Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand

This paper presents the application of artificial neural networks (ANN) and multivariable regression analysis (MRA) to predict the bearing capacity and the settlement of multi-edge skirted footings on sand. Respectively, these parameters are defined in terms of the bearing capacity ratio (BCR) of skirted to unskirted footing and the settlement reduction factor (SRF), the ratio of the difference in settlement of unskirted and skirted footing to the settlement of unskirted footing at a given pressure. The model equations for the prediction of the BCR and the SRF of the regular shaped footing were first developed using the available data collected from the literature. These equations were later modified to predict the BCR and the SRF of the multi-edge skirted footing, for which the data were generated by conducting a small scale laboratory test. The input parameters chosen to develop ANN models were the angle of internal friction (ϕ) and skirt depth (Ds) to the width of the footing (B) ratio for the prediction of the BCR; as for the SRF one additional input parameter was considered: normal stress (𝛔). The architecture for the developed ANN models was 2-2-1 and 3-2-1 for the BCR and the SRF, respectively. The R2 for the multi-edge skirted footings was in the range of 0,940-0,977 for the ANN model and 0,827-0,934 for the regression analysis. Similarly, the R2 for the SRF prediction might have been 0,913-0,985 for the ANN model and 0,739-0,932 for the regression analysis. It was revealed that the predicted BCR and SRF for the multi-edge skirted footings with the use of ANN is superior to MRA. Furthermore, the results of the sensitivity analysis indicate that both the BCR and the SRF of the multi-edge skirted footings are mostly affected by skirt depth, followed by the friction angle of the sand.

Download Full-text

Elastic and Frictional Properties of Fault Zones in Reservoir-Scale Hydro-Mechanical Models—A Sensitivity Study

Energies ◽

10.3390/en13184606 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4606

Author(s):

Torben Treffeisen ◽

Andreas Henk

Keyword(s):

Fault Zone ◽

Sensitivity Study ◽

Fault Zones ◽

Small Scale ◽

Fe Model ◽

Angle Of Internal Friction ◽

Fault Properties ◽

Mechanical Models ◽

The Difference ◽

A Minor

The proper representation of faults in coupled hydro-mechanical reservoir models is challenged, among others, by the difference between the small-scale heterogeneity of fault zones observed in nature and the large size of the calculation cells in numerical simulations. In the present study we use a generic finite element (FE) model with a volumetric fault zone description to examine what effect the corresponding upscaled material parameters have on pore pressures, stresses, and deformation within and surrounding the fault zone. Such a sensitivity study is important as the usually poor data base regarding specific hydro-mechanical fault properties as well as the upscaling process introduces uncertainties, whose impact on the modelling results is otherwise difficult to assess. Altogether, 87 scenarios with different elastic and plastic parameter combinations were studied. Numerical modelling results indicate that Young’s modulus and cohesion assigned to the fault zone have the strongest influence on the stress and strain perturbations, both in absolute numbers as well as regarding the spatial extent. Angle of internal friction has only a minor and Poisson’s ratio of the fault zone a negligible impact. Finally, some general recommendations concerning the choice of mechanical fault zone properties for reservoir-scale hydro-mechanical models are given.

Download Full-text

The settlement and bearing capacity of very large foundations on strong soils: 1996 R.M. Hardy keynote address

Canadian Geotechnical Journal ◽

10.1139/t97-070 ◽

1998 ◽

Vol 35 (1) ◽

pp. 131-145 ◽

Cited By ~ 31

Author(s):

Jack I Clark

Keyword(s):

Bearing Capacity ◽

Data Base ◽

Tall Buildings ◽

Friction Angle ◽

Geologic History ◽

Cohesionless Soils ◽

In Situ Tests ◽

Angle Of Internal Friction ◽

Undisturbed Samples ◽

Order Of Magnitude

Strong soils are not typically problem soils, and hence their behaviour has not been extensively studied. Strong soils are best defined on the basis of their geologic history, but for this paper they can be roughly defined as cohesive soils with an N value of about 15 or over and cohesionless soils with N values over 30. Settlement of tall buildings on strong soils has always been of interest. The means of estimating settlement of the large foundations or pile foundations associated with these structures varies but is generally understood to be predominantly elastic. Although predictions of settlement based on laboratory tests or in situ tests may vary as much as an order of magnitude, there now exists a reasonable data base which suggests that large buildings will settle similar amounts regardless of the size or bearing pressure of the foundations or, for that matter, the type of foundations. No data base exists for quantifying the maximum bearing pressure that will be tolerated by large foundations without failure. The angle of internal friction is known to be critical and to decrease with increasing pressure. It is difficult to measure the undisturbed strength of strong soils, since undisturbed samples are very difficult to secure. Centrifuge model tests of large foundations of different shapes confirm that the bearing capacity factor N gamma decreases with increased size of footing, but the decrease of N gamma may not be accounted for entirely by the friction angle change with pressure. Selection of a friction angle to determine the peak capacity of very large foundations must be done very carefully and with a great deal of judgement, since it cannot be accurately measured.Key words: settlement, bearing capacity, foundation behaviour.

Download Full-text

Scale Effects of Footings on Geocell Reinforced Sand Using Large-Scale Tests

Civil Engineering Journal ◽

10.28991/cej-0309110 ◽

2018 ◽

Vol 4 (3) ◽

pp. 497

Author(s):

A. Shadmand ◽

Mahmoud Ghazavi ◽

Navid Ganjian

Keyword(s):

Large Scale ◽

Initial Conditions ◽

Scale Effects ◽

Friction Angle ◽

Full Scale ◽

Small Scale ◽

Granular Soils ◽

Initial State ◽

The Difference ◽

Mean Stresses

The scale effect on bearing capacity of shallow footings supported by unreinforced granular soils has been evaluated extensively. However, the subject has not been addressed for shallow footings on geocell-reinforced granular soils. In this study, load-settlement characteristic of large square footings is investigated by performing large-scale loading tests on unreinforced and geocell-reinforced granular soils. The effects of footing width (B), soil relative density of soil (Dr), and reinforcement depth (u) have been investigated. The test results show that the scale effects exist in geocell-reinforced soils, like unreinforced soils, and the behavior of small-scale models of footings cannot be directly related to the behavior of full-scale footings due to the difference between initial conditions of tests and the initial state of mean stresses in the soil beneath the footings having different dimensions. Large footings create higher mean stresses in the soil, resulting in low soil friction angle and initial conditions of the test approach to the critical state lines. The results of tests indicate that model experiments should be conducted on low-density soil for better prediction of the behavior of full-scale footings, otherwise, the predicted behavior of full-scale footings does not seem conservative.

Download Full-text

Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

Applied Sciences ◽

10.3390/app9214594 ◽

2019 ◽

Vol 9 (21) ◽

pp. 4594 ◽

Cited By ~ 2

Author(s):

Hossein Moayedi ◽

Bahareh Kalantar ◽

Anastasios Dounis ◽

Dieu Tien Bui ◽

Loke Kok Foong

Keyword(s):

Bearing Capacity ◽

Prediction Models ◽

Soil Layer ◽

Ultimate Bearing Capacity ◽

Training Dataset ◽

Coefficient Of Determination ◽

Ann Model ◽

Fe Modelling ◽

Ann Models ◽

Circular Footing

In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils.

Download Full-text

Risk-reduction factor for bearing capacity of shallow foundations

Canadian Geotechnical Journal ◽

10.1139/t94-002 ◽

1994 ◽

Vol 31 (1) ◽

pp. 12-16 ◽

Cited By ~ 4

Author(s):

Adnan A. Basma

Keyword(s):

Bearing Capacity ◽

Risk Reduction ◽

Coefficient Of Variation ◽

Extreme Value Distribution ◽

Taylor Series Expansion ◽

Friction Angle ◽

Probability Of Failure ◽

Ultimate Bearing Capacity ◽

Reduction Factor ◽

Shallow Foundations

In this paper an ultimate bearing capacity risk-reduction factor is proposed to account for the variation and randomness in soil properties. Through a first-order Taylor series expansion, the mean and variance of the ultimate bearing capacity were assessed. Consequently, the variation of the ultimate bearing capacity is expressed as a function of the variation in cohesion and internal friction angle. To develop a risk-reduction factor, several probability density functions were utilized. The asymptotic type II extreme-value distribution for maxima was found best suited to represent the ultimate bearing capacity. The results indicate that the risk-reduction factor FR decreases with an increase in the coefficient of variation of ultimate bearing capacity and a decrease in the selected probability of failure pf. For pf = 0.0001, however, FR was found to range between 0.20 and 1.0. A numerical example is presented to illustrate the use of the proposed reduction factor. Key words : bearing capacity, coefficient of variation, probability distribution, probability of failure, risk factor, shallow foundations.

Download Full-text

The Analysis of The Process of Barley Grain Separation from Undesirable Particles

Inżynieria Mineralna ◽

10.29227/im-2019-01-33 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Jiří ZEGZULKA ◽

Lucie JEZERSKA ◽

HETCLOVA Vladimíra ◽

PROKES Rostislav ◽

RUTTKAY Vaclav

Keyword(s):

Friction Angle ◽

Barley Grain ◽

Separation Processes ◽

Material Particle ◽

Angle Of Internal Friction ◽

Powder Density ◽

First Results ◽

The Difference ◽

Barley Grains

The article focuses on the intensification of raw barley grains initial purification and separation processes before the subsequentprocessing in the area of brewing. Above all, it deals with the physical and mechanical concepts of the purification and separationof qualitatively satisfactory grains from undesirable impurities, e.g. coarse impurities, as the prevention from the potential damageof milling and scrapping facilities. Four different cultivated barley species were tested within the study. Physical and mechanicalparameters were determined in all samples, for instance powder density, angle of internal friction and external friction angle withsteel contact material, particle size distribution and morphology. The first results of measuring revealed the difference in the qualityof initial entering component of barley grains before the purification process compared to the output quality of grains after machinepurification and separation processes in the facilities determined for the subsequent grain storage. As a result of the non-effectiveprocess of separation, the final quality of the product, i.e. the beer, may be affected by the qualitative parameters of partial processesinvolved in treating barley grains.

Download Full-text

Experimental Research of Uniaxial Compression Bearing Capacity on Large Diameter CFST Columns

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.540.107 ◽

2013 ◽

Vol 540 ◽

pp. 107-118

Author(s):

Xiao Qiang Ren ◽

Yan Jiang Chen ◽

Wei Ming Yan ◽

Da Peng Gu ◽

Jin Jie Wang

Keyword(s):

Bearing Capacity ◽

Experimental Research ◽

Large Diameter ◽

Axial Loading ◽

Reduction Factor ◽

Steel Tubes ◽

Maximum Capacity ◽

Large Size ◽

The Difference ◽

Cfst Columns

This paper focused on the uniaxial ultimate-bearing-capacity of large size concrete filled steel tubular (CFST) columns. Two aspects were investigated experimentally. To verify the feasibility of similarity principles for large size components, a series of uniaxial compressive experiments were conducted using different scaled specimens, the prototype of which is a CFST arch rib in an authentic arch bridge. Meanwhile, two specimens with same scale were tested axially and eccentrically to investigate the difference of bearing capacity resulted from the pierced-column. The experimental results show that the size effect on the bearing capacity of the rib is so insignificant that the scaled specimen can be used to obtain the maximum capacity of the full-scale component. The inserted steel tubes in the middle of the column cause the reduction of its capacity and the reduction factor from axial loading is bigger than that from bias load. Moreover, the eccentricity reduces the effect of piercing on the capacity of CFST column.

Download Full-text

Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

Scientific Reports ◽

10.1038/s41598-021-81556-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhonghui Thong ◽

Jolena Ying Ying Tan ◽

Eileen Shuzhen Loo ◽

Yu Wei Phua ◽

Xavier Liang Shun Chan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Model ◽

Sensitivity Threshold ◽

Ann Model ◽

Blood Samples ◽

Ann Models ◽

Artificial Neural ◽

The Impact ◽

Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

Download Full-text

Changes in Children’s Body Composition and Posture during Puberty Growth

Children ◽

10.3390/children8040288 ◽

2021 ◽

Vol 8 (4) ◽

pp. 288

Author(s):

Wojciech Rusek ◽

Joanna Baran ◽

Justyna Leszczak ◽

Marzena Adamczyk ◽

Rafał Baran ◽

...

Keyword(s):

Body Composition ◽

Adipose Tissue ◽

Regression Analysis ◽

Body Mass ◽

Pelvic Obliquity ◽

The Body ◽

Body Posture ◽

Older Children ◽

The Difference ◽

The Right

The main goal of our study was to determine how the age of children, puberty and anthropometric parameters affect the formation of body composition and faulty body posture development in children. The secondary goal was to determine in which body segments abnormalities most often occur and how gender differentiates the occurrence of adverse changes in children’s body posture and body composition during puberty. The study group consisted of 464 schoolchildren aged from 6–16. Body posture was assessed with the Zebris system. The composition of the body mass was tested with Tanita MC 780 MA body mass analyzer and the body height was measured using a portable stadiometer PORTSTAND 210. The participants were further divided due to the age of puberty. Tanner division was adopted. The cut-off age for girls is ≥10 years and for boys it is ≥12 years. The analyses applied descriptive statistics, the Pearson correlation, stepwise regression analysis and the t-test. The accepted level of significance was p < 0.05. The pelvic obliquity was lower in older children (beta = −0.15). We also see that age played a significant role in the difference in the height of the right pelvis (beta = −0.28), and the difference in the height of the right shoulder (beta = 0.23). Regression analysis showed that the content of adipose tissue (FAT%) increased with body mass index (BMI) and decreased with increasing weight, age, and height. Moreover, the FAT% was lower in boys than in girls (beta negative equal to −0.39). It turned out that older children (puberty), had greater asymmetry in the right shoulder blade (p < 0.001) and right shoulder (p = 0.003). On the other hand, younger children (who were still before puberty) had greater anomalies in the left trunk inclination (p = 0.048) as well as in the pelvic obliquity (p = 0.008). Girls in puberty were characterized by greater asymmetry on the right side, including the shoulders (p = 0.001), the scapula (p = 0.001) and the pelvis (p < 0.001). In boys, the problem related only to the asymmetry of the shoulder blades (p < 0.001). Girls were characterized by a greater increase in adipose tissue and boys by muscle tissue. Significant differences also appeared in the body posture of the examined children. Greater asymmetry within scapulas and shoulders were seen in children during puberty. Therefore, a growing child should be closely monitored to protect them from the adverse consequences of poor posture or excessive accumulation of adipose tissue in the body.

Download Full-text

Application of Artificial Neural Networks for Predicting the Bearing Capacity of Shallow Foundations on Rock Masses

Rock Mechanics and Rock Engineering ◽

10.1007/s00603-021-02549-1 ◽

2021 ◽

Author(s):

M. A. Millán ◽

R. Galindo ◽

A. Alencar

Keyword(s):

Bearing Capacity ◽

Analytical Solutions ◽

Shear Failure ◽

Numerical Models ◽

Computational Effort ◽

Shallow Foundations ◽

Geological Strength Index ◽

Rock Masses ◽

Ann Model ◽

Artificial Neural

AbstractCalculation of the bearing capacity of shallow foundations on rock masses is usually addressed either using empirical equations, analytical solutions, or numerical models. While the empirical laws are limited to the particular conditions and local geology of the data and the application of analytical solutions is complex and limited by its simplified assumptions, numerical models offer a reliable solution for the task but require more computational effort. This research presents an artificial neural network (ANN) solution to predict the bearing capacity due to general shear failure more simply and straightforwardly, obtained from FLAC numerical calculations based on the Hoek and Brown criterion, reproducing more realistic configurations than those offered by empirical or analytical solutions. The inputs included in the proposed ANN are rock type, uniaxial compressive strength, geological strength index, foundation width, dilatancy, bidimensional or axisymmetric problem, the roughness of the foundation-rock contact, and consideration or not of the self-weight of the rock mass. The predictions from the ANN model are in very good agreement with the numerical results, proving that it can be successfully employed to provide a very accurate assessment of the bearing capacity in a simpler and more accessible way than the existing methods.

Download Full-text