Application of Machine Learning Technique in Predicting the Bearing Capacity of Rectangular Footing on Layered Sand under Inclined Loading

The aim of the present study is to apply machine learning technique to predict the ultimate bearing capacity of the rectangular footing on layered sand under inclined loading. For this purpose, a total 5400 data based on the finite element method for the rectangular footing on layered sand under inclined loading were collected from the literature to develop the machine learning model. The input variables chosen were the thickness ratio (0.00 to 2.00) of the upper dense sand layer, embedment ratio (0 to 2), the friction angle of upper dense (410 to 460) sand and lower loose (310 to 360) sand layer and inclination (00 to 450) of the applied load with respect to vertical. The output is the ultimate bearing capacity. Further, the impact of the individual variable on the bearing capacity was also assessed by conducting sensitivity analysis. The results reveal that, the load inclination is the major variable affecting the bearing capacity at embedment ratio 0, 1 and 2. Finally, the performance of the developed machine learning model was assessed using six assessing statistical parameters. The results reveal that the developed model was performing satisfactorily for the prediction of the ultimate bearing capacity of the rectangular footing on layered sand under inclined loading

Recent tendencies in the use of optimization techniques in geotechnics:a review

The use of optimization methods in geotechnics dates back to the 1950s. They were used in slope stability analysis (Bishop) and evolved to a wide range of applications in ground engineering. We present here a non-exhaustive review of recent publications that relate to the use of different optimization techniques in geotechnical engineering. Metaheuristic methods are present in almost all the problems in geotechnics that deal with optimization. In a number of cases, they are used as single techniques, in others in combination with other approaches, and in a number of situations as hybrids. Different results are discussed showing the advantages and issues of the techniques used. Computational time is one of the issues, as well as the assumptions those methods are based on. The article can be read as an update regarding the recent tendencies in the use of optimization techniques in geotechnics

Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve

This study evaluates the applicability of using a robust, novel, data-driven method in proposing surrogate models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio of coarse-grained soils using only the results of the grain size distribution analysis. The data-driven analysis has been conducted using evolutionary polynomial regression analysis (MOGA-EPR), employing a comprehensive database. The database included the particle diameter corresponding to a percentage of the passing of 10%, 30%, 50%, and 60%, coefficient of uniformity, coefficient of curvature, dry unit weight, optimum moisture content, and California bearing ratio. The statistical assessment results illustrated that the MOGA-EPR provides robust models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio. The new models’ performance has also been compared with the empirical models proposed by different researchers. It was found from the comparisons that the new models provide enhanced accuracy in predictions as these models scored lower mean absolute error and root mean square error, mean values closer to one, and higher a20−index and coefficient of correlation. Therefore, the new models can be used to ensure more optimised and robust design calculations

Analyse numérique de la stabilité des pentes renforcées par pieux

The assessment of slope stability is attributed to various critical conditions; one of which is the selfweight sliding stimulus, and the other one induces failure caused by a surface load condition (shallow foundation). In the particular case of a shallow foundation situated on a slope crest, the bearing capacity is significantly reduced. Therefore in practice, anti-slide piles are used to enhance the performance of the nearby footing. Whereas, the studies tend to rely on the hypothesis of purely vertical surface load condition. The present dissertation aims to contribute to the numerical and stochastic analyses by inducing vertical retaining structures, in order to deal with the group problem of slope stability and bearing capacity of an adjacent combined loaded strip footing. Firstly, a bibliographical research is presenting the most common deterministic and probabilistic methods, pertaining to slope stability assessment and bearing capacity of a shallow foundation. Followed by a presentation of bibliographical synthesis concerning studies published in the literature. The second part furnishes a contribution to the numerical analysis using the finite element software OptumG2. The investigation of the factor of safety is conducted under various conditions of a pile row, using elastoplastic shear strength reduction method. Thence after, a conducted study is done on the effect of reinforcing a cohesive slope by a row of multiple number of piles and a sheet pile wall on the undrained bearing capacity of a rigid strip footing, using the limit analysis

Advanced finite element modelling of coupled train-track systems : a geotechnical perspective

Abstract unavailable please refer to PD

Application of computational limit analysis to soil-structure interaction in masonry arch bridges.

For the assessment of Masonry Arch Bridges (MAB), many structural and material models have been applied, ranging from sophisticated non-linear finite element analysis models to much simpler rigid-block limit analysis models. i.e. elastic and plastic methods respectively. The application of elastic analysis to MAB suffers many drawbacks since it requires full mechanical characterization of ancient masonry structures. The mechanical characterization of ancient masonry is difficult since these structures have typically undergone a century or more of environmental deterioration and in many cases have been already subjected to extensive modification. Also, sophisticated material models generally require specialized parameters that are hard to assess, particularly if non-destructive tests are used. In these cases practicing engineers typically favour simpler material models, involving fewer parameters. Thus non-linear finite element methods or other sophisticated models may not be a good choice for the assessment of MAB, while simplified approaches for example based on limit analysis principles are likely to be more appropriate. In this research. a holistic computational limit analysis procedure is presented which involves modelling both soil and masonry components explicitly. Masonry bridge parts are discretized using rigid blocks whilst the soil fill is discretized using deformable triangular elements and modelled a.'i a Mohr-Coulomb material with a tension cut-off. Lower and upper bound estimates of the collapse load are obtained. Results are compared with those from recently performed bridge tests carried out in collaboration with the University of Salford. A key project finding is that the use of peak soil strength parameters in limit analysis models is inappropriate when the soil is modelled explicitly. However, use of mobilized strengths appears to be a promising way forward, yielding much closer correlation with experimental data

Bearing capacity of clay bed improved by sand compaction piles under caisson loading

Master'sMASTER OF ENGINEERIN

ISGSR 2011 - Proceedings of the 3rd International Symposium on Geotechnical Safety and Risk

Scientific standards applicable to publication of BAWProceedings: http://izw.baw.de/publikationen/vzb_dokumente_oeffentlich/0/2020_07_BAW_Scientific_standards_conference_proceedings.pd