Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil

AbstractThe shortage of available and suitable construction sites in city centres has led to the increased use of problematic areas, where the bearing capacity of the underlying deposits is very low. The reinforcement of these problematic soils with granular fill layers is one of the soil improvement techniques that are widely used. Problematic soil behaviour can be improved by totally or partially replacing the inadequate soils with layers of compacted granular fill. The study presented herein describes the use of artificial neural networks (ANNs), and the multi-linear regression model (MLR) to predict the bearing capacity of circular shallow footings supported by layers of compacted granular fill over natural clay soil. The data used in running the network models have been obtained from an extensive series of field tests, including large-scale footing diameters. The field tests were performed using seven different footing diameters, up to 0.90m, and three different granular fill layer thicknesses. The results indicate that the use of granular fill layers over natural clay soil has a considerable effect on the bearing capacity characteristics and that the ANN model serves as a simple and reliable tool for predicting the bearing capacity of circular footings in stabilized natural clay soil

Prediction of Safe Bearing Capacity with Adaptive Neuro-Fuzzy Inference System of Fine-Grained Soils

A lot of fieldwork is required to assess the safe bearing capacity (SBC) of fine-grained soil using IS Code, along with performing shear parameters to determine angle of internal friction and cohesion. Standard penetration tests are conducted in order to obtain N-value of soil, and evaluating atterberg limits and dry soil density. Here, it is proposed that Adaptive Neuro-Fuzzy Inference System(ANFIS) is adopted to predict fine-grained soil's safe bearing capacity. For this, input parameters considered for ANFIS system are depth of foundation, dry density, liquid limit, plasticity index, Percentage fine fraction, width/Length ratio, and N-Value. A wide range of safe bearing capacity data from various site locations was investigated and trained on. Four different models were developed with variations in membership function for each input, all the models are used with a gaussbell type of membership function. Among the four, the third model is predicting the nearest value with an R2 of 0.9738. Based on the conclusion the ANFIS model is the most reliable technique for assessing the SBC of soils. Investigation of soil properties and estimation of safe bearing capacity will be having more difficulty with respect to skilled person to investigate and time required is also more as dimension of the footing changes SBC also varies. So, to overcome this type of problems my model will give you a best suitable and reliable SBC

Behaviour of Shallow Strip Foundation on Granular Soil Under Eccentrically Lnclined Load

Since the publication of Terzaghi’s theory on the ultimate bearing capacity of shallow foundations in 1943, results of numerous studies—both theoretical and experimental—by various investigators have been published. Most of the studies relate to the case of a vertical load applied centrally to the foundation. Meyerhof (1953) developed empirical procedures for estimating the ultimate bearing capacity of foundations subjected to eccentric and inclined loads. Based on the review of the existing literature on the bearing capacity of shallow foundations, it appears that limited attention has been paid to estimate the ultimate bearing capacity when the foundation is subjected to both eccentric and inclined load and the objective of present study stems from this paucity. Besides, only a few studies have been made to estimate the average settlement of embedded footings when subjected to eccentric load.In order to arrive at the objective and to quantify certain parameters, extensive laboratory model tests have been conducted to determine the ultimate bearing capacity of shallow strip foundation resting over sand bed and subjected to eccentric and inclined loads. The tests have been conducted on two types of sand i.e. dense sand and medium dense sand. The load inclination has been varied from 00 to 200 whereas the eccentricity varies from 0 to 0.15B (B = width of footing). Depth of the footing is varied from 0 to B. Traditionally,in all analysis of such problems; the line of load application is towards the center line of the footing. However, in this thesis, it is investigated for the two possible ways of load application i.e. (i) towards and (ii) away from the center line of the footing.Based on the model test results, an empirical non-dimensional reduction factor has been developed for each mode of load application. This reduction factor will compute the ultimate bearing capacity of footing subjected to eccentric and inclined load by knowing the ultimate bearing capacity of footings under centric vertical load at the same depth of footing. Similarly, neural network models have been developed under each mode of load application and combined mode of load application to compute reduction factor as described above. Finally, the developed equations are compared with the existing theories.vi In addition to bearing capacity, the settlement of eccentrically loaded embedded footings is investigated. Based on some of those laboratory test results as discussed above, an empirical procedure has been developed to estimate the average settlement of the foundation subjected to an average allowable eccentric load per unit area, where the applied load is vertical

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

A New Prediction Model for Slope Stability Analysis

The instability of river bank can result in considerable human and land losses. The Po river is the most important in Italy, characterized by main banks of significant and constantly increasing height. This study presents multilayer perceptron of artificial neural network (ANN) to construct prediction models for the stability analysis of river banks along the Po River, under various river and groundwater boundary conditions. For this aim, a number of networks of threshold logic unit are tested using different combinations of the input parameters. Factor of safety (FS), as an index of slope stability, is formulated in terms of several influencing geometrical and geotechnical parameters. In order to obtain a comprehensive geotechnical database, several cone penetration tests from the study site have been interpreted. The proposed models are developed upon stability analyses using finite element code over different representative sections of river embankments. For the validity verification, the ANN models are employed to predict the FS values of a part of the database beyond the calibration data domain. The results indicate that the proposed ANN models are effective tools for evaluating the slope stability. The ANN models notably outperform the derived multiple linear regression models

Modelling of geotechnical structures using multi-variate adaptive regression spline (MARS) and genetic programming (GP)

The soil is considered as a complex material produced by the weathering of solid rock. Due to its uncertain behavior, modeling the behavior of such materials is complex by using more traditional forms of mechanistic based engineering methods like analytical and finite element methods etc. Very often it is difficult to develop theoretical/statistical models due to the complex nature of the problem and uncertainty in soil parameters. These are situations where data driven approach has been found to more appropriate than model oriented approach. To take care of such problems in artificial intelligence (AI) techniques has been developed in the computational methods. Though AI techniques has proved to have the superior predictive ability than other traditional methods for modeling complex behavior of geotechnical engineering materials, still it is facing some criticism due to the lack of transparency, knowledge extraction and model uncertainty. To overcome this problem there are developments of improvised AI techniques. Different AI techniques as ‘black box’ i.e artificial neural network (ANN), ‘grey box’ i.e Genetic programming (GP) and ‘white box’ i.e multivariate adaptive regression spline (MARS) depending upon its transparency and knowledge extraction. Here, in this study of GP and MARS ‘grey box’ and ‘white box’ AI techniques are applied to some geotechnical problems such as prediction of lateral load capacity of piles in clay, pull-out capacity of ground anchor, factor of safety of slope stability analysis and ultimate bearing capacity of shallow foundations.. Different statistical criteria are used to compare the developed GP and MARS models with other AI models like ANN and support vector machine (SVM) models. It was observed that for the problems considered in the present study, the MARS and GP model are found to be more efficient than ANN and SVM model and the model equations are also found to be more comprehensive

CIVIL ENGINEERING, SCIENCE AND TECHNOLOGY CHALLENGES: GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING

The book is based on scientific and technological advances in various Geotechnical and Geoenvironmental Engineering areas of Civil Engineering. It nurtures therefore the exchange of discoveries among research workforces worldwide including those focusing on the vast variety of facets of the fundamentals and applications within the Geotechnical and Geoenvironmental Engineering area. To offer novel and rapid developments, this book contains original contributions covering theoretical, physical experimental, and/or field works that incite and promote new understandings while elevating advancement in the Geotechnical and Geoenvironmental Engineering fields. Works in closing the gap between the theories and applications, which are beneficial to both academicians and practicing engineers, are particularly of interest to this book that paves the intellectual route to navigate new areas and frontiers of scholarly studies in Geotechnical and Geoenvironmental Engineering area

An Experimental and Theoretical Study of Pile Foundations Embedded in Sand Soil

This study aimed to examine the load carrying capacity of model instrumented piles embedded in sand soil, and to develop and verify reliable, highly efficient predictive models to fully correlate the non-linear relationship of pile load-settlement behaviour using a new, self-tuning artificial intelligence (AI) approach. In addition, a new methodology has been developed, in which the most effective pile bearing capacity design parameters can be precisely determined. To achieve this, a series of comprehensive experimental pile load tests were carried out on precast concrete piles, steel closed-ended piles and steel open-ended piles, comprised of three slenderness ratios of 12, 17 and 25, using an innovative calibrated testing rig, designed and manufactured at Liverpool John Moores University. The model piles were tested in a large pile testing chamber at a range of different densities of sand; loose (18%), medium (51%) and dense (83%). It is worth noting that novel structural fibres were utilised and optimised for different volume fractions to enhance the mechanical performance of concrete piles. The obtained results revealed that the higher the values of the of the pile effective length, Lc (embedded length of pile), sand density, and the soil-pile angle of shearing resistance, the higher the axial load magnitudes to reach the yield limit. This can be attributed to the increase in the end bearing point and mobilised shaft resistance. In addition, the plastic mechanism occurring in the surrounding soil was identified as the leading cause for the presence of nonlinearity in the pile-load tests. Furthermore, a new enhanced self-tuning supervised Levenberg-Marquardt (LM) training algorithm, based on a MATLAB environment, was introduced and applied in this process. The proposed algorithm was trained after conducting a comprehensive statistical analysis, the key objectives being to identify and yield reliable information from the most effective input parameters, highlight the relative importance “Beta values” and the statistical significance “Sig values” of each model input variable (IV) on the model output. To assess the accuracy and the efficiency of the employed algorithm, different measuring performance indicators (MPI), suggested in the open literature, were utilised. Common statistical performance indexes, i.e., root mean square error (RMSE), Pearson’s moment correlation coefficient (p), coefficient of determination (R), and mean square error (MSE) for each model were determined. Based on the graphical and numerical comparisons between the experimental and predicted load-settlement values, the results revealed that the optimum models of the LM training algorithm fully characterised load-settlement response with remarkable agreement. Additionally, the proposed algorithm successfully outperformed the conventional approaches, demonstrating the feasibility of the current study. New design charts have been developed to calculate the individual contribution of the most significant pile bearing capacity design parameters “the earth pressure coefficient (K) and the bearing capacity factor (N )”. The improved approach takes into account the change in sand relative density, pile material type, and the pile slenderness ratios. It is therefore a significant improvement over most conventional design methods recommended in the existing design procedures, which do not consider the influence of the most significant parameters that govern the pile bearing capacity design process

Cone Penetration Testing 2022

This volume contains the proceedings of the 5th International Symposium on Cone Penetration Testing (CPT’22), held in Bologna, Italy, 8-10 June 2022. More than 500 authors - academics, researchers, practitioners and manufacturers – contributed to the peer-reviewed papers included in this book, which includes three keynote lectures, four invited lectures and 169 technical papers. The contributions provide a full picture of the current knowledge and major trends in CPT research and development, with respect to innovations in instrumentation, latest advances in data interpretation, and emerging fields of CPT application. The paper topics encompass three well-established topic categories typically addressed in CPT events: - Equipment and Procedures - Data Interpretation - Applications. Emphasis is placed on the use of statistical approaches and innovative numerical strategies for CPT data interpretation, liquefaction studies, application of CPT to offshore engineering, comparative studies between CPT and other in-situ tests. Cone Penetration Testing 2022 contains a wealth of information that could be useful for researchers, practitioners and all those working in the broad and dynamic field of cone penetration testing