Estimation Of Bearing Capacity Research Articles

Application of improved ANFIS approaches to estimate bearing capacity of piles

An accurate estimation of deep foundation bearing capacity in different types of soils with the aid of the field experiments results is taken into account as the most important problems in geotechnical engineering. In recent decade, applying a broad range of artificial intelligence (AI) models has become widespread to solve various types of complicated problems in geotechnics. In this way, this study presents an application of two improved adaptive neuro-fuzzy inference system (ANFIS) techniques to estimate ultimate piles bearing capacity on the basis of cone penetration test (CPT) results basically used in analysis of pile foundations. The first model was combination of ANFIS and group method of data handling (GMDH) and the second one was related to the integration of fuzzy polynomial (FP) and GMDH model. Furthermore, in the ANFIS–GMDH, constant coefficients of ANFIS model were optimized using gravitational search algorithm (GSA). To improve the proposed AI models for carrying out training and testing stages, a reliable database in form of four input variables included information about different properties of soils and driven piles obtained from CPTs results. Performance of the proposed approaches indicated that FP–GMDH had better performance (RMSE = 0.0647 and SI = 0.378) in comparison with ANFIS–GMDH–GSA (RMSE = 0.084 and SI = 0.412). The use of multiple linear regression and multiple nonlinear regression equations showed lower level of precision in prediction of axial-bearing capacity of driven piles compared to the ANFIS–GMDH–GSA and FP–GMDH techniques.

Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations

PurposeThis study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (qult) of rock foundations, which is a considerable civil and geotechnical engineering problem.Design/methodology/approachThe input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of qult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study.FindingsA new and explicit formula generated by GEP is proposed for the estimation of the qult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems.Originality/valueThe superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

GEOTECHNICAL CONSIDERATIONS OF PILING TESTING IN PROBLEMATICAL SOILS OF WEST KAZAKHSTAN

Estimation of bearing capacity and settlement plays an essential role in designing pile foundations of buildings. This paper presents the results of pile static and dynamic loading tests taken place in the construction sites of Caspian Sea Port Prorva and Tengiz. The static loading tests (SLT) and static compression loading tests (SCLT) were carried out according to the Kazakhstan GOST and ASTM standards correspondingly. The dynamic loading tests (DLT) were performed by GOST, as well as by pile dynamic analyzer (PDA) in accordance with ASTM standards. According to the results of DLT of driven joint piles (with a cross-section of 40×40 cm, lengths of 25 m and 27 m) performed by PDA, the bearing capacity of the piles was 2143 kN. In the construction site of Prorva, the bearing capacities of driven joint piles according to the results of SCLT were2067 kN and 2042 kN. In the construction site of Tengiz, the bearing capacities of driven piles tested by SLT were 1000 kN and 1605kN. These investigations are important for the understanding of soil-structure interaction, especially of driven piles with a cross-section of 40x40 cm and lengths of 12 and 16m in construction site Tengiz.

Estimation of Ultimate Bearing Capacity of Footings on Soft Clay from Plate Load Test Data Considering Variability

The ultimate bearing capacity of a foundation can be determined from the load–settlement response obtained from plate load tests. For clayey soil it is known that the ultimate bearing capacity of footing is equal to that of a test plate. However, the present study shows that it varies with the variation of type of loading, size and shape of test plate and soil consistency. For reaction type of loading, the load is observed as normally distributed random variable with an average co-efficient of variation of 0.018. For different shape and size of plates, the bearing pressure–settlement curves show a high degree of variation. Subsequently the bearing pressures and settlements are normalized to dimensionless parameters and the normalized plot shows a reduction in variability. Further, the normalized curves are fitted to the hyperbolic model by nonlinear regression with non-constant variance. The model parameters, their standard errors, 95% confidence intervals and co-efficient of determination are quantified. The horizontal asymptote of the hyperbolic model has been used to estimate the ultimate bearing capacity. This method yields a value very close to other conventional bearing capacity methods. A sensitivity analysis shows the influence of various parameters on the bearing pressure.

Study of the behaviour of skirted shallow foundations resting on sand

The performance of skirted shallow foundations resting on sand bed was investigated using physical modelling. Laboratory tests were performed on small-scale foundation models to study the behaviour of circular, skirted foundations subjected to vertical loads. The effects of foundation diameter, relative density of sand, skirt depth and roughness of the model surface on the bearing capacity and settlement of skirted foundations were studied. The results of the model tests have shown that using skirts improve the bearing capacity and settlement values of skirted foundations compared with shallow foundations without skirt. The improvement in bearing capacity and a reduction in settlement of shallow foundations increase with increasing the skirt depth, roughness of skirt sides and decreasing the relative density of sand. The ultimate bearing capacity of shallow foundations may be increased by up to 5·0 times and the settlement can be reduced to a value of 8% of those of foundations without skirts. On the basis of the obtained results, a series of charts are presented to estimate bearing capacity improvement and settlement reduction in terms of sand relative density, skirt depth to foundation diameter ratio and roughness of skirt sides. Skirted foundations resting on loose sand are more beneficial than those resting on medium and dense sand.

Experimental and intelligent techniques to estimate bearing capacity of cohesive soft soils reinforced with soil-cement columns

Deep Mixing (DM) method is a soil improvement technique, in which the soil is mixed in situ with stabiliser agents (cement and/or lime) in the form of slurry or powder. The group column is one of DM type that has been extensively used to treat subsoil. Limited attempts have been made to investigate the failure behaviour of a group of floating soil–cement columns. This paper presents a series of 1-G physical tests to identify the bearing capacity of floating soil–cement columns formed via DM method. Pre-formed soil cement columns of 24 mm diameter with different lengths (i.e. 50 mm and 100 mm) were installed beneath a rigid foundation of 80 mm width in a soft clay bed using replacing method. The study also compares the effect of different improvement area ratios i.e. 17%, 26% and 35% on the bearing capacity. Overall, the laboratory results suggest the improvement ratio in the range of 29–79% when the bearing capacity of the treated soil is compared with that of untreated soil. Additionally, using the results of the laboratory tests as well as a dataset comprising other related works (overall 55 sets of data), the paper proposes a predictive model of bearing capacity using adaptive neuro-fuzzy inference system (ANFIS) for cohesive soft soils reinforced with soil–cement columns. The prediction performance of the proposed ANFIS model (coefficients of determination equal to 0.989 and 0.960 for training and testing data, respectively) reveals the feasibility of the model.

Strut-and-tie models for deteriorated reinforced concrete half-joints

A reinforced concrete half-joint bridge consists of suspended span dapped-end beams or a full-width deck supported on the nibs of abutments or adjacent beams. The design of their disturbed regions is traditionally performed by means of strut-and-tie modelling. The design provisions found in standards and codes can be used for the assessment of existing structures with minor adjustments. However, current documents provide limited guidance on the incorporation of deterioration aspects such as corrosion, insufficient anchorage lengths, and crack formation.Experiments performed on 12 half-joint beams demonstrated the effects of single defects, but synergistic effects were also found to exist and might lead to much higher reductions than expected from the sum of individual defects. These results were compared to different strut-and-tie models (STMs) and the application of STMs to achieve the highest lower bound estimate of the load carrying capacity is discussed.For the beams studied in the current work, the predictions based on codes and standards, combined with appropriate methods to incorporate deterioration effects, led to safe load bearing capacity estimates. However, the developed STMs seem to be, in some instances, unable to pick up alternative load paths that develop as soon as the capacity of a certain tie is reached. Hence the actual capacities might be higher than what is obtained from the STM calculations.

Probabilistic assessment of pile capacity based on CPTu probing including random pile foundation depth

The modern recognition of subsoil with the use of CPTu static probes allows to obtain detailed information necessary for the designing. Registered basic two quantities, i.e. cone resistance qc and friction on the sleeve fs, often become direct data, which allow to estimate bearing capacity of the base and the side surface of the pile. Direct methods use similarity of the pile work and piezo-cone work during the examination. An important design stage is the appropriate development of measurement data prior to the commencement of the procedure of determining the pile bearing capacity. Algorithms generated on the basis of empirical experiments are often applied with the simultaneous use of test loads. The probabilistic approach is also significant, because it enables objective assessment of the reliability level of performed design calculations. This work contains an analysis of the impact on the estimated bearing capacity and reliability of a pile of variable random depth of the pile base. It also includes the determination of probabilities of obtaining the assumed safety index for the designed solution at random foundation depth.

Seismic Bearing Capacity Factor Considering Composite Failure Mechanism

This article describes how the design of shallow foundation needs complete knowledge about bearing capacity. During earthquakes additional lateral force acts at the foundation bed which reduces the bearing capacity. Most of the literature present either the pseudo static analysis or assume a planar failure surface to estimate seismic bearing capacity factors. Here, a pseudo dynamic approach that considers the time dependent effect of earthquake loading is employed. A composite failure surface has been considered for a more realistic estimation of seismic bearing capacity. New expressions were formulated to arrive at the seismic bearing capacity factor, considering the forces acting on the failure wedge based on the limit equilibrium approach. The effect of soil friction angles and the seismic peak of horizontal ground accelerations on the seismic bearing capacity were studied using the proposed method. It was observed that present pseudo-dynamic analysis with a composite failure mechanism gives lower values of seismic bearing capacity factors when compared to pseud- static analysis.

Estimation of Ultimate Bearing Capacity of SCP and GCP Reinforced Clay for Laboratory Load Test Data

Estimation of Ultimate Bearing Capacity of SCP and GCP Reinforced Clay for Laboratory Load Test Data

Semi-Deep Skirted Foundations and Numerical Solution to Evaluate Bearing Capacity

Semi-deep foundations are a remarkable solution in conditions where the soil beneath the foundation is loose to a great depth and there is no possible way to use any way of soil improvement and applying piles would not be a logical way considering their cost and time of enforcing. Skirted foundations are a type of semi-deep foundations that can penetrate to the soil up to two times of their breadth. Estimating bearing capacity of these foundations is a long geotechnical problem for engineers whether under absolute or combined loading because of their usage in offshore and onshore projects. For estimating the vertical bearing capacity of these foundations, series of finite element analyses were performed for a range of embedment ratios to investigate the effect of the length of the skirt. The foundation has been modelled with two different types of soil and the results validated with previous analytical, numerical and experimental researches. In addition, the bearing capacity of a skirted foundation under combined loading in V-H space has been analyzed by this approach and the 2-dimentional failure envelope has been presented.

Estimating bearing capacity of shallow foundations by artificial neural networks

In this study, the Artificial Neural Network, ANN is applied to data extracted from a large set of random data created by using Terzaghi and Meyerhof formulae. By using MS Excel, 3750 sets of data for Terzaghi's equation, 4000 for Meyerhof's equation were generated. A simulated ANN was trained on a subset of bearing capacity data, and the performance was tested on the remaining data. The performances of the ANN models were compared to Terzaghi and Meyerhof results. ANN models were as accurate as the other techniques in estimating the ultimate bearing capacity. The models estimated the ultimate bearing capacity with an average error of around 1% of the value obtained from Terzaghi and Meyerhof equations, and the coefficient of determination (r2) was almost equal to 1. Their sensitivity and specificity is dependent on the function and the algorithm used in the training process. Validation subset is crucial in preventing the over-fitting of the ANN models to the training data. ANN models are potentially useful technique for estimating the bearing capacity of the soil. Large training data sets are needed to improve the performance of data-derived algorithms, in particular ANN models.

Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil

The application of models provided by artificial neural network (ANN) in predicting bearing capacity of driven pile is underlined in several investigations. However, weakness of ANN in slow rate of convergence as well as finding reliable testing output is known to be the major drawbacks of implementing ANN-based techniques. The present study aims to introduce and evaluate an optimized ANN with imperialism competitive algorithm (ICA) model based to estimate bearing capacity of driven pile in cohesionless soil. The training data for optimizing the ICA-ANN structure are based on the in situ study. To develop the ICA-ANN model, the input parameters are internal friction angle of soil located in shaft (φ shaft), and tip (φ tip), pile length (L), effective vertical stress at pile toe (σ v), and pile area (A) while the output is the total driven pile bearing capacity in cohesionless soil. The predicted results are compared with a pre-developed ANN model to demonstrate the ability of the hybrid model. As a result, coefficient of determination (R 2) values of (0.885 and 0.894) and (0.964 and 0.974) was obtained for testing and training datasets of ANN and ICA-ANN models, respectively. In addition, values of variance account for (VAF) of (88.212 for training and 89.215 for testing) and (96.369 for training and 97.369 for testing, respectively) were obtained for ANN and ICA-ANN models, respectively. The obtained results declare high reliability of the developed ICA-ANN model. This model can be introduced as a new model in field of deep foundation engineering.

Prediction of bearing capacity of thin-walled foundation: a simulation approach

In the recent past years, utilization of intelligent models for solving geotechnical problems has received considerable attention. This paper highlights the feasibility of adaptive neuro-fuzzy inference system (ANFIS) for predicting the bearing capacity of thin-walled foundations. For this reason, a data set comprising nearly 150 recorded cases of footing load tests was compiled from literature. Footing width, wall length-to-footing width ratio, internal friction angle, and unit weight of soil were set as inputs of the predictive model of bearing capacity. In addition, a pre-developed artificial neural network (ANN) model was utilized to estimate the bearing capacity of thin-walled foundations. The results recommend the workability of ANFIS in predicting the bearing capacity of thin-walled foundation. The coefficient of determination (R 2) results of 0.933 and 0.875, and root mean square error (RMSE) results of 0.075 and 0.048 for training and testing data sets show higher accuracy and efficiency level of ANFIS in estimating bearing capacity of thin-walled spread foundations compared to the ANN model (R 2 = 0.710, RMSE = 0.512 for train, R 2 = 0.420, RMSE = 0.529 for test). Overall, findings of the study suggest utilization of ANFIS, as a feasible and quick tool, for predicting the bearing capacity of thin-walled spread foundations, though further study is still recommended to enhance the reliability of the proposed model.

Estimation of bearing capacity of floating group of stone columns

Stone column is one of the ground improvement techniques. This technique has a proven performance, short time schedule, durability, constructability and low costs. The stone column technique has been used as a method of reinforcement of soft ground over the past 30years. The bearing capacity of the stone column still has high level of uncertainties because the existing formulas for the estimation of the bearing capacity are general and do not take into consideration the type of the stone column whether if it’s floating or end bearing and the method of the implementation of the stone columns and the length to diameter ratio of the stone column (L/D) and many other factors that affect the bearing capacity of the stone column.In the present study, a general equation was obtained by carrying out statistical analysis using the SPSS (Statistical Package for the Social Sciences) program from the present experimental work and previous studies. The equation is used to estimate the bearing capacity of floating stone column group installed in clays of different undrained shear strengths between (4–25)kPa and with different diameters and L/D ratios constructed by cased bored method. The equation indicates that the most controlling parameter in the prediction of stone columns bearing capacity, qu, is the area replacement ration, As, where qu increases considerably with increase of As, i.e. decrease of spacing between columns.

Applicability of Small Strain Stiffness Parameters for Pile Settlement Calculation

This study presents the importance of using advanced constitutive soil models in numerical modelling as a daily geotechnical practice. Back-calculated stiffness from observations of the soil deformations in-situ very often are greater than those used for the geotechnical design. Design is often too conservative, because of insufficient soil investigation data, in terms of applied material models and simplification in analytical or numerical design [2–4,10,14].Hence, there are highlighted advanced soil testing techniques and consecutive soil material models in this study, which could help to estimate pile settlements in PLAXIS 3D AE02 numerical code. Pile loading test is simulated for different pile diameters and interface constrains at the specific site, where residential building is designed on piles. Bender element tests were conducted at laboratory for subglacial till as the end-bearing soil layer for the project, and Hardening Soil Small (HSSmall) soil material model applied in the numerical analysis. The presented approach gives initial pile bearing capacity estimation for different pile diameters and interface constrains, so designer can decide the most technically and economically substantiation technology. In-situ pile loading test data essentially would help to calibrate initial model for the designed pile, finally adjusting the geotechnical design.

Contribution to Estimating Bearing Capacity of Pile in Clayey Soils

AbstractThe estimation of real geotechnical parameters is key factor for safe and economic design of geotechnical structures. One of these are pile foundations, which require proper design and evaluation due to accessing more deep foundation soil and because remediation work of not bearable piles or broken piles is a crucial operation. For this reason, geotechnical field testing like cone penetration test (CPT), standard penetration (SPT) or dynamic penetration test (DP) are realized in order to receive continuous information about soil strata. Comparing with rotary core drilling type of survey with sampling, these methods are more progressive. From engineering geologist point of view, it is more important to know geological characterization of locality but geotechnical engineers have more interest above the real geotechnical parameters of foundation soils. The role of engineering geologist cannot be underestimated because important geological processes in origin or during history can explain behaviour of a geological environment. In effort to streamline the survey, investigation by penetration tests is done as it is able to provide enough information for designers. This paper deals with actual trends in pile foundation design; because there are no new standards and usable standards are very old. Estimation of the bearing capacity of a single pile can be demonstrated on the example of determination of the cone factorNkfrom CPT testing. Then results were compared with other common methods.

Dynamic Bearing Capacity of Shallow Foundation Under Earthquake Force

Prevailing practice for computation of bearing capacity of soil under seismic force allows for an increase in its allowable capacity by a factor of 1.25 as recommended in a number of codes around the world. However research and post reconnaissance survey of structures after earthquake shows contradicting evidence to the abovementioned assumption. A number of researchers have thus tried to develop the dynamic bearing capacity factors (Nc, Nq, N) under seismic loading to establish a realistic mathematical model for estimation of ultimate bearing capacity based on assumption that the seismic force is pseudo-static in nature. Present paper attempts to develop a mathematical model based on modal response analysis that shows that bearing capacity of soil varies with mode and actually there are nothing called dynamic bearing capacity factors. The allowable bearing capacity under service load condition is actually augmented by the vibrating soil mass and goes on to reduce the factor of safety provided to the ultimate bearing capacity and under conducive conditions can well reduce the factor of safety below 1.0 inducing failure to the foundation.

Evaluation of Ground Bearing Capacity Estimation Methods Based On Plate Loading Tests

Within the scope of this study, bearing capacities were calculated based on eleven different estimation methods in literature, using some mass and material properties for different rock units (magnetite, syenite, serpentinite, limestone, clayey limestone and gypsum) encountered in three different open-pit mines (Sivas-Ulaş Open-Pit Celestite Mine, Divriği Open-Pit Iron Mine and Kangal Open-Pit Coal Mine) around Sivas in Turkey. Through regression analyses between estimated bearing capacity values and those that had been determined as a result of plate loading tests, bearing capacity estimation methods specified in the literature were assessed. Moreover, four different equations to be used in bearing capacity estimation were proposed.

Problems with Approximate Bearing Capacity of Gravel Soils

The publication aims to bring undoubted importance approximate table bearing capacity of gravel soils, which are used in the new constructions. Tabulated values enable to determine the estimated bearing capacity of foundation soil for a simple and inexpensive construction to a depth of 1 meter. In the publication there are compares particular class of gravel soils in depending on the width of the base and ingredients of fine-grained soil. Gravelly soils are the best foundation soil in terms of bearing capacity, but the amount of fine-grained soils or poorly grained gravel, or gravel with a low value of relative density can greatly reduce this value.