-weight and surface area of the pile group and also the decrement of stress overlap in the soil caused by piles respectively. According to the suitability analysis, it is observed that Granular Anchor Pile is sensible or practical to apply for the actual problem of Expansive soil in a low rising building constructed in the country because of its convenience for all considerations.

Keywords: expansive soil, granular anchor pile, PLAXIS, suitability analysis

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3998 Non-Contact Measurement of Soil Deformation in a Cyclic Triaxial Test

Authors: Erica Elice Uy, Toshihiro Noda, Kentaro Nakai, Jonathan Dungca

Abstract:

Deformation in a conventional cyclic triaxial test is normally measured by using point-wise measuring device. In this study, non-contact measurement technique was applied to be able to monitor and measure the occurrence of non-homogeneous behavior of the soil under cyclic loading. Non-contact measurement is executed through image processing. Two-dimensional measurements were performed using Lucas and Kanade optical flow algorithm and it was implemented Labview. In this technique, the non-homogeneous deformation was monitored using a mirrorless camera. A mirrorless camera was used because it is economical and it has the capacity to take pictures at a fast rate. The camera was first calibrated to remove the distortion brought about the lens and the testing environment as well. Calibration was divided into 2 phases. The first phase was the calibration of the camera parameters and distortion caused by the lens. The second phase was to for eliminating the distortion brought about the triaxial plexiglass. A correction factor was established from this phase. A series of consolidated undrained cyclic triaxial test was performed using a coarse soil. The results from the non-contact measurement technique were compared to the measured deformation from the linear variable displacement transducer. It was observed that deformation was higher at the area where failure occurs.

Keywords: cyclic loading, non-contact measurement, non-homogeneous, optical flow

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3997 Groundwater Potential Mapping using Frequency Ratio and Shannon’s Entropy Models in Lesser Himalaya Zone, Nepal

Authors: Yagya Murti Aryal, Bipin Adhikari, Pradeep Gyawali

Abstract:

The Lesser Himalaya zone of Nepal consists of thrusting and folding belts, which play an important role in the sustainable management of groundwater in the Himalayan regions. The study area is located in the Dolakha and Ramechhap Districts of Bagmati Province, Nepal. Geologically, these districts are situated in the Lesser Himalayas and partly encompass the Higher Himalayan rock sequence, which includes low-grade to high-grade metamorphic rocks. Following the Gorkha Earthquake in 2015, numerous springs dried up, and many others are currently experiencing depletion due to the distortion of the natural groundwater flow. The primary objective of this study is to identify potential groundwater areas and determine suitable sites for artificial groundwater recharge. Two distinct statistical approaches were used to develop models: The Frequency Ratio (FR) and Shannon Entropy (SE) methods. The study utilized both primary and secondary datasets and incorporated significant role and controlling factors derived from field works and literature reviews. Field data collection involved spring inventory, soil analysis, lithology assessment, and hydro-geomorphology study. Additionally, slope, aspect, drainage density, and lineament density were extracted from a Digital Elevation Model (DEM) using GIS and transformed into thematic layers. For training and validation, 114 springs were divided into a 70/30 ratio, with an equal number of non-spring pixels. After assigning weights to each class based on the two proposed models, a groundwater potential map was generated using GIS, classifying the area into five levels: very low, low, moderate, high, and very high. The model's outcome reveals that over 41% of the area falls into the low and very low potential categories, while only 30% of the area demonstrates a high probability of groundwater potential. To evaluate model performance, accuracy was assessed using the Area under the Curve (AUC). The success rate AUC values for the FR and SE methods were determined to be 78.73% and 77.09%, respectively. Additionally, the prediction rate AUC values for the FR and SE methods were calculated as 76.31% and 74.08%. The results indicate that the FR model exhibits greater prediction capability compared to the SE model in this case study.

Keywords: groundwater potential mapping, frequency ratio, Shannon’s Entropy, Lesser Himalaya Zone, sustainable groundwater management

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3996 Cooperative Coevolution for Neuro-Evolution of Feed Forward Networks for Time Series Prediction Using Hidden Neuron Connections

Authors: Ravneil Nand

Abstract:

Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature.

Keywords: cooperative coevaluation, feed forward network, problem decomposition, neuron, synapse

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3995 Provision of Slope Stability with Barette Piles: A Case Analysis

Authors: Leyla Yesilbas, M. Sukru Ozcoban, M. Ergenekon Selcuk

Abstract:

From past to present, there is a constant need for engineering structures such as high-rise buildings, wide-span bridges, airports and stadiums, business towers due to technological developments and increasing population. Because of the large loads transferred from the superstructure to the ground layers in these types of structures, the bearing strength and seating problems usually occur on the floors. In order to solve these problems, piled foundations are used by passing the weak soil layers and transferring the loads from the superstructure to the solid soil layers. Considering the factors such as the characteristics of the building to be constructed, the purpose and location of the building, the basic cost of the pile should be at normal levels. When these requirements are taken into consideration, a new basic system called 'Barette Foundation' has been developed. In this thesis, an application made to provide slope stability with 'Baret Piles' was investigated. In addition, the ground parameters obtained from the field and laboratory experiments were numerically modeled using a PLAXİS 2D finite element software and barette piles. The effects of barette piles on slope stability were investigated by numerical analysis, and the results of inclinometer measurements in the field were compared with numerical analysis results.

Keywords: barette pile, PLAXİS 2D, slope, soil

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3994 Numerical Prediction of Entropy Generation in Heat Exchangers

Authors: Nadia Allouache

Abstract:

The concept of second law is assumed to be important to optimize the energy losses in heat exchangers. The present study is devoted to the numerical prediction of entropy generation due to heat transfer and friction in a double tube heat exchanger partly or fully filled with a porous medium. The goal of this work is to find the optimal conditions that allow minimizing entropy generation. For this purpose, numerical modeling based on the control volume method is used to describe the flow and heat transfer phenomena in the fluid and the porous medium. Effects of the porous layer thickness, its permeability, and the effective thermal conductivity have been investigated. Unexpectedly, the fully porous heat exchanger yields a lower entropy generation than the partly porous case or the fluid case even if the friction increases the entropy generation.

Keywords: heat exchangers, porous medium, second law approach, turbulent flow

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3993 Potential Application of Artocarpus odoratisimmus Seed Flour in Bread Production

Authors: Hasmadi Mamat, Noorfarahzilah Masri

Abstract:

The search for lesser known and underutilized crops, many of which are potentially valuable as human and animal foods has been the focus of research in recent years. Tarap (Artocarpus odoratisimmus) is one of the most delicious tropical fruit and can be found extensively in Borneo, particularly in Sabah and Sarawak. This study was conducted in order to determine the proximate composition, mineral contents as well as to study the effect of the seed flour on the quality of bread produced. Tarap seed powder (TSP) was incorporated (up to 20%) with wheat flour and used to produce bread. The moisture content, ash, protein, fat, ash, carbohydrates, and dietary fiber were measured using AOAC methods while the mineral content was determined using AAS. The effect of substitution of wheat flour with Tarap seed flour on the quality of dough and bread was investigated using various techniques. Farinograph tests were applied to determine the effect of seaweed powder on the rheological properties of wheat flour dough, while texture profile analysis (TPA) was used to measure the textural properties of the final product. Besides that sensory evaluations were also conducted. On a dry weight basis, the TSP was composed of 12.50% moisture, 8.78% protein, 15.60% fat, 1.17% ash, 49.65% carbohydrate and 12.30% of crude fiber. The highest mineral found were Mg, followed by K, Ca, Fe and Na respectively. Farinograh results found that as TSP percentage increased, dough consistency, water absorption capacity and development time of dough decreased. Sensory analysis results showed that bread with 10% of TSP was the most accepted by panelists where the highest acceptability score were found for aroma, taste, colour, crumb texture as well as overall acceptance. The breads with more than 10% of TSP obtained lower acceptability score in most of attributes tested.

Keywords: tarap seed, proximate analysis, bread, sensory evaluation

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3992 Chemical and Mineralogical Properties of Soils from an Arid Region of Misurata-Libya: Treated Wastewater Irrigation Impacts

Authors: Khalifa Alatresh, Mirac Aydin

Abstract:

This research explores the impacts of irrigation by treated wastewater (TWW) on the mineralogical and chemical attributes of sandy calcareous soils in the Southern region of Misurata. Soil samples obtained from three horizons (A, B, and C) of six TWW-irrigated pedons (29years) and six other pedons from nearby non-irrigated areas (dry-control). The results demonstrated that the TWW-irrigated pedons had significantly higher salinity (EC), sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP), cation exchange capacity (CEC), available phosphor (AP), total nitrogen (TN), and organic matter (OM) relative to the control pedons. Nonetheless, all the values of interest (EC < 4000 µs/cm < SAR < 13, pH < 8.5 and ESP < 15) remained lower than the thresholds, showing no issues with sodicity or salinity. Irrigated pedons contained significantly higher amounts of total clay and showed an altered distribution of particle sizes and minerals identified (quartz, calcite, microcline, albite, anorthite, and dolomite) within the profile. The observed results included the occurrence of Margarite, Anorthite, Chabazite, and Tridymite minerals after the application of TWW in small quantities that are not enough to influence soil genesis and classification.0,51 cm.

Keywords: treated wastewater, sandy calcareous soils, soil mineralogy, and chemistry

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3991 Application of Particle Image Velocimetry in the Analysis of Scale Effects in Granular Soil

Authors: Zuhair Kadhim Jahanger, S. Joseph Antony

Abstract:

The available studies in the literature which dealt with the scale effects of strip footings on different sand packing systematically still remain scarce. In this research, the variation of ultimate bearing capacity and deformation pattern of soil beneath strip footings of different widths under plane-strain condition on the surface of loose, medium-dense and dense sand have been systematically studied using experimental and noninvasive methods for measuring microscopic deformations. The presented analyses are based on model scale compression test analysed using Particle Image Velocimetry (PIV) technique. Upper bound analysis of the current study shows that the maximum vertical displacement of the sand under the ultimate load increases for an increase in the width of footing, but at a decreasing rate with relative density of sand, whereas the relative vertical displacement in the sand decreases for an increase in the width of the footing. A well agreement is observed between experimental results for different footing widths and relative densities. The experimental analyses have shown that there exists pronounced scale effect for strip surface footing. The bearing capacity factors rapidly decrease up to footing widths B=0.25 m, 0.35 m, and 0.65 m for loose, medium-dense and dense sand respectively, after that there is no significant decrease in . The deformation modes of the soil as well as the ultimate bearing capacity values have been affected by the footing widths. The obtained results could be used to improve settlement calculation of the foundation interacting with granular soil.

Keywords: DPIV, granular mechanics, scale effect, upper bound analysis

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3990 Assessing the Influence of Station Density on Geostatistical Prediction of Groundwater Levels in a Semi-arid Watershed of Karnataka

Authors: Sakshi Dhumale, Madhushree C., Amba Shetty

Abstract:

The effect of station density on the geostatistical prediction of groundwater levels is of critical importance to ensure accurate and reliable predictions. Monitoring station density directly impacts the accuracy and reliability of geostatistical predictions by influencing the model's ability to capture localized variations and small-scale features in groundwater levels. This is particularly crucial in regions with complex hydrogeological conditions and significant spatial heterogeneity. Insufficient station density can result in larger prediction uncertainties, as the model may struggle to adequately represent the spatial variability and correlation patterns of the data. On the other hand, an optimal distribution of monitoring stations enables effective coverage of the study area and captures the spatial variability of groundwater levels more comprehensively. In this study, we investigate the effect of station density on the predictive performance of groundwater levels using the geostatistical technique of Ordinary Kriging. The research utilizes groundwater level data collected from 121 observation wells within the semi-arid Berambadi watershed, gathered over a six-year period (2010-2015) from the Indian Institute of Science (IISc), Bengaluru. The dataset is partitioned into seven subsets representing varying sampling densities, ranging from 15% (12 wells) to 100% (121 wells) of the total well network. The results obtained from different monitoring networks are compared against the existing groundwater monitoring network established by the Central Ground Water Board (CGWB). The findings of this study demonstrate that higher station densities significantly enhance the accuracy of geostatistical predictions for groundwater levels. The increased number of monitoring stations enables improved interpolation accuracy and captures finer-scale variations in groundwater levels. These results shed light on the relationship between station density and the geostatistical prediction of groundwater levels, emphasizing the importance of appropriate station densities to ensure accurate and reliable predictions. The insights gained from this study have practical implications for designing and optimizing monitoring networks, facilitating effective groundwater level assessments, and enabling sustainable management of groundwater resources.

Keywords: station density, geostatistical prediction, groundwater levels, monitoring networks, interpolation accuracy, spatial variability

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3989 Predicting Data Center Resource Usage Using Quantile Regression to Conserve Energy While Fulfilling the Service Level Agreement

Authors: Ahmed I. Alutabi, Naghmeh Dezhabad, Sudhakar Ganti

Abstract:

Data centers have been growing in size and dema nd continuously in the last two decades. Planning for the deployment of resources has been shallow and always resorted to over-provisioning. Data center operators try to maximize the availability of their services by allocating multiple of the needed resources. One resource that has been wasted, with little thought, has been energy. In recent years, programmable resource allocation has paved the way to allow for more efficient and robust data centers. In this work, we examine the predictability of resource usage in a data center environment. We use a number of models that cover a wide spectrum of machine learning categories. Then we establish a framework to guarantee the client service level agreement (SLA). Our results show that using prediction can cut energy loss by up to 55%.

Keywords: machine learning, artificial intelligence, prediction, data center, resource allocation, green computing

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3988 Geological, Engineering Geological, and Hydrogeological Characteristics of the Knowledge Economic City, Al Madinah Al Munawarah, KSA

Authors: Mutasim A. M. Ez Eldin, Tareq Saeid Al Zahrani, Gabel Zamil Al-Barakati, Ibrahim Mohamed AlHarthi, Marwan Mohamed Al Saikhan, Waleed Abdel Aziz Al Aklouk, Waheed Mohamed Saeid Ba Amer

Abstract:

The Knowledge Economic City (KEC) of Al Madinah Al Munawarah is one of the major projects and represents a cornerstone for the new development activities for Al Madinah. The study area contains different geological units dominated by basalt and overlain by surface deposits. The surface soils vary in thickness and can be classified into well-graded SAND with silt and gravel (SW-SM), silty SAND with gravel (SM), silty GRAVEL with sand (GM), and sandy SILTY clay (CL-ML). The subsurface soil obtained from the drilled boreholes can be classified into poorly graded GRAVEL (GP), well-graded GRAVEL with sand (GW), poorly graded GRAVEL with silt (GP-GM), silty CLAYEY gravel with sand (GC-GM), silty SAND with gravel (SM), silt with SAND (ML), and silty CLAY with sand (CL-ML), sandy lean CLAY (CL), and lean CLAY (CL). The relative density of the deposit and the different gravel sizes intercalated with the soil influenced the Standard Penetration Tests (SPT) values. The SPT N values are high and approach refusal even at shallow depths. The shallow refusal depth (0.10 to 0.90m) of the Dynamic Cone Penetration Test (DCPT) was observed. Generally, the soil can be described as inactive with low plasticity and dense to very dense consistency. The basalt of the KEC site is characterized by slightly (W2) to moderately (W3) weathering, their strength ranges from moderate (S4) to very strong (S2), and the Rock Quality Designation (RQD) ranges from very poor (R5) to excellent (R1). The engineering geological map of the KEC characterized the geoengineering properties of the soil and rock materials and classified them into many zones. The high sulphate (SO₄²⁻) and chloride (Cl⁻) contents in groundwater call for protective measures for foundation concrete. The current study revealed that geohazard(s) mitigation measures concerning floods, volcanic eruptions, and earthquakes should be taken into consideration.

Keywords: engineering geology, KEC, petrographic description, rock and soil investigations

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3987 Use of Vapor Corrosion Inhibitor for Tank Bottom Protection

Authors: Muhammad Arsalan Khan Sherwani

Abstract:

The use of Volatile Corrosion Inhibitors (VCI) to protect Aboveground Storage Tank (AST) bottom plates against soil-side corrosion is one of the emerging corrosion prevention methods, specifically for tanks constructed on oily sand pad. Oily sand pad and the presence of air gaps underneath the bottom plates lead to severe corrosion and high metal thickness loss. In such cases, the cathodic protection cannot be fully considered as effective due to Cathodic Protection (CP) current shielding. These situations sometimes result in serious failures on multiple fronts, such as; containment losses, system shutdowns, extensive repairs, environmental impact and safety concerns in case of flammable fluids. Recently, East West Pipeline Department (EWPD) of Saudi Aramco has deployed this technology to one of the crude oil storage tanks, which showed high metal thickness loss during its out of service inspection. Soil-side corrosion rustled in major repairs of bottom plates and ultimately caused enormous unplanned activities in term of time as well as cost. This paper mainly focuses on the methodology of VCI installation, corrosion monitoring system and the expected results of protection.

Keywords: Vapor Corrosion Inhibitor, Soil Side Corrosion, External Corrosion, Above Grade Storage Tank

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3986 Undrained Bearing Capacity of Circular Foundations on two Layered Clays

Authors: S. Benmebarek, S. Benmoussa, N. Benmebarek

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Natural soils are often deposited in layers. The estimation of the bearing capacity of the soil using conventional bearing capacity theory based on the properties of the upper layer introduces significant inaccuracies if the thickness of the top layer is comparable to the width of the foundation placed on the soil surface. In this paper, numerical computations using the FLAC code are reported to evaluate the two clay layers effect on the bearing capacity beneath rigid circular rough footing subject to axial static load. The computation results of the parametric study are used to illustrate the sensibility of the bearing capacity, the shape factor and the failure mechanisms to the layered strength and layered thickness.

Keywords: numerical modeling, circular footings, layered clays, bearing capacity, failure

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3985 Big Data: Appearance and Disappearance

Authors: James Moir

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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

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3984 Managing Shallow Gas for Offshore Platforms via Fit-For-Purpose Solutions: Case Study for Offshore Malaysia

Authors: Noorizal Huang, Christian Girsang, Mohamad Razi Mansoor

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Shallow gas seepage was first spotted at a central processing platform offshore Malaysia in 2010, acknowledged as Platform T in this paper. Frequent monitoring of the gas seepage was performed through remotely operated vehicle (ROV) baseline survey and a comprehensive geophysical survey was conducted to understand the characteristics of the gas seepage and to ensure that the integrity of the foundation at Platform T was not compromised. The origin of the gas back then was unknown. A soil investigation campaign was performed in 2016 to study the origin of the gas seepage. Two boreholes were drilled; a composite borehole to 150m below seabed for the purpose of soil sampling and in-situ testing and a pilot hole to 155m below the seabed, which was later converted to a fit-for-purpose relief well as an alternate migration path for the gas. During the soil investigation campaign, dissipation tests were performed at several layers which were potentially the source or migration path for the gas. Five (5) soil samples were segregated for headspace test, to identify the gas type which subsequently can be used to identify the origin of the gas. Dissipation tests performed at four depth intervals indicates pore water pressure less than 20 % of the effective vertical stress and appear to continue decreasing if the test had not been stopped. It was concluded that a low to a negligible amount of excess pore pressure exist in clayey silt layers. Results from headspace test show presence of methane corresponding to the clayey silt layers as reported in the boring logs. The gas most likely comes from biogenic sources, feeding on organic matter in situ over a large depth range. It is unlikely that there are large pockets of gas in the soil due to its homogeneous clayey nature and the lack of excess pore pressure in other permeable clayey silt layers encountered. Instead, it is more likely that when pore water at certain depth encounters a more permeable path, such as a borehole, it rises up through this path due to the temperature gradient in the soil. As the water rises the pressure decreases, which could cause gases dissolved in the water to come out of solution and form bubbles. As a result, the gas will have no impact on the integrity of the foundation at Platform T. The fit-for-purpose relief well design as well as adopting headspace testing can be used to address the shallow gas issue at Platform T in a cost effective and efficient manners.

Keywords: dissipation test, headspace test, excess pore pressure, relief well, shallow gas

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3983 Finite Difference Based Probabilistic Analysis to Evaluate the Impact of Correlation Length on Long-Term Settlement of Soft Soils

Authors: Mehrnaz Alibeikloo, Hadi Khabbaz, Behzad Fatahi

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Probabilistic analysis has become one of the most popular methods to quantify and manage geotechnical risks due to the spatial variability of soil input parameters. The correlation length is one of the key factors of quantifying spatial variability of soil parameters which is defined as a distance within which the random variables are correlated strongly. This paper aims to assess the impact of correlation length on the long-term settlement of soft soils improved with preloading. The concept of 'worst-case' spatial correlation length was evaluated by determining the probability of failure of a real case study of Vasby test fill. For this purpose, a finite difference code was developed based on axisymmetric consolidation equations incorporating the non-linear elastic visco-plastic model and the Karhunen-Loeve expansion method. The results show that correlation length has a significant impact on the post-construction settlement of soft soils in a way that by increasing correlation length, probability of failure increases and the approach to asymptote.

Keywords: Karhunen-Loeve expansion, probability of failure, soft soil settlement, 'worst case' spatial correlation length

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3982 Prediction of Childbearing Orientations According to Couples' Sexual Review Component

Authors: Razieh Rezaeekalantari

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Objective: The purpose of this study was to investigate the prediction of parenting orientations in terms of the components of couples' sexual review. Methods: This was a descriptive correlational research method. The population consisted of 500 couples referring to Sari Health Center. Two hundred and fifteen (215) people were selected randomly by using Krejcie-Morgan-sample-size-table. For data collection, the childbearing orientations scale and the Multidimensional Sexual Self-Concept Questionnaire were used. Result: For data analysis, the mean and standard deviation were used and to analyze the research hypothesis regression correlation and inferential statistics were used. Conclusion: The findings indicate that there is not a significant relationship between the tendency to childbearing and the predictive value of sexual review (r = 0.84) with significant level (sig = 219.19) (P < 0.05). So, with 95% confidence, we conclude that there is not a meaningful relationship between sexual orientation and tendency to child-rearing.

Keywords: couples referring, health center, sexual review component, parenting orientations

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3981 Congolese Wood in the Antwerp Interwar Interior

Authors: M. Jaenen, M. de Bouw, A. Verdonck, M. Leus

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During the interwar period artificial materials were often preferred, but many Antwerp architects relied on the application of wood for most of the interior finishing works and furnishings. Archival, literature and on site research of interwar suburban townhouses and the Belgian wood and furniture industry gave a new insight to the application of wood in the interwar interior. Many interwar designers favored the decorative values in all treatments of wood because of its warmth, comfort, good-wearing, and therefore, economic qualities. For the creation of a successful modern interior the texture and surface of the wood becomes as important as the color itself. This aesthetics valuation was the result of the modernization of the wood industry. The development of veneer and plywood gave the possibility to create strong, flat, long and plain wooden surfaces which are capable of retaining their shape. Also the modernization of cutting machines resulted in high quality and diversity in texture of veneer. The flat and plain plywood surfaces were modern decorated with all kinds of veneer-sliced options. In addition, wood species from the former Belgian Colony Congo were imported. Limba (Terminalia superba), kambala (Chlorophora excelsa), mubala (Pentaclethra macrophylla) and sapelli (Entandrophragma cylindricum) were used in the interior of many Antwerp interwar suburban town houses. From the thirties onwards Belgian wood firms established modern manufactures in Congo. There the local wood was dried, cut and prepared for exportation to the harbor of Antwerp. The presence of all kinds of strong and decorative Congolese wood products supported its application in the interwar interior design. The Antwerp architects combined them in their designs for doors, floors, stairs, built-in-furniture, wall paneling and movable furniture.

Keywords: Antwerp, congo, furniture, interwar

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3980 Characterization of Organic Matter in Spodosol Amazonian by Fluorescence Spectroscopy

Authors: Amanda M. Tadini, Houssam Hajjoul, Gustavo Nicolodelli, Stéphane Mounier, Célia R. Montes, Débora M. B. P. Milori

Abstract:

Soil organic matter (SOM) plays an important role in maintaining soil productivity and accounting for the promotion of biological diversity. The main components of the SOM are the humic substances which can be fractionated according to its solubility in humic acid (HA), fulvic acids (FA) and humin (HU). The determination of the chemical properties of organic matter as well as its interaction with metallic species is an important tool for understanding the structure of the humic fractions. Fluorescence spectroscopy has been studied as a source of information about what is happening at the molecular level in these compounds. Specially, soils of Amazon region are an important ecosystem of the planet. The aim of this study is to understand the molecular and structural composition of HA samples from Spodosol of Amazonia using the fluorescence Emission-Excitation Matrix (EEM) and Time Resolved Fluorescence Spectroscopy (TRFS). The results showed that the samples of HA showed two fluorescent components; one has a more complex structure and the other one has a simpler structure, which was also seen in TRFS through the evaluation of each sample lifetime. Thus, studies of this nature become important because it aims to evaluate the molecular and structural characteristics of the humic fractions in the region that is considered as one of the most important regions in the world, the Amazon.

Keywords: Amazonian soil, characterization, fluorescence, humic acid, lifetime

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3979 Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy

Authors: Irsa Ejaz, Siyang He, Wei Li, Naiyue Hu, Chaochen Tang, Songbo Li, Meng Li, Boubacar Diallo, Guanghui Xie, Kang Yu

Abstract:

Background: Near-infrared spectroscopy (NIR) is a non-destructive, fast, and low-cost method to measure the grain quality of different cereals. Previously reported NIR model calibrations using the whole grain spectra had moderate accuracy. Improved predictions are achievable by using the spectra of whole grains, when compared with the use of spectra collected from the flour samples. However, the feasibility for determining the critical biochemicals, related to the classifications for food, feed, and fuel products are not adequately investigated. Objectives: To evaluate the feasibility of using NIRS and the influence of four sample types (whole grains, flours, hulled grain flours, and hull-less grain flours) on the prediction of chemical components to improve the grain sorting efficiency for human food, animal feed, and biofuel. Methods: NIR was applied in this study to determine the eight biochemicals in four types of sorghum samples: hulled grain flours, hull-less grain flours, whole grains, and grain flours. A total of 20 hybrids of sorghum grains were selected from the two locations in China. Followed by NIR spectral and wet-chemically measured biochemical data, partial least squares regression (PLSR) was used to construct the prediction models. Results: The results showed that sorghum grain morphology and sample format affected the prediction of biochemicals. Using NIR data of grain flours generally improved the prediction compared with the use of NIR data of whole grains. In addition, using the spectra of whole grains enabled comparable predictions, which are recommended when a non-destructive and rapid analysis is required. Compared with the hulled grain flours, hull-less grain flours allowed for improved predictions for tannin, cellulose, and hemicellulose using NIR data. Conclusion: The established PLSR models could enable food, feed, and fuel producers to efficiently evaluate a large number of samples by predicting the required biochemical components in sorghum grains without destruction.

Keywords: FT-NIR, sorghum grains, biochemical composition, food, feed, fuel, PLSR

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3978 Analytical Study of Data Mining Techniques for Software Quality Assurance

Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

Abstract:

Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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3977 Study of Natural Radioactive and Radiation Hazard Index of Soil from Sembrong Catchment Area, Johor, Malaysia

Authors: M. I. A. Adziz, J. Sharib Sarip, M. T. Ishak, D. N. A. Tugi

Abstract:

Radiation exposure to humans and the environment is caused by natural radioactive material sources. Given that exposure to people and communities can occur through several pathways, it is necessary to pay attention to the increase in naturally radioactive material, particularly in the soil. Continuous research and monitoring on the distribution and determination of these natural radionuclides' activity as a guide and reference are beneficial, especially in an accidental exposure. Surface soil/sediment samples from several locations identified around the Sembrong catchment area were taken for the study. After 30 days of secular equilibrium with their daughters, the activity concentrations of the naturally occurring radioactive material (NORM) members, i.e. ²²⁶Ra, ²²⁸Ra, ²³⁸U, ²³²Th, and ⁴⁰K, were measured using high purity germanium (HPGe) gamma spectrometer. The results obtained showed that the radioactivity concentration of ²³⁸U ranged between 17.13 - 30.13 Bq/kg, ²³²Th ranged between 22.90 - 40.05 Bq/kg, ²²⁶Ra ranged between 19.19 - 32.10 Bq/kg, ²²⁸Ra ranged between 21.08 - 39.11 Bq/kg and ⁴⁰K ranged between 9.22 - 51.07 Bq/kg with average values of 20.98 Bq/kg, 27.39 Bq/kg, 23.55 Bq/kg, 26.93 Bq/kg and 23.55 Bq/kg respectively. The values obtained from this study were low or equivalent to previously reported in previous studies. It was also found that the mean/mean values obtained for the four parameters of the Radiation Hazard Index, namely radium equivalent activity (Raeq), external dose rate (D), annual effective dose and external hazard index (Hₑₓ), were 65.40 Bq/kg, 29.33 nGy/h, 19.18 ¹⁰⁻⁶Sv and 0.19 respectively. These obtained values are low compared to the world average values and the values of globally applied standards. Comparison with previous studies (dry season) also found that the values for all four parameters were low and equivalent. This indicates the level of radiation hazard in the area around the study is safe for the public.

Keywords: catchment area, gamma spectrometry, naturally occurring radioactive material (NORM), soil

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3976 Cardiovascular Disease Prediction Using Machine Learning Approaches

Authors: P. Halder, A. Zaman

Abstract:

It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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3975 Prediction of Sepsis Illness from Patients Vital Signs Using Long Short-Term Memory Network and Dynamic Analysis

Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

Abstract:

The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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3974 Personalized Infectious Disease Risk Prediction System: A Knowledge Model

Authors: Retno A. Vinarti, Lucy M. Hederman

Abstract:

This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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3973 An Ecosystem Approach to Natural Resource Management: Case Study of the Topčiderska River, Serbia

Authors: Katarina Lazarević, Mirjana Todosijević, Tijana Vulević, Natalija Momirović, Ranka Erić

Abstract:

Due to increasing demand, climate change, and world population growth, natural resources are getting exploit fast. One of the most important natural resources is soil, which is susceptible to degradation. Erosion as one of the forms of land degradation is also one of the most global environmental problems. Ecosystem services are often defined as benefits that nature provides to humankind. Soil, as the foundation of basic ecosystem functions, provides benefits to people, erosion control, water infiltration, food, fuel, fibers… This research is using the ecosystem approach as a strategy for natural resources management for promoting sustainability and conservation. The research was done on the Topčiderska River basin (Belgrade, Serbia). The InVEST Sediment Delivery Ratio model was used, to quantify erosion intensity with a spatial distribution output map of overland sediment generation and delivery to the stream. InVEST SDR, a spatially explicit model, is using a method based on the concept of hydrological connectivity and (R) USLE model. This, combined with socio-economic and law and policy analysis, gives a full set of information to decision-makers helping them to successfully manage and deliver sustainable ecosystems.

Keywords: ecosystem services, InVEST model, soil erosion, sustainability

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3972 Different Data-Driven Bivariate Statistical Approaches to Landslide Susceptibility Mapping (Uzundere, Erzurum, Turkey)

Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

Abstract:

The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

Procedia PDF Downloads 134