Uncertainty Characterization for Soil Cohesion in a Project Site in Nasiriyah Using Bayesian Methods

High uncertainties arias through the characterization of soil parameters because of the lack of data obtained from geotechnical reports. Reducing these uncertainties may improve the characteristic values of soil parameters. This research aims to probabilistically characterize a soil's cohesion parameter in Nasiriyah. The Bayesian approach has been applied to soil data obtained through a project in Nasiriyah. The soil at the site is classified as lean clay, and the soil cohesion has been evaluated using two Bayesian methods: the ordinary, normal distribution method (OND) and the Marcove Chain Monte Carlo-based Bayesian approach (MCMC) method. The previous knowledge utilized in the Bayesian approach was based on 20 boreholes, and the subjective probability approach has functioned in the prior probability distribution. The OND method deduced a mean value of cohesion of (195.9 kPa) and a standard deviation of (14.68 kPa), (COV) 7.49%. It was noted that the probability distribution has a more significant effect than the previous distribution on the posterior distribution. The MCMC method summarized the probabilistic description of the soil characteristic, through which it reached the mean and the subsequent standard deviation (167.49) kPa (109.8) kPa, respectively, and the coefficient of Variation (COV) was 65.6%. It is considered the most appropriate and common method, especially in high-dimensional data when the results are not well known because it can provide a probabilistic value for the not well-known data

Evaluation on the SPT Based Design Approach for Shallow Foundations

This research evaluated four SPT-based design equations used to estimate the carrying capacity of shallow footing. Using different methods, two plate load tests performed on silty clay and clay soil in Nasiriyah have been used to obtain the ultimate load-carrying capacity. Further, this study aims to utilize the finite element method based on Plaxis 3D foundation software to simulate the behavior of load settlement of the foundation with actual dimensions. It was concluded that the numerical analysis methods showed a good convergence to the actual test results, where the numerical results were 67 and 65 ton/m2 for two projects, respectively. The field values were 70 ton/m2 for projects with a number of possibilities in determining the failure areas of the soil to give a sufficient picture of the load expectations. The boundary of the influence zone obtained by the finite element method has functioned as an influence zone proposed for a new proposed equation which gave a good convergence with the measured bearing capacity values

Probabilistic-based analysis of a shallow square footing using Monte Carlo simulation

In this research, the bearing capacity (BC) of square shallow foundations at a depth of 0.5 m has been computed with different width ranged from 1 m to 4 m utilizing direct approach based on cone tip resistance (qc). The qcs have been measured through performing the Cone Penetration Tests (CPTs) on soil at four locations in the site of water intake project in Nasiriyah, Iraq as part of a soil site investigation program. The suggested applied vertical loads were 50–550 kPa. The qcs below the foundation level were considered uncertain and were simulated as random variables. Different probability distributions (PDs) were used to simulate the qcs values: normal, logistic, lognormal, Gamma, Weibull, Inverse Gaussian and Rayleigh distributions. Choosing the appropriate distribution was based on a goodness of fit (GOF) test. Monte Carlo simulation (MCS) was used to model the limit state function (LSF) based on a direct solution. It was found that the bearing capacity is sensitive to both the coefficient of variation of qcs and applied vertical load. It also was found that different PDs for soil parameters may result in the different PD of BC. Varying the width of shallow foundations changes the values of qcs and their probability distributions. According to this change, the reliability index of BC is slightly affected by the width of the foundation. The reliability index shows a good value at stress 100 kN/m2 higher than the target reliability and the reliability equal to zero at 550 kN/m2. A nonlinear behavior was observed between the probability of bearing capacity failure and the coefficient of variation of the applied vertical load. Keywords: Bearing capacity (BC), Reliability analysis, Monte Carlo simulation (MCS), Cone Penetration Test (CPT), Shallow foundatio

Uncertainty Characterization for Soil Cohesion in a Project Site in Nasiriyah Using Bayesian Methods

High uncertainties arias through the characterization of soil parameters because of the lack of data obtained from geotechnical reports. Reducing these uncertainties may improve the characteristic values of soil parameters. This research aims to probabilistically characterize a soil's cohesion parameter in Nasiriyah. The Bayesian approach has been applied to soil data obtained through a project in Nasiriyah. The soil at the site is classified as lean clay, and the soil cohesion has been evaluated using two Bayesian methods: the ordinary, normal distribution method (OND) and the Marcove Chain Monte Carlo-based Bayesian approach (MCMC) method. The previous knowledge utilized in the Bayesian approach was based on 20 boreholes, and the subjective probability approach has functioned in the prior probability distribution. The OND method deduced a mean value of cohesion of (195.9 kPa) and a standard deviation of (14.68 kPa), (COV) 7.49%. It was noted that the probability distribution has a more significant effect than the previous distribution on the posterior distribution. The MCMC method summarized the probabilistic description of the soil characteristic, through which it reached the mean and the subsequent standard deviation (167.49) kPa (109.8) kPa, respectively, and the coefficient of Variation (COV) was 65.6%. It is considered the most appropriate and common method, especially in high-dimensional data when the results are not well known because it can provide a probabilistic value for the not well-known data

Preface

Because of the travel restrictions between China and other countries of our keynote speaker, the 9th annual 2021 International Conference on Material Science and Environmental Engineering [MSEE2021] was held on November 27th, 2021 (Virtual Conference). The conference was held via Tencent Meeting Application. MSEE2021 aims to bring researchers, engineers, and students to the areas of Material Science and Environmental Engineering. MSEE2021 features unique mixed topics of Material Science and Advanced Materials, Material Engineering and Application, Environmental Science and Engineering and Mechanical Design and Technology. We received over 197 submissions from various parts of the world. The Technical Program Committee worked very hard to have all manuscripts reviewed before the review deadline. All the accepted papers have been submitted to strict peer-review, and selected based on originality, significance and clarity for the purpose of the conference. The conference program is extremely profound and featuring high-impact presentations of selected papers and additional late-breaking contributions. We sincerely hope that the conference would not only show the participants a broad overview of the latest research results on related fields, but also provide them with a significant platform for academic connection and exchange. There are two keynote speakers and four invited sessions. The keynote speakers are internationally recognized leading experts in their research fields, who have demonstrated outstanding proficiency and have achieved distinction in their profession. The proceedings would be published by IOP Journal of Physics Conference Series. We would like to express our sincere gratitude to all the members of Technical Program Committee and organizers for their enthusiasm, time, and expertise. Our deep thanks also go to many volunteers and staffs for the long hours and hard work they have generously given to MSEE2021. Last but not least, we would like to thank all the authors, speaker and participants for their great contributions to the success of MSEE2021. MSEE2021 Organizing Committee List of Committee of MSEE2021 are available in this pdf.</jats:p