Evaluation of Undrained Shear Strength of Soil, Ultimate Pile Capacity and Pile Set-Up Parameter from Cone Penetration Test (CPT) Using Artificial Neural Network (ANN)

Over the years, numerous design methods were developed to evaluate the undrained shear strength, Su, ultimate pile capacity and pile set-up parameter, A. In recent decades, the emphasis was given to the in-situ cone and piezocone penetration tests (CPT, PCPT) to estimate these parameters since CPT/PCPT has been proven to be fast, reliable and cost-effective soil investigation method. However, because of the paucity of a vivid comprehension of the physical problem, some of the developed methods incorporate correlation assumptions which might compromise the consistent accuracy. In this study, the Artificial Neural Network (ANN) was exerted using CPT data and soil properties to generate a better and unswerving interpretation of Su, ultimate pile capacity and ‘A’ parameter. In this regard, a data set was prepared consisting of CPT/PCPT data as well as relevant soil properties from 70 sites in Louisiana for the evaluation of Su. For ultimate pile capacity, a database of 80 pile load tests was prepared. Lastly, data was collected from 12 instrumented pile load tests for the interpretation of the ‘A’ parameter. Corresponding CPTs along with the soil borings were also collected. Presenting these data to ANN, models were trained through trial and error using different feed-forward network techniques, e.g. Back Propagation method. Different models of ANN were explored with cone sleeve friction, fs, and tip resistance, qt, as well as plasticity index, PI, effective overburden pressure, σ’vo, etc. as input data and were compared to the conventional methods. It was found that the ANN model with qt, fs, and σ’vo as inputs performed satisfactorily and was found to be better than the conventional empirical method of evaluation of Su. On the other hand, ANN models with pile embedment length, pile width, qt, and fs as inputs, outperformed the best-performed direct pile-CPT methods in the interpretation of ultimate pile capacity. Similarly, the ‘A’ parameter predicted by the ANN models (PI, OCR, and Su as inputs) was also in good agreement with the actual one. These findings, hence, fortifies the applicability of ANN for estimating the undrained shear strength, ultimate pile capacity and ‘A’ parameter from CPT data and soil properties

The Use of the Pile Driving Analyzer for Piles Seated on Rock

The PDA Test has been used for mostly driven piles since the 1970s. The PDA test gets the capacity of the pile by measuring the displacement of the pile being driven into the ground by using sensors called accelerometers. For the case in Georgia, the pile is sitting on the rock and being struck by the hammer selected. This pile sits on a hole that is already pre-drilled into the ground. There are concerns when striking the pile on hard rock such as damaging the pile because of too much hammer energy or other safety concerns in the field that will limit the data presented. The goal of this research is to determine the reliability of the PDA test’s use on hard and weathered rock and to see if it is useful to the economical design of the piles seated on rock or weathered rock in the future

Prediction of Pile Capacity Parameters using Functional Networks and Multivariate Adaptive Regression Splines

The soil is found to vary spatially everywhere in nature. As such, it’s generally a difficult task to predict the nature of soil for any particular application with traditional methods like experimental, empirical, finite element or finite difference analysis. Analysis with traditional methods taking into factor all the varying inputs makes it a complex problem, which is difficult to solve and comprehend. This necessitates the use of statistical modelling tool for the solution to problems concerning soil. Pile foundations are widely used in civil engineering construction. However, owing to the variable behavior of soil and the dependence of vertical pile load capacity on numerous factors, there does not exist a definite equation which can estimate the pile load accurately and include all the factors comprehensively. Artificial intelligence techniques are known to successfully develop accurate prediction models with the obtained input and output data form laboratory experiments or field data. Therefore, in the present study, Functional Network (FN) and multivariate adaptive regression splines (MARS) were used to develop prediction models for the lateral load capacity of piles, vertical capacity of driven piles in cohesionless soil, friction capacity of piles in clay, axial capacity of piles and pullout capacity of ground anchors. In all the cases, prediction equations were provided for the developed models which were found to be simple and can be easily used by practicing geotechnical engineers. A standalone application was also developed to facilitate the calculation of required pile capacity parameters based on the prediction equations. The prediction models built by FN and MARS were compared with different artificial intelligence (AI) techniques and empirical models available in the literature and FN and MARS were found to invariably outperform other AI techniques and empirical methods

Collapsibility Prediction of Stabilized Soil with Styrene-Butadiene Rubber Polymer Using ANFIS

Collapsible soils are among the problematic soils in nature that, due to moisture content increasing and under the same stress, show a high rate of decrease in volume. This volume reduction leads to loss of soil structure and ultimately to significant subsidence. Such soils in many parts of the world, including the Kerman province of Iran, necessitate researches regarding the collapsible soils\u27 behavior and characteristics. This study aims to investigate the effect of butadiene rubber on the stabilization of collapsible soils. The tested fine-grained soils that have been sampled from two different sites were stabilized through injecting different percentages of butadiene (the number of experiments was 84). The ASTM D5333 Double-Consolidation Method was applied to examine the stabilized soils on intact soil samples. The results show that the penetrations of butadiene rubber and the formation of butadiene rubber columns have led to a reduction in soil collapse. Considering the development of intelligent systems using the prediction behavior of stabilized collapsible soils, the adaptive neural-fuzzy inference system (ANFIS) model was used to predict the degree of collapsibility of soil samples stabilized by injection of Styrene Butadiene Rubber

Lateral Capacity of Piles and Caissons in Cohesive Soils

Upper bound plastic limit analysis (PLA) solutions have been widely used to assess maximum capacity of laterally loaded piles and caissons. However, for the specific case of short piles and caissons with aspect ratios generally ranging from one to three, the current solutions tend to over-estimate capacities. Furthermore, these over predictions seem to be significantly influenced by eccentricity of loading. This dissertation presents a unified upper bound plastic limit analysis solution aiming to improve predictions of capacity for the aforementioned cases. In addition, a simplified upper bound method is proposed for cases in which computational efficiency is needed. Both solutions are compared to results from three dimensional finite element studies. Towards this end, most of the existing simplified predictive methods typically apply to idealized soil strength profiles that are either constant or linearly increasing with depth. However, site investigations often reveal complex strength profiles that deviate significantly from simple linear profiles. One example is the case in which a superficial stiff layer overlays a thicker layer of very soft soil. The work herein presented also includes analyses of pile and caisson performance in stratified soils based on a three dimensional upper bound PLA with a collapse mechanism comprising a surface failure wedge, a flow-around region and a spherical base failure surface. An introductory discussion on the influence of soil stratigraphy and geology for design purposes is included. Selected strength distributions are representative from field data obtained through cone penetration testing. Finally, the installation of driven piles and suction caissons in clayey soils generates excess pore pressures that temporarily reduce load capacity due to side resistance. Time dependent dissipation of these excess pore pressures leads to recovery of side resistance, a process known as ‘setup’. Since many facilities cannot be put into operation until sufficient pile load capacity has been mobilized, realistic predictions of setup time can be important. A simplified method of analysis for calculation of the setup time following open ended pile penetration is also presented

An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques

The precise estimation of the bonding strength between concrete and fiber-reinforced polymer (FRP) bars holds significant importance for reinforced concrete structures. This study introduces a new methodology that utilizes soft computing methods to enhance the prediction of FRP bars’ bonding strength. A significant compilation of experimental bond strength tests is assembled, covering various variables. Significant variables that affect bonding strength are found in the study of this database. The prediction process is optimized using soft computing methods, particularly Gene Expression Programming (GEP) and the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR).The proposed soft computing approaches accommodate complex relationships and optimize prediction accuracy depending on the input variables. Results demonstrate its effectiveness in predicting bond strength and comparing it with existing codes and other models from the literature. The results have shown that the MOGA-EPR and the GEP models have high R2 values between 0.91 and 0.94. The proposed new models enhance the reliability and efficiency of designing and assessing FRP-reinforced concrete

Lateral Capacity of Piles and Caissons in Cohesive Soils

Upper bound plastic limit analysis (PLA) solutions have been widely used to assess maximum capacity of laterally loaded piles and caissons. However, for the specific case of short piles and caissons with aspect ratios generally ranging from one to three, the current solutions tend to over-estimate capacities. Furthermore, these over predictions seem to be significantly influenced by eccentricity of loading. This dissertation presents a unified upper bound plastic limit analysis solution aiming to improve predictions of capacity for the aforementioned cases. In addition, a simplified upper bound method is proposed for cases in which computational efficiency is needed. Both solutions are compared to results from three dimensional finite element studies. Towards this end, most of the existing simplified predictive methods typically apply to idealized soil strength profiles that are either constant or linearly increasing with depth. However, site investigations often reveal complex strength profiles that deviate significantly from simple linear profiles. One example is the case in which a superficial stiff layer overlays a thicker layer of very soft soil. The work herein presented also includes analyses of pile and caisson performance in stratified soils based on a three dimensional upper bound PLA with a collapse mechanism comprising a surface failure wedge, a flow-around region and a spherical base failure surface. An introductory discussion on the influence of soil stratigraphy and geology for design purposes is included. Selected strength distributions are representative from field data obtained through cone penetration testing. Finally, the installation of driven piles and suction caissons in clayey soils generates excess pore pressures that temporarily reduce load capacity due to side resistance. Time dependent dissipation of these excess pore pressures leads to recovery of side resistance, a process known as ‘setup’. Since many facilities cannot be put into operation until sufficient pile load capacity has been mobilized, realistic predictions of setup time can be important. A simplified method of analysis for calculation of the setup time following open ended pile penetration is also presented

A numerical study of the suitability of rigid inclusion ground reinforcement beneath caisson quay walls

The objective of this study was to determine whether rigid inclusions are suitable for reinforcement of the foundation of a caisson quay wall functioning as a container terminal. Apart from their brittle behaviour under lateral loading, rigid inclusions are well suited to the large uniform loads and stringent post-construction deflection tolerances associated with container terminal structures. Their inherent strength and stiffness means they have certain advantages over other stiffening columns commonly used for ground reinforcement in port expansion projects. Their mechanical properties allow construction to unrestricted heights at any construction rate and, in theory, RIs can be applied to all soil types. Additionally the locations of many ports coincide with rivers, deltas and estuaries which are associated with poor soil conditions often requiring ground improvement. Their suitability is of practical significance to port planners and engineers who are faced with the challenge of providing satisfactory foundation performance that is cost effective. The addition of RI ground reinforcement for this structural application would allow for greater flexibility in meeting these challenges. The literature review for this study was broad in its scope with emphasis placed on describing the mechanics of the problem, analysis methods and suitable installation methods for execution in the marine environment. One of the key outcomes of the literature review was identifying the problem of lateral loading due to "free-field" lateral ground movements. In light of this, suitable strategies for limiting and accommodating lateral loading of the RIs were proposed. A numerical study of the proposed ground improvement scheme was undertaken using the 3D finite element method. The key model outputs were caisson deflections and RI forces, moments and stresses, for the various simulated construction phases up to operational conditions. The model results were assessed in terms of the key foundation performance criteria which were related to STS crane rail tolerances and limiting tensile stresses in the RIs. This study found that for a firm clay subsoil condition the proposed RI ground reinforcement scheme met the foundation performance criteria for this structural application provided (i) strategies to limit lateral loading were implemented and (ii) the RIs were reinforced over the length where they were not fully compressed. While this study provided insights into the behaviour of RIs for this structural application, ultimately suitability is a function of range of factors, in addition to the limited technical performance criteria derived for this study