Reliability-based analysis of settlements of shallow foundations on cohesionless soils

2020 ◽  
Author(s):  
◽  
Hashim Ghalib Al-Sumaiday

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Although settlements of foundations on cohesionless soils usually are small, it is important to be able to predict them because the primary issue in the design of shallow foundations on sand is the settlement requirement. Many methods for estimation of settlements in cohesionless soils have been published and evaluated. The majority of these methods rely on an empirical or semiempirical correlation with in-situ tests due to the difficulty and expense of obtaining undisturbed samples of cohesionless soils. The empiricism, in addition to the natural inherent soil variability, bring significant uncertainties into evaluation of design soil properties, and consequently to the settlement estimations. Traditional settlement analysis methodologies do not incorporate a consistent approach to account for the uncertainties and can lead to either costly design by overestimation of the settlement or a risky design by underestimation of the settlement. In contrast, a reliability-based methodology allows engineers to produce designs with a consistent level of safety that separately accounts for variability and uncertainty. The published works have extended the reliability-based methodology to settlement prediction. However, in the previous works, the uncertainties have been considered as one lumped factor and footing size has never been considered as an input variable. The research aims to extend the reliability-based methodology to settlement of shallow foundations on cohesionless soils considering the footing size and the main sources of uncertainties. It is hypothesized that incorporating the footing size in addition to the main sources of uncertainties in the reliability-based methodology will improve the reliability estimation of the settlement prediction; and consequently, improve the designs of shallow foundations on cohesionless soils. In the research described herein, six settlement prediction methods were evaluated using a database of 361 settlement case histories in terms of reliability, "the percentage of cases which the predicted settlement is equal or larger than measured settlement", and accuracy, "the ratio of the average of predicted settlement to the average of measured settlement". Sources of uncertainties associated with settlement prediction were investigated. The sources included inherent soil variability (from the natural formation of the soil), measurement uncertainty (from equipment, procedural, and random errors of the in-situ testing), transformation uncertainty (from empirical models to transform field or laboratory measurements into a design soil property), and the applied stress variability. Three probabilistic approaches were used to estimate: (i) probability of failure "the probability that the actual settlement exceeds a tolerable settlement", and (ii) settlement factors "multipliers are used in the design equations to target one of several acceptable probabilities of failure for serviceability limit state". The first approach was performed to estimate the probability of failure and the settlement factors probabilistically based on the total uncertainties of each settlement prediction method. The total uncertainties were characterized as one lumped factor by the statistics of the predicted to the measured settlements ratios. The second approach considered the main sources of uncertainty separately. A second-moment probabilistic technique was used to estimate the upper bound of the transformation uncertainty based on best- and worst- case scenarios of other uncertainty components. The estimated upper bound of the transformation uncertainty for each settlement prediction method was used in the probabilistic analysis herein. In the third approach, a framework was developed to estimate the realistic transformation uncertainty of each settlement prediction method to be used in the probabilistic analysis. A new approach to estimate the settlement is presented. The method has better accuracy and lower dispersion of the predicted to the measured settlement ratio than existing methods. The influence of soil type, size of footing, embedment depth, elevation of groundwater, and length to width ratio on both reliability and accuracy of the settlement prediction methods were examined. The width of the footing was found to be the most influential factor on the reliability and accuracy of the settlement prediction. The results support the hypothesis and show that the same amount of predicted settlement might indicate a different reliability according to the footing size. The results can be used to determine the reliability of settlement prediction in terms of probability of failure at different ranges of footing size, inherent soil variability and measurement uncertainty. The findings of this study can be used as a guide for geotechnical engineers to avoid over- or under- estimation of settlement. The results of the research described herein allow geotechnical engineers to achieve a better design of shallow foundations on cohesionless soils with a consistent level of safety that accounts for variability and uncertainty.

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fei Yu ◽  
Shichang Li ◽  
Zhangjun Dai ◽  
Jian Li ◽  
Shanxiong Chen

An improved method, which combines in situ measured settlement data, hyperbolic method, and deep lateral displacement rate, is presented in this study to predict the consolidation and stability of the ground, which can be used in conducting staged filling construction on soft subsoil. A case history of a highway embankment construction in a tidal flat with thick mucky clay is studied in Xia Pu, China. Preloading with the prefabricated vertical drain method is adopted to accelerate the consolidation of a subgrade. The field behavior of soft ground under filling load is observed through in situ monitoring sensors in four typical sections. The final ground settlement in each stage is determined using the field monitoring data based on the hyperbolic settlement prediction method. For each stage of graded filling load, the ground settlement with a strain consolidation degree of 95% is defined as the standard settlement, and the corresponding settlement time is set as the standard settlement time. The preloading period is estimated according to the standard settlement time. The deep lateral displacement rate of the ground is monitored to control the stability of the foundation and recommended to guide the embankment construction. Results indicate that the presented method can predict the preloading time of graded filling, reduce the frequency of observation, and ensure the consolidation and stability of the ground.


2010 ◽  
Vol 8 (2) ◽  
pp. 135-143
Author(s):  
Nebojsa Davidovic ◽  
Zoran Bonic ◽  
Verka Prolovic ◽  
Biljana Mladenovic ◽  
Dragoslav Stojic

The paper presents a brief description of experiment within the research project 'Theoretical and experimental analysis of interaction of shallow reinforced concrete foundations and soil for the purpose of improvement of national regulations and implementaation of Eurocode system' where in situ tests of a series of reinforced concrete foundation footing were performed, by loading until failure. As a rule, methods for calculation of shallow foundations settlement on granular soils overestimate the expected settlement, and underestimate soil bearing capacity, which results in a conservative foundation design. In order to test accuracy and reliability of the different settlements prediction methods, a comparative analysis of settlements calculated using these methods and those measured during experiment, was performed.


2005 ◽  
Vol 42 (1) ◽  
pp. 110-120 ◽  
Author(s):  
M A Shahin ◽  
M B Jaksa ◽  
H R Maier

Traditional methods of settlement prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted settlement. Artificial neural networks, like more traditional methods of settlement prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of settlement. In this paper, the likely distribution of predicted settlements, given the uncertainties associated with settlement prediction, is obtained by combining Monte Carlo simulation with a deterministic ANN model. A set of stochastic design charts, which incorporate the uncertainty associated with the ANN method, is developed. The charts are considered to be useful in the sense that they enable the designer to make informed decisions regarding the level of risk associated with predicted settlements and consequently provide a more realistic indication of what the actual settlement might be.Key words: settlement prediction, shallow foundations, neural networks, Monte Carlo, stochastic simulation.


2016 ◽  
Vol 56 (1) ◽  
pp. 144-151 ◽  
Author(s):  
Hirochika Hayashi ◽  
Satoshi Nishimoto ◽  
Takahiro Yamanashi

2020 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Firyal Baktir ◽  
Dwi Prijatmoko ◽  
Masniari Novita

There are several methods of analizing tooth size discrepancy in orthodontics include prediction methods for mixed dentition. Prediction method of Moyers and Sitepu most commonly used although both were obtained from 2 different races, Caucasian and Deutromelayu. Yemeni ethnic is one of the ethnic groups settled in Indonesia which descendants of the Caucasian race. The aim of the study was to observed the suitable prediction table for Yemeni ethnic. It was an observasional analitics study consist of 40 samples with cross sectional design. The results showed that slight difference for prediction of Moyers on the maxilla (1.02) and prediction of Sitepu on the mandibula (0.11). As conclusion, the most suitable predicition method for Yemeni ethnic is Moyers’s method for maxila and sitepu’s method for mandibula.   Key words: mesiodistal width permanen teeth, Moyers method, Sitepu method, Yemeni Etnic


2021 ◽  
Vol 16 ◽  
Author(s):  
Yayan Zhang ◽  
Guihua Duan ◽  
Cheng Yan ◽  
Haolun Yi ◽  
Fang-Xiang Wu ◽  
...  

Background: Increasing evidence has indicated that miRNA-disease association prediction plays a critical role in the study of clinical drugs. Researchers have proposed many computational models for miRNA-disease prediction. However, there is no unified platform to compare and analyze the pros and cons or share the code and data of these models. Objective: In this study, we develop an easy-to-use platform (MDAPlatform) to construct and assess miRNA-disease association prediction method. Methods: MDAPlatform integrates the relevant data of miRNA, disease and miRNA-disease associations that are used in previous miRNA-disease association prediction studies. Based on the componentized model, it develops differet components of previous computational methods. Results: Users can conduct cross validation experiments and compare their methods with other methods, and the visualized comparison results are also provided. Conclusion: Based on the componentized model, MDAPlatform provides easy-to-operate interfaces to construct the miRNA-disease association method, which is beneficial to develop new miRNA-disease association prediction methods in the future.


1993 ◽  
Vol 30 (04) ◽  
pp. 297-307
Author(s):  
John M. Almeter

There are literally dozens of different ways that the boat designer can predict the resistance of planing hulls. The term planing hull is used generically to describe the majority of hard chine boats being built today. No single prediction method is good for all types of planing hulls. Some methods can be relied on to give good predictions for certain boats and other methods can't be relied upon at all. This paper is meant as a reference for designers in selecting resistance prediction methods for planing hulls. It describes numerous resistance prediction methods and gives their variable ranges and the type of planing hulls they are based on or are intended for. Inherent problems or limitations of the methods are stated. The concept of hull shape, which is often neglected in resistance prediction, and its important role are discussed.


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