This document contains formulas and equations related to geotechnical engineering and soil mechanics. It covers topics such as soil classification systems, phase relationships, stresses in soils, shallow foundations, soil consolidation, retaining structures, pile foundations, post-tensioned slabs, asphalt mix design, and concrete mix design. For each topic, relevant equations are presented along with brief explanations and examples of their usage. The document serves as a handy reference guide for engineers working in geotechnical analysis and design.
Index properties of soil provide information about engineering properties like permeability and shear strength without requiring expensive testing. For coarse-grained soils, key index properties are particle size distribution and relative density, while for fine-grained soils they are consistency and Atterberg limits. Particle size distribution is determined through sieve analysis for coarser particles and sedimentation analysis for finer particles. It is presented as a grading curve showing the percentage of particles finer than each size. Well-graded soils have a wide range of particle sizes while poorly-graded soils are mostly one size.
Presentation of soil in subject of engineering geology which have index properties of soil, engineering classification of soil, types of soil and more importantly definition of soil in engineering .
This document provides information on procedures for determining soil classification parameters through laboratory tests. It describes the liquid limit test, plastic limit test, and sieve analysis test. The liquid limit test determines the water content at which a soil behaves as a liquid. The plastic limit test finds the water content where a soil rod crumbles. Sieve analysis involves separating soil into grain sizes to determine classifications. The results of these tests are used to classify soils based on standards like the Unified Soil Classification System.
This document provides an overview of the Unified Soil Classification System (USCS) including definitions of key terms, laboratory and field classification procedures, and the parameters used in the system. It describes the tests used to classify soils based on particle size distribution, plasticity, and organic content including sieve analysis, Atterberg limits, and consistency states. Guidelines are given for plotting data on charts to determine soil classifications.
- Nepal has been working to systematically classify its soils since 1957, completing surveys of 55 districts by 1983, though some high hill districts remained unsurveyed for a long time.
- In 1998 and 2014, soil maps of Nepal were prepared using the USDA and WRB soil classification systems, respectively. Around 6000 soil profiles were studied from five physiographic regions.
- The data from 158 representative soil profiles were analyzed and converted to fit the HWSD format using formulas from Batjes et al. 2017 to standardize the data into layers from 0-30 cm and 30-100 cm.
- Major soils identified include Calcaric Fluvisols, Eutric Gleysols, Calcaric Ph
1. The document discusses two soil classification systems - the AASHTO system used by highway departments and the USCS system preferred by geotechnical engineers.
2. Both systems classify soils based on grain size distribution and Atterberg limits to group soils with similar engineering behaviors.
3. The USCS system divides soils into four main categories - coarse grained soils, fine grained soils, organic soils, and peat. Soils are identified by symbols indicating grain characteristics and plasticity.
Class notes of Geotechnical Engineering course I used to teach at UET Lahore. Feel free to download the slide show.
Anyone looking to modify these files and use them for their own teaching purposes can contact me directly to get hold of editable version.
Index properties of soil provide information about engineering properties like permeability and shear strength without requiring expensive testing. For coarse-grained soils, key index properties are particle size distribution and relative density, while for fine-grained soils they are consistency and Atterberg limits. Particle size distribution is determined through sieve analysis for coarser particles and sedimentation analysis for finer particles. It is presented as a grading curve showing the percentage of particles finer than each size. Well-graded soils have a wide range of particle sizes while poorly-graded soils are mostly one size.
Presentation of soil in subject of engineering geology which have index properties of soil, engineering classification of soil, types of soil and more importantly definition of soil in engineering .
This document provides information on procedures for determining soil classification parameters through laboratory tests. It describes the liquid limit test, plastic limit test, and sieve analysis test. The liquid limit test determines the water content at which a soil behaves as a liquid. The plastic limit test finds the water content where a soil rod crumbles. Sieve analysis involves separating soil into grain sizes to determine classifications. The results of these tests are used to classify soils based on standards like the Unified Soil Classification System.
This document provides an overview of the Unified Soil Classification System (USCS) including definitions of key terms, laboratory and field classification procedures, and the parameters used in the system. It describes the tests used to classify soils based on particle size distribution, plasticity, and organic content including sieve analysis, Atterberg limits, and consistency states. Guidelines are given for plotting data on charts to determine soil classifications.
- Nepal has been working to systematically classify its soils since 1957, completing surveys of 55 districts by 1983, though some high hill districts remained unsurveyed for a long time.
- In 1998 and 2014, soil maps of Nepal were prepared using the USDA and WRB soil classification systems, respectively. Around 6000 soil profiles were studied from five physiographic regions.
- The data from 158 representative soil profiles were analyzed and converted to fit the HWSD format using formulas from Batjes et al. 2017 to standardize the data into layers from 0-30 cm and 30-100 cm.
- Major soils identified include Calcaric Fluvisols, Eutric Gleysols, Calcaric Ph
1. The document discusses two soil classification systems - the AASHTO system used by highway departments and the USCS system preferred by geotechnical engineers.
2. Both systems classify soils based on grain size distribution and Atterberg limits to group soils with similar engineering behaviors.
3. The USCS system divides soils into four main categories - coarse grained soils, fine grained soils, organic soils, and peat. Soils are identified by symbols indicating grain characteristics and plasticity.
Class notes of Geotechnical Engineering course I used to teach at UET Lahore. Feel free to download the slide show.
Anyone looking to modify these files and use them for their own teaching purposes can contact me directly to get hold of editable version.
1. Soils are particulate materials that form from sedimentary or residual processes and contain a range of particle sizes from large particles like quartz down to very small clay particles.
2. Simple soil classification systems are needed for preliminary engineering design to determine properties like strength and stiffness from cheap, simple tests using core samples.
3. The most common classification systems are based on particle size using percentages of sand, silt, and clay, Atterberg limits for fine-grained soils, and systems like the Unified Soil Classification System which assigns a two-letter symbol based on grain size and plasticity.
This document provides an overview and summary of key concepts from a PE refresher course on geotechnical engineering. It covers soil classification methods including the USCS and AASHTO systems. It also discusses important soil properties like grain size, plasticity, compaction, permeability, consolidation, and shear strength. Applications covered include settlement analysis, slope stability, shallow and deep foundations, and retaining structures. Calculation of stresses, settlements, and determining appropriate soil parameters for analysis are also summarized.
This document presents a seminar by Anand Singh on soil classification based on the Indian Standard Classification System. It discusses the various soil classification systems and focuses on defining the ISCS. The ISCS classifies soils into four major divisions - coarse grained, fine grained, organic, and peat. It then explains how to classify soils under each division based on factors like grain size, plasticity, liquid limit, and location on a plasticity chart. Examples are provided to demonstrate how to determine the classification symbol of a given soil sample based on test results.
Determination grain size distribution of soilSumanHaldar8
This document describes procedures for determining the grain size distribution of soils through sieve analysis and sedimentation tests. It explains that soils can be classified as coarse-grained if particles are larger than 75 micrometers, and fine-grained if smaller. Sieve analysis involves shaking soils through a series of sieves to separate grains by size, while sedimentation tests use pipette or hydrometer methods for fine soils. The results characterize the soil type, gradation, and engineering properties.
Stabilization of black cotton soil by using plastic rfAnurupJena1
This document presents the results of various laboratory tests conducted on black cotton soil collected from Balugaon, Chilika in Odisha, India to characterize its engineering properties. The tests included liquid limit, plastic limit, specific gravity, standard proctor, CBR, and unconfined compression tests. The liquid limit of the soil was found to be 64.63%, plastic limit 46.67%, and specific gravity 2.73. Optimum moisture content from the standard proctor test was 27.6% and maximum dry density was 1.49 g/cm3. CBR values at 2.5mm and 5mm penetrations were 2.678832 and 2.134793 respectively. Unconf
The document describes soil classification systems. It discusses how soils can be classified based on particle size distribution into groups like gravel, sand, silt, and clay. The USDA soil textural classification system uses a ternary diagram to classify soils based on their relative percentages of sand, silt, and clay. Soils can also be classified according to engineering purposes, considering properties like plasticity in addition to particle size. Systems like the Unified Soil Classification System (USCS) are more suitable for geotechnical engineering applications.
This document discusses methods for classifying soils based on particle size analysis. It describes separating soils into gravel, sand, silt and clay fractions based on particle diameter size ranges. It presents equations for calculating uniformity coefficient (Cu) and curvature coefficient (Cc) to characterize soil gradation. It also summarizes the process of hydrometer analysis for determining soil particle size distribution and provides the Stokes' law equation for calculating particle settling velocity in suspension. Key criteria are outlined for classifying gravels and sands as well as fine-grained soils based on liquid limit, plasticity index and other properties in accordance with standardized soil classification systems.
This document provides information on procedures to determine properties of aggregates through various laboratory tests. It describes tests to determine the particle size distribution of fine and coarse aggregates through sieve analysis. It also describes tests to determine the bulk density, void ratio, porosity and specific gravity of aggregates in loose and compacted states. Additionally, it provides the procedure to determine the bulking characteristics of sand and how bulking increases with moisture content up to a maximum point. The document contains sections on aim, apparatus, procedure, observations and calculations and results for each test.
Solution to first semester soil 2015 16chener Qadr
This document contains a soil mechanics exam with four questions. Question 1 involves determining properties like void ratio, dry density, and bulk density of a saturated soil sample. It also asks about changes if saturation is reduced and calculating soil requirements for an embankment. Question 2 asks to classify a soil sample using soil classification charts and determine its suitability as backfill. Question 3 involves calculating seepage flow from a canal into a ditch based on soil properties. Question 4, which is not fully summarized, involves analyzing seepage in an embankment dam.
This document provides information on procedures to determine various properties of aggregates through laboratory experiments. It describes 12 experiments related to grain size distribution, bulk density, voids ratio, porosity, specific gravity, bulking, crushing value, impact value, and compressive strength of aggregates and cement. The summary focuses on Experiment 1 which involves determining the particle size distribution of fine and coarse aggregates through sieve analysis.
This document provides a summary of the review and analysis conducted for the Blakely Mountain Dam located in Arkansas. Key points include:
1) The dam is 1200 feet long and 230 feet high and was constructed between 1950-1952 for flood control and hydropower.
2) Analyses included seepage flow using flow nets and PLAXIS, slope stability using circular arc and PLAXIS methods, and settlement analysis using 1D and parabolic equations.
3) The dam was found to be stable under steady state seepage, rapid drawdown, and after construction conditions based on factor of safety calculations.
4) A test fill was conducted to determine suitable compaction methods
This document summarizes soil characteristics of different pedons from research stations in Maharashtra, India. It includes physical and chemical properties as well as morphological characteristics. The soils are classified as Typic Haplusterts, Typic Haplustepts, and Typic Ustorthents. Land use recommendations are provided based on soil type and slope to promote sustainable agriculture through suitable cropping systems and agroforestry.
1) The document discusses methods for classifying soils through sieve analysis, liquid limit tests, and plastic limit tests. Sieve analysis is used to determine the grain size distribution of coarser soil particles, while hydrometer testing identifies finer particles.
2) The tests are used to classify soils based on properties like plasticity index and grain size distribution curve. This allows soils to be designated under specific categories in the Unified Soil Classification System.
3) Key measurements identified include D10, D30, D60 grain sizes, Cu and Cc values for grading, and liquid limit and plastic limit water contents for defining soil types.
The document summarizes various methods used to analyze soil properties for highway construction projects. It describes procedures for sieve analysis, liquid limit testing, plastic limit testing, and other methods to determine characteristics like density, bearing capacity, and moisture content that are used in designing roadway foundations and pavements. Preliminary soil surveys are also outlined to identify soil types and conditions along proposed routes to inform design and construction decisions.
Particle Size Distribution & Classification of Soilwasim shaikh
The document discusses particle size distribution and classification of soils. It describes how particle size distribution is determined through sieve analysis and sedimentation analysis. It provides the classification criteria for different particle sizes according to the Indian standard. Stokes' law, which describes the terminal settling velocity of particles in fluid, is also explained. Some examples of calculating particle settling times using Stokes' law are given. Limitations of Stokes' law are noted. Classification of fine-grained soils using the plasticity chart is briefly covered.
1) Soil classification systems group soils with similar physical properties into units in a systematic way. The Unified Soil Classification System (USCS) and American Association of State Highway and Transportation Officials (AASHTO) systems are commonly used.
2) The USCS and AASHTO systems classify soils based on grain size and plasticity. Soils are categorized as coarse-grained or fine-grained. Parameters like the D10, D30, and D60 values are determined from grain size distribution curves to characterize soils.
3) Under the USCS, soils are given a two-letter symbol indicating major material and gradation/plasticity. For example, well-graded gravel is
This document provides procedures for determining various properties of aggregates through laboratory experiments. It describes 15 experiments related to aggregate testing, including procedures to determine grain size distribution, bulk density, crushing value, impact value, and others. The grain size distribution experiment involves sieving samples of fine and coarse aggregates and calculating parameters like effective size and uniformity coefficient. The crushing value and impact value experiments involve compressing aggregate samples and measuring the amount of particles that break off to determine the aggregates' resistance to impact and crushing forces.
MATLAB Modeling of SPT and Grain Size Data in Producing Soil ProfilePsychōjit MØmz
The study was carried out to find out a suitable numerical procedure for establishing a graphical presentation of the soil profile of a site using SPT values and grain size analysis data. MATLAB numerical tool was used for this purpose and the soil properties was estimated using established empirical correlations. A computer Software was developed where SPT values at borehole locations, percent of grain sizes, water table and GPS coordinates of the site were used as inputs, Rectangular grids in 2-D or 3-D space were created for interpolation or extrapolation of the gridded data in ‘meshgrid’ format. The output yielded intermittent SPT profile and the contour plot matrix for subsoil soil condition of a site. The output soil-profile is presented by a 3-D shaded surface plot that would be useful for preliminary selection of a project site, land use planning, zoning ordinances, pre-disaster planning, capital investment planning,
Fifteen borehole data of SPT values and grain sizes along a 20 km stretch of ongoing Janjira approach road project of Padma multipurpose bridge in Madaripur district were used to verify the usability of the developed Software. Disturbed soil sample were collected up to depths of 19.5m depth in every 1.5m interval to perform grain size analysis test. Excel spreadsheet was used where more than 500 data including SPT-N values, percent sand and fines at depths, GPS coordinated, reduce level and ground water table. The soils at the site were predominantly alluvial deposits. All these data were used in MATLAB interactive environment for numerical computation, visualization, and programming. The purposes of the study were to find SPT contour profile and soil-profile of a particular alignment of the site and to extract borehole Log form SPT profile and soil-profile of a specific location of the alignment.
Outcome of this study can be used in microzonation studies, site response analysis, calculation of bearing capacity of subsoils in the region and producing a number of parameters which are empirically related to SPT values.
This document provides an overview of soil classification systems, focusing on the Unified Soil Classification System (USCS) and the American Association of State Highway and Transportation Officials (AASHTO) system. It defines key aspects of each system such as grouping soils by grain size and plasticity. Examples are provided to demonstrate how to classify soils using index properties and test results based on the criteria of each system.
This document discusses soil classification systems. It provides information on classifying soils based on their grain size, plasticity properties, and engineering behavior. The key points are:
- Soils are classified into groups like gravel, sand, silt, and clay based on particle size using systems like the Indian Standard Classification System. Additional criteria describe grading.
- The plasticity of fine-grained soils is assessed using limits like liquid limit and plastic limit to classify them as low, intermediate, or high plasticity.
- Classification helps describe and group soils based on meaningful engineering properties that influence permeability, compressibility, and shear strength for foundation and construction purposes.
1. Soils are particulate materials that form from sedimentary or residual processes and contain a range of particle sizes from large particles like quartz down to very small clay particles.
2. Simple soil classification systems are needed for preliminary engineering design to determine properties like strength and stiffness from cheap, simple tests using core samples.
3. The most common classification systems are based on particle size using percentages of sand, silt, and clay, Atterberg limits for fine-grained soils, and systems like the Unified Soil Classification System which assigns a two-letter symbol based on grain size and plasticity.
This document provides an overview and summary of key concepts from a PE refresher course on geotechnical engineering. It covers soil classification methods including the USCS and AASHTO systems. It also discusses important soil properties like grain size, plasticity, compaction, permeability, consolidation, and shear strength. Applications covered include settlement analysis, slope stability, shallow and deep foundations, and retaining structures. Calculation of stresses, settlements, and determining appropriate soil parameters for analysis are also summarized.
This document presents a seminar by Anand Singh on soil classification based on the Indian Standard Classification System. It discusses the various soil classification systems and focuses on defining the ISCS. The ISCS classifies soils into four major divisions - coarse grained, fine grained, organic, and peat. It then explains how to classify soils under each division based on factors like grain size, plasticity, liquid limit, and location on a plasticity chart. Examples are provided to demonstrate how to determine the classification symbol of a given soil sample based on test results.
Determination grain size distribution of soilSumanHaldar8
This document describes procedures for determining the grain size distribution of soils through sieve analysis and sedimentation tests. It explains that soils can be classified as coarse-grained if particles are larger than 75 micrometers, and fine-grained if smaller. Sieve analysis involves shaking soils through a series of sieves to separate grains by size, while sedimentation tests use pipette or hydrometer methods for fine soils. The results characterize the soil type, gradation, and engineering properties.
Stabilization of black cotton soil by using plastic rfAnurupJena1
This document presents the results of various laboratory tests conducted on black cotton soil collected from Balugaon, Chilika in Odisha, India to characterize its engineering properties. The tests included liquid limit, plastic limit, specific gravity, standard proctor, CBR, and unconfined compression tests. The liquid limit of the soil was found to be 64.63%, plastic limit 46.67%, and specific gravity 2.73. Optimum moisture content from the standard proctor test was 27.6% and maximum dry density was 1.49 g/cm3. CBR values at 2.5mm and 5mm penetrations were 2.678832 and 2.134793 respectively. Unconf
The document describes soil classification systems. It discusses how soils can be classified based on particle size distribution into groups like gravel, sand, silt, and clay. The USDA soil textural classification system uses a ternary diagram to classify soils based on their relative percentages of sand, silt, and clay. Soils can also be classified according to engineering purposes, considering properties like plasticity in addition to particle size. Systems like the Unified Soil Classification System (USCS) are more suitable for geotechnical engineering applications.
This document discusses methods for classifying soils based on particle size analysis. It describes separating soils into gravel, sand, silt and clay fractions based on particle diameter size ranges. It presents equations for calculating uniformity coefficient (Cu) and curvature coefficient (Cc) to characterize soil gradation. It also summarizes the process of hydrometer analysis for determining soil particle size distribution and provides the Stokes' law equation for calculating particle settling velocity in suspension. Key criteria are outlined for classifying gravels and sands as well as fine-grained soils based on liquid limit, plasticity index and other properties in accordance with standardized soil classification systems.
This document provides information on procedures to determine properties of aggregates through various laboratory tests. It describes tests to determine the particle size distribution of fine and coarse aggregates through sieve analysis. It also describes tests to determine the bulk density, void ratio, porosity and specific gravity of aggregates in loose and compacted states. Additionally, it provides the procedure to determine the bulking characteristics of sand and how bulking increases with moisture content up to a maximum point. The document contains sections on aim, apparatus, procedure, observations and calculations and results for each test.
Solution to first semester soil 2015 16chener Qadr
This document contains a soil mechanics exam with four questions. Question 1 involves determining properties like void ratio, dry density, and bulk density of a saturated soil sample. It also asks about changes if saturation is reduced and calculating soil requirements for an embankment. Question 2 asks to classify a soil sample using soil classification charts and determine its suitability as backfill. Question 3 involves calculating seepage flow from a canal into a ditch based on soil properties. Question 4, which is not fully summarized, involves analyzing seepage in an embankment dam.
This document provides information on procedures to determine various properties of aggregates through laboratory experiments. It describes 12 experiments related to grain size distribution, bulk density, voids ratio, porosity, specific gravity, bulking, crushing value, impact value, and compressive strength of aggregates and cement. The summary focuses on Experiment 1 which involves determining the particle size distribution of fine and coarse aggregates through sieve analysis.
This document provides a summary of the review and analysis conducted for the Blakely Mountain Dam located in Arkansas. Key points include:
1) The dam is 1200 feet long and 230 feet high and was constructed between 1950-1952 for flood control and hydropower.
2) Analyses included seepage flow using flow nets and PLAXIS, slope stability using circular arc and PLAXIS methods, and settlement analysis using 1D and parabolic equations.
3) The dam was found to be stable under steady state seepage, rapid drawdown, and after construction conditions based on factor of safety calculations.
4) A test fill was conducted to determine suitable compaction methods
This document summarizes soil characteristics of different pedons from research stations in Maharashtra, India. It includes physical and chemical properties as well as morphological characteristics. The soils are classified as Typic Haplusterts, Typic Haplustepts, and Typic Ustorthents. Land use recommendations are provided based on soil type and slope to promote sustainable agriculture through suitable cropping systems and agroforestry.
1) The document discusses methods for classifying soils through sieve analysis, liquid limit tests, and plastic limit tests. Sieve analysis is used to determine the grain size distribution of coarser soil particles, while hydrometer testing identifies finer particles.
2) The tests are used to classify soils based on properties like plasticity index and grain size distribution curve. This allows soils to be designated under specific categories in the Unified Soil Classification System.
3) Key measurements identified include D10, D30, D60 grain sizes, Cu and Cc values for grading, and liquid limit and plastic limit water contents for defining soil types.
The document summarizes various methods used to analyze soil properties for highway construction projects. It describes procedures for sieve analysis, liquid limit testing, plastic limit testing, and other methods to determine characteristics like density, bearing capacity, and moisture content that are used in designing roadway foundations and pavements. Preliminary soil surveys are also outlined to identify soil types and conditions along proposed routes to inform design and construction decisions.
Particle Size Distribution & Classification of Soilwasim shaikh
The document discusses particle size distribution and classification of soils. It describes how particle size distribution is determined through sieve analysis and sedimentation analysis. It provides the classification criteria for different particle sizes according to the Indian standard. Stokes' law, which describes the terminal settling velocity of particles in fluid, is also explained. Some examples of calculating particle settling times using Stokes' law are given. Limitations of Stokes' law are noted. Classification of fine-grained soils using the plasticity chart is briefly covered.
1) Soil classification systems group soils with similar physical properties into units in a systematic way. The Unified Soil Classification System (USCS) and American Association of State Highway and Transportation Officials (AASHTO) systems are commonly used.
2) The USCS and AASHTO systems classify soils based on grain size and plasticity. Soils are categorized as coarse-grained or fine-grained. Parameters like the D10, D30, and D60 values are determined from grain size distribution curves to characterize soils.
3) Under the USCS, soils are given a two-letter symbol indicating major material and gradation/plasticity. For example, well-graded gravel is
This document provides procedures for determining various properties of aggregates through laboratory experiments. It describes 15 experiments related to aggregate testing, including procedures to determine grain size distribution, bulk density, crushing value, impact value, and others. The grain size distribution experiment involves sieving samples of fine and coarse aggregates and calculating parameters like effective size and uniformity coefficient. The crushing value and impact value experiments involve compressing aggregate samples and measuring the amount of particles that break off to determine the aggregates' resistance to impact and crushing forces.
MATLAB Modeling of SPT and Grain Size Data in Producing Soil ProfilePsychōjit MØmz
The study was carried out to find out a suitable numerical procedure for establishing a graphical presentation of the soil profile of a site using SPT values and grain size analysis data. MATLAB numerical tool was used for this purpose and the soil properties was estimated using established empirical correlations. A computer Software was developed where SPT values at borehole locations, percent of grain sizes, water table and GPS coordinates of the site were used as inputs, Rectangular grids in 2-D or 3-D space were created for interpolation or extrapolation of the gridded data in ‘meshgrid’ format. The output yielded intermittent SPT profile and the contour plot matrix for subsoil soil condition of a site. The output soil-profile is presented by a 3-D shaded surface plot that would be useful for preliminary selection of a project site, land use planning, zoning ordinances, pre-disaster planning, capital investment planning,
Fifteen borehole data of SPT values and grain sizes along a 20 km stretch of ongoing Janjira approach road project of Padma multipurpose bridge in Madaripur district were used to verify the usability of the developed Software. Disturbed soil sample were collected up to depths of 19.5m depth in every 1.5m interval to perform grain size analysis test. Excel spreadsheet was used where more than 500 data including SPT-N values, percent sand and fines at depths, GPS coordinated, reduce level and ground water table. The soils at the site were predominantly alluvial deposits. All these data were used in MATLAB interactive environment for numerical computation, visualization, and programming. The purposes of the study were to find SPT contour profile and soil-profile of a particular alignment of the site and to extract borehole Log form SPT profile and soil-profile of a specific location of the alignment.
Outcome of this study can be used in microzonation studies, site response analysis, calculation of bearing capacity of subsoils in the region and producing a number of parameters which are empirically related to SPT values.
This document provides an overview of soil classification systems, focusing on the Unified Soil Classification System (USCS) and the American Association of State Highway and Transportation Officials (AASHTO) system. It defines key aspects of each system such as grouping soils by grain size and plasticity. Examples are provided to demonstrate how to classify soils using index properties and test results based on the criteria of each system.
This document discusses soil classification systems. It provides information on classifying soils based on their grain size, plasticity properties, and engineering behavior. The key points are:
- Soils are classified into groups like gravel, sand, silt, and clay based on particle size using systems like the Indian Standard Classification System. Additional criteria describe grading.
- The plasticity of fine-grained soils is assessed using limits like liquid limit and plastic limit to classify them as low, intermediate, or high plasticity.
- Classification helps describe and group soils based on meaningful engineering properties that influence permeability, compressibility, and shear strength for foundation and construction purposes.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
2. TABLE OF CONTENTS
Page
1. SOIL CLASSIFICATION ...............................................................................3
1.1 USCS: Unified Soil Classification System..........................................3
1.1.1 Relative Density of Cohesionless Soils: .....................................4
1.1.2 Fine Grained(Cohesive) Soil Charts using the USCS System:..4
1.1.3 Consistency of Fine Grained Soils: .............................................5
1.2 USDA Soil Classification System........................................................5
1.3 AASHTO Soil Classification System:..................................................6
2. PHASE RELATIONSHIP EQUATIONS: .......................................................7
2.1 Shear Strength of Soils........................................................................7
2.2 Bearing Capacity of Soils ....................................................................7
3. STRESSES IN SOILS ...................................................................................9
3.1 Various Loading Conditions:...............................................................9
..........................................................................................................................9
..........................................................................................................................9
4. SHALLOW FOUNDATIONS .......................................................................10
4.1 Conventional Footings.......................................................................10
4.11Geotechnical Analysis........................................................................10
4.12 Structural Design:..............................................................................10
4.2 Strap or Cantilever Footings:.................................................................11
4.3 Trapezoidal Footings: .............................................................................12
5. SOIL CONSOLIDATION EQUATIONS .......................................................14
5.1 Instant Settlement of footings:..........................................................14
5.2 Primary Consolidation: ......................................................................14
5.3 Overconsolidated Soils ..........................................................................14
5.4 Time rate of settlement ..........................................................................15
5.41 Coefficient of consolidation..............................................................15
6. RETAINING STRUCTURES: ......................................................................16
6.1 Horizontal Stresses: Active, At Rest and Passive................................16
6.2 Basement Wall with surcharge: .............................................................17
6.3 Braced Excavations:...............................................................................17
6.4 Forces on Struts:.....................................................................................18
6.5 Cantilever Sheetpiles in Sand ................................................................20
6.6 Cantilever Sheetpiles in Clay .................................................................21
6.6 Anchored Sheetpiles in Sand (Also called Bulkheads) .......................22
6.7 Anchored Sheetpiles in Clay (Also called Bulkheads).........................24
7. PILE FOUNDATIONS .................................................................................25
8. Post Tensioned Slabs: ..............................................................................28
9. Asphalt Mix Design: ..................................................................................30
10. Concrete Mix Design:.............................................................................33
3. 1. SOIL CLASSIFICATION
1.1 USCS: Unified Soil Classification System
Coarse Grained soils have less than 50% passing the # 200 sieve:
Symbol Passing
the #200
Cu=
30
60
D
D Cc =
6010
2
30
)(
DD
D
×
Soil Description
GW < 5% 4 or higher 1 to 3 Well graded gravel
GP < 5% Less than 4 1 to 3 Poorly graded gravel
GW-GM 5 to12% 4 or higher 1 to 3 but with
<15% sand
Well graded gravel with
silt
GW-GM 5 to12% 4 or higher 1 to 3 but with
≥15% sand
Well graded gravel with
silt and sand
GW-GC 5 to12% 4 or higher 1 to 3 but with
<15% sand
Well graded gravel with
clay or silty clay
GW-GC 5 to12% 4 or higher 1 to 3 but with
≥15% sand
Well graded gravel with
clay and sand
GC >12% N/A N/A,<15%sand Clayey Gravel
GC > 12% N/A N/A,>15%sand Clayey Gravel with sand
GM-GC >12% N/A N/A,<15%sand Clayey Silt with gravel
GM-GC >12% N/A N/A,≥15%sand Clayey Silt with sand
SW < 5% 6 or higher 1 to 3 Well graded sand
SP < 5% Less than 6 1 to 3 Poorly graded sand
SM >12% N/A N/A Silty Sand or Sandy Silt
SC >12% N/A N/A Clayey Sand or Sandy
Clay
SC-SM >12% N/A N/A Silty Clay with Sand
Where:
Cu = Uniformity Coefficient; gives the range of grain sizes in a given sample. Higher Cu means well graded.
Cz = Coefficient of Curvature is a measure of the smoothness of the gradation curve. Usually less than 3.
D10, D3, & D60 are the grain size diameter corresponding to 10%, 30% and 60% passing screen.
4. 1.1.1 Relative Density of Cohesionless Soils:
SPT or N value Relative Density % Relative Density
0 – 3 Very loose 0 – 15
4 – 10 Loose 15 – 35
11 – 30 Medium dense 35 – 65
31 – 50 Dense 65 -85
> 50 Very dense 85 - 100
1.1.2 Fine Grained(Cohesive) Soil Charts using the USCS System:
5. 1.1.3 Consistency of Fine Grained Soils:
SPT or N value Cohesion, C or Su Consistency
< 2 < 500 psf Very soft
2 – 4 500 – 1000 psf Soft
5 – 8 1000 – 2000 psf Firm
9 – 15 2000 – 4000 psf Stiff
16-30 4000 – 8000 psf Very stiff
>30 > 8000 psf Hard
1.2 USDA Soil Classification System
The percent SAND,SILT,and CLAY lines are drawn and their intersection gives the
soil classification.
7. 2. PHASE RELATIONSHIP EQUATIONS:
Dry Unit
Weight, γd
Bulk or Wet or Total
Unit Weight, γm or
γw or γt or γ
Saturated Unit
Weight, γs or γsat
2.1 Shear Strength of Soils
2.2 Bearing Capacity of Soils
Hansen B.C. Factors:
9. Note:If Df/B > 1, terzaghi’s B.C. factors do not apply. Use Hansen’s B.C. factors.
For example, if depth of footing (Df) is 3 ft but footing width (B) is 2.75 ft.
3. STRESSES IN SOILS
3.1 Various Loading Conditions:
Strip
10. 4. SHALLOW FOUNDATIONS
4.1 Conventional Footings
4.11Geotechnical Analysis
qall = Q / Bx1 for Continuous Footings
qall = Q / BxL for Rectangular Footings
qall = Q / BxB for Square Footings
qall < qu / 3 from Bearing Capacity Calculations
e < B/6, where e=eccentricity
Df > 1.0 ft minimum
Df > frost depth
Df > setback distance for footings on slope
Df > scour depth
Df > high moisture variations depth(expansive soils)
4.12 Structural Design:
Given: A Continuous footing with γm = 100 pcf, Df = 5 ft, qall = 4,000 psf, D.L=22 k/ft,
L.L.=12 k/ft, f’c=3 ksi, fy= 60 ksi. Design the footings using the ACI code:
Layer 1
Layer 2
Layer 3
3B
2B
1B
B
Df
q all
GWT
2
1 Cc___
1+Eo
C=
Q
11. 4.2 Strap or Cantilever Footings:
Strap Footing with varying beam thickness
Strap Footings with constant beam thickness
29. Center Lift:
The Structural Engineer also needs Kv (given in immediate settlement section), effective PI(pp
138 of Geotechnical DVD book) and other climatic constants that are from building codes(given).