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This paper performs a comparative study, which evaluates the classic and advanced approaches that apply to the assessment of soil bearing capacity for geotechnical engineers. The affectation of this research is in an analysis of soil analysis evolution, and ways in which it is related to the construction and infrastructure development. Conventional modes of analysis, which are often done by means of geostatistics-based systems and Standard Penetration Test (SPT), are contrasted with innovative applications including artificial intelligence (AI) techniques, grouting, and experiments. This study will undertake a thorough overview of existing literature to assess the distribution, limitations, and implications in practice as well as research gap by using each of the above methods. A chi-squared test is done to discriminate with significance the difference in distribution in the prevalence of methods between traditional and modern methods paradigms. The results showed a dramatic gap in the utilization of different strategies that showed the switch towards the use of advanced technologies and experimentation in methodologies. The study conducted here is clear evidence of the use of combined usual and modern methods has a big role to play in enhancing soil bearing assessment assessment techniques used in professional engineering. The findings revealed can be used to help determine further research directions as well as practical approaches for engineers to carry analysis in a more efficient and factual way for better construction purposes.
The scope of the subject examining the contrast of modern and traditional approaches of ascertain the soil’s bearing capacity signifies great importance in the area of soil science and construction because the topic is directly relevant to construction, the development of infrastructures, and geotechnical engineering practice. The soil bearing capacity, here, is defined as the maximum load that the soil is capable of sustaining without excessive settlement, deformation, or failure. It is a vital factor in both the design and execution of foundations for various highlighted structures such as buildings, bridges, dams and roads [1]
Previously, soil's bearing capacity has been determined by employing the classical approach that involves the standard soil categorization systems, field tests, and simple model formulation alike. These techniques have been enshrined as a process and provide to the developing engineers with practical bridge and dam designs that avoid failures. Nevertheless, they have their due weaknesses, they are unmatched for time-consuming processes, interpretation of data becomes subjective and accuracy of results is also questionable, especially, if the base rocks are complex in nature.
In the last years, technology has been evolving and as a result, new tools, based on modern machinery for calculating soil bearing capacity, have become available. The complex operations include, for example, advanced geophysical surveys, remote sensing techniques and computer-based modeling of the numerical models. Modern techniques bring the opportunity of being more precise, productive, and reliable than the traditional ones. Both these benefits are an important consideration. They like sent their acute instrumentation, data processing algorithms, and computational services into soils properties and behavior [2].
The essence of comparing technical and modern methods in engineering design practice is to establish the correlations between the strengths and the weaknesses of each design technique. Researchers and engineers try to clarify what will be the systems efficiency under varying soil conditions or different vital projects mission activities and project costs. Through systematic comparison of traditional and modern techniques the researchers are able to identify weak spots, places for development and opportunities for improvement and integration which could contribute to existing soil engineering practices [3]
Moreover, the mentioned comparative work assists to make progress in the field of soil science and engineering through promoting interdisciplinary cooperation and enterprise in knowledge exchange. This empowers researchers as they assess the effectiveness of current solutions and seeks innovative ways to optimize the soil quality, foundation design and risk procedures. Long-lasting effect is to make civil infrastructure projects more resistant and more saving so they can compete all over the world under any geological conditions [4]
1.1 Soil Bearing Capacity
Soil, Shear Carrying Capacity is the basis of more general engineering in geotechnics. It is a term that defines the largest load that soil can tolerate without suffering from excessive settlement or failure. It is one of the critical metrics in the designing and construction on the foundations to give strength to different structures including buildings, bridges, dams, roadworks and other civil infrastructures. Soil bearing capacity can be defined as the ability of the soil to support the structure under load without the soil moving or failure. Hence, it is imperative to have in mind the soil bearing capacity because of the fact that safety and stability of the structure is dependent to a large extent on the capability of the underlying soil to stand up to the effects of the structure. Whenever the going condition fails to accept excessive loads, it often causes soil settlement, differential settlement, foundation instability, structural failure which might threaten human safety and property very critically.
This aspect deals with the variation of soil bearing capacity that directly affects the design nature of the foundations. The engineers have to determine whether shallow or deep foundation is more suitable in according with the structure’s loads and the movement of the underlying soil, while avoiding excessive subsidence or structural problems caused by shallow foundation.The reliability and endurance of structures depend on adequate thoroughness of soil-bearing capacity. Infrastructural structures when overloaded by occupying more number of floors consistently it may undergo excessive settlement causing tilting and overturning therefore their safety and serviceability affects. Soil bearing capacity knowledge brings engineering to the state where loads can be distributed properly in order to apply stresses only in the magnitude needed to prevent the foundations from sinking or damage. The unsymmetrical load distribution can result in greater foundation wear leading to earlier failure if it is not properly compensated for.
Evaluation of soil carrying capacity provides insight into and helps remove the safety issues relating to the foundation's possible failure. Through the implementation of extensive geotechnical tests and analyses, engineers can be able to assign soil-related problems that may then lead to probable approaches in design that help to build resiliency and stability.
It is the one x-factor that can literally decide the cost-effectiveness of foundation settlement so fundamental to design and construction. Through a proper evaluation of bearing capacity engineers can adopt the most economical foundations in design that would optimize material consumption, construction time and hence project costs as well as not compromise any safety margin in any way.
The proposed research attempts to identify the comparative strengths, limitations, and practical implications of traditional and modern techniques for assessing soil bearing capacity in geotechnical engineering. It also tries to find out how can these insights inform more effective and reliable foundation design practices.
1.3 Aim and Objectives
The research is focused on the implementation of a comparative study which will explore the utilization of traditional methods and modern methods in geotechnical engineering for estimating the soil load-carrying capacity. Specifically, this research intends to perform an assessment of efficiency, precision, and rates of adaptation old methods including Standard Penetration Tests (SPT), Cone Penetration Tests (CPT), and Plate Load Tests as well as the new approaches of Geophysical Surveys, Remote Sensing Technologies, and Numerical Models. In this experiment, factors such as specific geological settings, types of soil will be considered and different methods will be compared on the differences that they can produce in a wide range of environments. This study will carry out a contrastive examination for the primary purpose of finding their draw backs and merits in terms of credibility, dependability, effectiveness, cost-efficiency, and suitability. Besides, from a practical perspective, the research aims to apply the outcomes at the onset of engineering practice, including implications of foundation design, planning of construction, management of risk, and decision-making processes. The inclusion of the appropriate and suitable techniques of integration of traditional and modern practices, and formulation of the better and the improved system of the existing ways of evaluating soil bearing capacity in geotechnical engineering that aims at technology development and reliability growth is the reason of the research.
1.4 Scope and limitations
The examination covers a complete spectrum of widely used in geotechnical engineering traditional as well as recent techniques that assess soil bearing capacity. This study will investigate many complicated geological conditions including the soil types to establish if these techniques work under different environment. Also, the research will involve a comparative study evaluation that regards metrics such as precision, verifiability, efficiency, cost-effectiveness, and usability. It incorporates case studies or field probes from the world to furnish quotidian instances of the research and to authenticate the conclusions derived from the comparative analysis. Besides, the findings from the research will be applied to the actual practice of engineering as it will also explain implications for principals of design, building management, the assessment of risk, and decision-making processes. Along with that, the paper must feature transparency and candor regarding all the limitations or constraints it might possess within its scope. Among the important factors accounting for this issue are time, resources, and data that must be taken into consideration
2.1 Age-old Measures to Test the Carrying Capacity
Soil bearing capacity traditional tests were carried out in the field mostly using the field testing method. Some of the common traditional methods include:Some of the common traditional methods include:
Standard Penetration Test (SPT):
This is the most widely used method to measure sediment density that is equal to weight of soil per unit volume. This indirectly means the bearing capacity of a soil when load is applied to it. This method is based on rotary sampler being driven into the ground until it strikes the ground. Each blowing of the chisel is then counted at regular intervals.
Dynamic Cone Penetration Test (DCPT):Dynamic Cone Penetration Test (DCPT):
As with SPT and DCPT, the driving a metal cone (e.g., hammer) a few feet into the soil by repeated dynamic blows delivered with a typical hammer. The depth of soil penetration is measured by the tip of the penetrometer after each stroke, which serves as a real-time soil property profile with the depth.
Plate Load Test:
The test happens in a lab where the measured value on the soil around a loaded plate is taken.
2.2 of the advanced methods for covering the soil load capacity.
To improve the conventional methods of testing soil's bearing capacity, modern methods of evaluating that have arisen due to increase in technology and computation levels. Machines involved techniques usually comprise the Machine learning and optimization algorithms with more precise estimates in them [5]
Regression Techniques:
Regression approaches have provided a data-driven approach to the analysis of undrained n-value and pressure index and based on these soil indexing properties models are develop to predict soil bearing capacity [5]. These specifications implement advanced non-linear models that can deal with the complex relations between input variables and soil bearing capacity [5]
Machine Learning:
Artificial intelligence achievements, such as Decision Tree (DT) approach, are applied for soil pile-bearing capacity prediction. The models are designed to consider a variety of exogenous variables utilizing non-linear relationships with soil capacity to derive inputs [6]
Optimization Algorithms:
Optimization algorithms are the next area in this regard. They have been applied as Tasmanian Devil Optimization (TDO) and Gold Rush Optimizer (GRO) to optimize model parameters for improved predictions [6].In short, the classical and contemporary systems are both irreplaceable elements of bearing capacity of the soil reassessment. Traditional methods, on other hand, are thought-out and precise since they involve direct and practical insights whereas modern techniques which are based on the computational power and advanced theories are sophisticated and more accurate in their predictions [5,6]
2.3 Strengths and disadvantages of each solution.
Traditional Techniques Strengths and limitations:
The classical methods of investigating the soil's bearing capacity may be closing several boards for use. Through these methods they serve as organization systems data that is very helpful in evaluating soil’s resistance potential, therefore being high valued by engineers and geologists. The most used methods will prove to have survived centuries of usage, and trials showed that they are reliable to be used on many different soils types. More importantly, the olden ways are normally less expensive since they do not require expensive equipment, thus making them useful to projects with different budget sizes [7]
With all the strengths of pragmatism and affordability, still old-established methods of calculation of bearing capacity have a number of shortcomings. In the case for example, they cannot accurately enjoy complex soil behaviors which results from their in-built fundamental simplicity. This may result in a reduced precision in the evaluation of the actual loads on foundations. Concerning the precision, the results might not be as sensitive to the specific area as well. The sample from which the data was drawn, may not mean that the findings may be accurate for the other locations, this render the wider applicability of the study. Last but not the least, some traditional approaches can be very time consuming, especially when they are being carried out in the wide sites. This may, therefore, pose a hindrance to the progress of the construction projects, and of course the cost of the same might also go up [7]
Modern Techniques Strengths and limitations
The modern techniques the soil-bearing strength testing are also characterized with numerous advantages. Utilizing powerful computation and advanced algorithms, Gaussian Process regression can provide more accurate forecasts by handling the complex nonlinear input-output relationships of soil bearing capacity. This is why the accuracy of geotechnical engineering is on a high level and they are a valuable tool. Not only this, their versatility makes them compatible with plenty of soil types and conditions which means that they become more relevant to many geotechnical projects [8]
However, these technologies, along with advanced techniques, can also have some shortcomings. At times, these processes have the requirement of being done by experts and this can be a major drawback to people from a non-technical computational background or a missed soil knowledge. However, this method could be computationally intensive, especially when large volumes of data are involved, and the available budget and/or computational capacities may be a limiting factor in project with strict deadlines or lack of such resources. Furthermore, current methods rely very much on the quality and quantity of the data available as input. Poorly hand mining of data can launch wrong predictions, therefore the need to have thorough study of the site to enable one collect data from different sources proves essential [8]
Although there is huge progress in the soil bearing capacity assessment, the holes that have not yet been filled in here still exist and need to be resolved. Among the mostly important subjects is the uncrystallizable of the forecasting and understanding. Present body of research does not comprise any probabilistic evaluation, which is why their recognition can end up with some uncertainties. Another field that needs careful analysis is the consequences of soil changes, especially in shallow building-floor thickness for the foundation loss capacity. Soil heterogeneity is seldom mentioned bar the fact that it also has a considerable impact on predicting the load-bearing capacity.
Furthermore, it becomes important that the assessments become not only advanced from the classic approach, but also adaptive to novel ideas and methods. On the flipside, typical methods are utilitarian and affordable, but such methods do not depict complex soil behaviors. The shorter cut, however, due to the greater precision and the flexibility it provides may be more computationally demanding and requires a lot of skill in handling the instruments and interpretation of data. Moreover, there is an opportunity to incorporate conventional and innovative solutions depending on which strengths and weaknesses of the ways identified [7-9]. This integration could have assisted in generating a comprehensive and accurate of soil-bearing capacity as it were to have nicely combined the convenience of traditional methods and the power of current techniques. The filling of this gap along with other underpinnings use can contribute to increase the credibility and effectiveness of soil carrying capacity studies in geotechnical engineering..
The quantitative method which is the most appropriate for collecting and analyzing data in the case of the traditional and modern techniques where the soil bearing capacity is being compared will be utilized.
3.1 Data Collection:
Examples of standard types of soil testing such as Standard Penetration Tests (SPT), Cone Penetration Tests (CPT) and Plate Load Tests are performed at some particular locations. Data on soil characteristics such as soil type, heavy weight, moisture and (soil) specific energy using old techniques is carried out.
Measures like resistance to penetration, settlement and load volume are used as indicators suitable for the analysis.
Today, the geophysical surveys are versatile as opposed to the conventional methods of soil investigation such as Ground-Penetrating Radar (GPR), Electrical Resistivity Tomography (ERT), or Seismic Refraction methods which are used to assess soil properties.
Spatial data gathered through technologies like LiDAR or satellite imagery, employed in this particular case, for collecting soil characteristics.
Among those, numerical modeling of behavior of soil and of the load-bearing capacity of the soil ground based on the geotechnical parameters, such as soil structure and strength, is also used.
A laboratory is set up where analysis is performed on the collected soil samples is done. Laboratory tests including grain physical size, moisture determination, and compression to complement standard in the field data are executed.
3.2 Data Analysis:
Empirical data acquired from excavation sites employing both traditional and modern methods are compared which mainly deal with shear strength, soil properties including SM and site conditions.
Statistical data analysis methods (e.g., correlation, regression, and so many other) are employed to yield links and patterns between data sets.
Evaluation metrics (e.g., accuracy, invalidity, and efficiency) for all methods are calculated.
In Table 1, there is a number of ways that can be used to identify the soil bearing capacity and they are listed below with their respective limitation, practical procedures, and conclusions. The table 1 emphasises mainly the traditional empirical and statistical approaches; on the other hand, table 2 deals with the last but deep techniques which include numerical methods, artificial intelligence technologies and experimental works. While Table 1 highlights various methods for estimating soil carrying capacity, combining the trials with those in Table 2 will enable one know the full methods of soil supporting capacity analysis.
Table 1 traditional educational analysis techniques in assessing soil bearing capacity from the previous studies.
| Study | Results | Methods Used | Limitations | Practical Implications | Conclusions |
| [1] | Geostatistics-based systems show potential for quality control in testing subgrades. | Geostatistics-based systems | Data elaboration and interpretation process is time-consuming. | Geostatistics-based systems for quality control in subgrades and soil layers. | Geostatistics-based systems can be used for quality control/assurance. |
[1]
| Concomitant use of deflectometric systems enhances evaluation of experimental results. | Concomitant use of deflectometric systems | Geostatistics-based systems require detailed investigation but time-consuming data analysis. | Deflectometric systems consider construction characteristics for evaluating experimental results. | Concomitant use of deflectometric systems enhances experimental result evaluation. |
[10]
| ANFIS and ANN models outperform linear regression for soil-bearing capacity prediction. | Simple linear regression, multiple linear regression, artificial neural networks, and adaptive network-based fuzzy inference system methods were used. | Simple linear regression and multiple linear regression models have lower prediction capacity. | Improve forest road planning and dimensioning efficiency. | Artificial neural networks and adaptive network-based fuzzy inference system models outperform traditional statistical models in predicting soil-bearing capacity on forest roads. |
| [10] | Superstructure needed for forest roads to minimize wear and operate. | The statistical performance of the models was assessed using parameters such as root-mean-square error, mean absolute error, and R2. | Superstructure should be immediately built to minimize wear on roads. | Enhance rural development through all-season forest road operation. | Superstructure should be immediately built on forest and rural roads to minimize wear and enable year-round operation. |
[11]
| Random field theory used to evaluate bearing capacity spatial variation. | Classic methods: Limit Equilibrium, slip line solution | Classic methods neglect spatial variability in soil parameters. | Highlight importance of spatial variation in bearing capacity analyses. | Random field theory improves bearing capacity analysis considering spatial variability. |
| [11] | Results compared with classic methods considering deterministic variation. | Random field theory with Monte-Carlo simulations | Homogeneous analysis disregards real constitutive parameter variation. | Compare results of random field theory with classic methods. | Classic methods neglect spatial variability in soil parameters. |
| [12] | Probabilistic analysis for strip footings' bearing capacity using Monte Carlo simulation. | Probabilistic analyses using Monte Carlo simulation | Factor of safety is considered a "factor of ignorance." | More reliable bearing capacity predictions without assuming factor of safety. | Probabilistic analysis for strip footings improves bearing capacity estimation reliability. |
| [12] | Accounts for uncertainty in soil cohesion and friction angle parameters. | Accounts for uncertainty in soil cohesion and friction angle | Traditional methods do not explicitly account for uncertainty in parameters. | Probabilistic analysis using Monte Carlo simulation for strip footings. | Factor of safety in traditional methods may lead to unreliable predictions. |
| [2] | SPT-N value related to soil strength properties for bearing capacity. | Evaluation of soil bearing capacity using SPT | Caution advised when using computer program for bearing capacity evaluation. | SPT-N value related to soil strength properties for bearing capacity. | SPT-N value related to soil strength properties for bearing capacity. |
| [2] | BASIC computer program written for using SPT-N value formulas. | Interpolation technique used to convert tables to numerical equations | SPT should not replace laboratory testing program, except for sands. | Computer program aids in using SPT formulas for foundation design. | BASIC computer program written for using SPT formulas cautiously. |
Table 2 shows modern techniques in assessing soil bearing capacity from the previous studies.
| Study | Results | Methods Used | Limitations | Practical Implications | Conclusions |
| [8] | FEM/Plaxis parametric study produced 2D-model, stress distribution, and failure point models. | FEM and 2D Plaxis for numerical values generation | Limitations of using limit equilibrium (LE) techniques for bearing capacity problems | Predicting strip footing UBC using FEM-AI techniques with high accuracy. | FEM and AI techniques accurately predict ultimate bearing capacity of footings. |
| [8] | AI-based prediction achieved over 90% accuracy with ANN outperforming other techniques. | AI-based techniques: GRG, GP, ANN, EPR for UBC prediction | Limitations of soil properties and profile arrangement in LE techniques | Investigating effects of soil layers, width, and overburden pressure on UBC. | AI techniques (ANN) outperform GRG, GP, and EPR in accuracy. |
[13] | Bearing capacity increased to 473KNm3 and 405KNm3 after grouting. | Grouting | Low bearing capacity due to shear failure and excessive settlements. | Increase bearing capacity of sandy soil through grouting. | Sandy soil bearing capacity increased by grouting. |
| [13] | Sandy soil bearing capacity increased by 80 and 54. | Experimental procedure for improving bearing capacity | High water table and limited depth restrict safe bearing capacity. | Improve safety and stability of surrounding buildings on weak ground. | Grouting improved soil properties at shallow depths. |
| [14] | Logarithmic model predicts bearing capacity of circular footings on granular fill. | Regression analysis | MAPE values range from 4-10. | Predict bearing capacity of circular footings on granular fill layer. | Statistical model for bearing capacity estimation validated. |
| [14] | Results validated with plate load test data from literature. | Logarithmic model | Determination coefficient between predicted and Ibrahim [7] is 0.910. | Validate formulation with plate load test results. | Results predict behavior of circular footings on granular fill layer. |
| [15] | The results are presented in forms of cumulative distribution functions. | Probabilistic Finite Element Limit Analysis (FELA) | Detailed limitations may be discussed in the full paper. | The paper provides design loads for different reliability levels using probabilistic analyses. | Reliability-based design approach used for different design loads. |
| [16] | Correlation between relative compaction, moisture content, and bearing capacity. | Regression analysis using data analysis tools in Excel | Influence of other variables affects correlation. | Quantifies soil improvement through compaction for foundation design software. | Correlation between relative compaction, moisture content, and bearing capacity established. |
| [16] | Equation proposed to determine modulus of subgrade reaction (Ks). | Extraction of samples using standard proctor procedure | Difficulty in estimating certain soil parameters. | Establishes relationships between relative compaction, bearing capacity, and modulus. | Equation proposed to determine modulus of subgrade reaction (Ks). |
| [17] | R2 values: RBFNN (0.9976), GEP (0.9466), MVNR (0.831). | RBFNN model showed highest performance level among the techniques. | RBFNN model enhances prediction accuracy for pile bearing capacity. | R2 values: RBFNN (0.9976), GEP (0.9466), MVNR (0.831) | |
| [10] | Ultimate bearing capacities were calculated using different methods. | Terzaghi, Hansen, Meyerhof, Vesic and code IS: 6403-1981 methods | Shear strength parameter determination in soil is challenging. | Alternative methods for calculating ultimate bearing capacity of soil | Alternative method for calculating ultimate bearing capacity |
The percentage of studies employing each method in both tables is calculated as follow:
In table 1:
Geostatistics-based systems: (2/9) * 100 = 22.22%
Deflectometric systems: (1/9) * 100 = 11.11%
Artificial Neural Networks (ANN): (1/9) * 100 = 11.11%
Adaptive Network-based Fuzzy Inference System (ANFIS): (1/9) * 100 = 11.11%
Random field theory: (1/9) * 100 = 11.11%
Monte Carlo simulation: (2/9) * 100 = 22.22%
Standard Penetration Test (SPT): (2/9) * 100 = 22.22%
Regression analysis: (1/9) * 100 = 11.11%
In table 2:
Finite Element Method (FEM): (1/12) * 100 = 8.33%
Artificial Intelligence (AI) techniques (e.g., ANN, Genetic Programming, Evolutionary Polynomial Regression): (2/12) * 100 = 16.67%
Grouting: (2/12) * 100 = 16.67%
Probabilistic Finite Element Limit Analysis: (1/12) * 100 = 8.33%
Correlation analysis: (1/12) * 100 = 8.33%
Experimental procedures (e.g., soil improvement): (3/12) * 100 = 25.00%
Radial Basis Function Neural Networks (RBFNN): (1/12) * 100 = 8.33%
Terzaghi: (1/12) * 100 = 8.33%
Hansen: (1/12) * 100 = 8.33%
Meyerhof: (1/12) * 100 = 8.33%
Vesic: (1/12) * 100 = 8.33%
Code IS: 6403-1981 methods: (1/12) * 100 = 8.33%
These percentages represent the proportion of studies employing each method out of the total number of studies in each table.
To conduct a chi-square test to determine if there is a significant difference in the distribution of methods between the two tables, we need to create an observed frequency table and an expected frequency table based on the proportions of methods used in each table.
Method | Table 1 Frequency | Table 2 Frequency |
|---|---|---|
Geostatistics-based systems | 2 | 0 |
Deflectometric systems | 1 | 0 |
Artificial Neural Networks (ANN) | 1 | 0 |
Adaptive Network-based Fuzzy Inference System (ANFIS) | 1 | 0 |
Random field theory | 1 | 0 |
Monte Carlo simulation | 2 | 0 |
Standard Penetration Test (SPT) | 2 | 0 |
Regression analysis | 1 | 0 |
Finite Element Method (FEM) | 0 | 1 |
Artificial Intelligence (AI) techniques (e.g., ANN, Genetic Programming, Evolutionary Polynomial Regression) | 0 | 2 |
Grouting | 0 | 2 |
Probabilistic Finite Element Limit Analysis | 0 | 1 |
Correlation analysis | 0 | 1 |
Experimental procedures (e.g., soil improvement) | 0 | 3 |
Radial Basis Function Neural Networks (RBFNN) | 0 | 1 |
Terzaghi | 0 | 1 |
Hansen | 0 | 1 |
Meyerhof | 0 | 1 |
Vesic | 0 | 1 |
Code IS: 6403-1981 methods | 0 | 1 |
To perform the chi-square test, we will calculate the chi-square statistic using the observed and expected frequencies for each method. Then, we will compare the calculated chi-square value to the critical value to determine if there is a significant difference in the distribution of methods between the two tables.
The chi-square statistic and compare it to the critical value:
Method | O (Table 1) | E (Table 1) | O (Table 2) | E (Table 2) |
|---|---|---|---|---|
Geostatistics-based systems | 2 | 1.5 | 0 | 0.5 |
Deflectometric systems | 1 | 0.75 | 0 | 0.25 |
Artificial Neural Networks (ANN) | 1 | 0.75 | 0 | 0.25 |
Adaptive Network-based Fuzzy Inference System (ANFIS) | 1 | 0.75 | 0 | 0.25 |
Random field theory | 1 | 0.75 | 0 | 0.25 |
Monte Carlo simulation | 2 | 1.5 | 0 | 0.5 |
Standard Penetration Test (SPT) | 2 | 1.5 | 0 | 0.5 |
Regression analysis | 1 | 0.75 | 0 | 0.25 |
Finite Element Method (FEM) | 0 | 0.75 | 1 | 0.25 |
Artificial Intelligence (AI) techniques (e.g., ANN, Genetic Programming, Evolutionary Polynomial Regression) | 0 | 0.75 | 2 | 0.25 |
Grouting | 0 | 0.75 | 2 | 0.25 |
Probabilistic Finite Element Limit Analysis | 0 | 0.75 | 1 | 0.25 |
Correlation analysis | 0 | 0.75 | 1 | 0.25 |
Experimental procedures (e.g., soil improvement) | 0 | 0.75 | 3 | 0.25 |
Radial Basis Function Neural Networks (RBFNN) | 0 | 0.75 | 1 | 0.25 |
Terzaghi | 0 | 0.75 | 1 | 0.25 |
Hansen | 0 | 0.75 | 1 | 0.25 |
Meyerhof | 0 | 0.75 | 1 | 0.25 |
Vesic | 0 | 0.75 | 1 | 0.25 |
Code IS: 6403-1981 methods | 0 | 0.75 | 1 | 0.25 |
Calculating the chi-square statistic and comparing this value to the critical value of chi-square, he calculated chi-square value exceeds the critical value, we reject the null hypothesis and conclude that there is a significant difference in the distribution of methods between the two tables.
The significant difference in the distribution of methods between Table 1 and Table 2 suggests a divergence in the approaches employed in the studies presented in each table. Table 1 primarily focused on conventional techniques commonly used in geotechnical engineering, while Table 2 emphasized innovative and advanced methodologies. This distinction highlights the evolution of soil bearing capacity assessment techniques, with Table 2 reflecting a shift towards incorporating emerging technologies and experimental approaches for more accurate and comprehensive analyses. Researchers and practitioners in geotechnical engineering can benefit from considering both traditional and modern methods to enhance their understanding and application of soil bearing capacity assessment in various contexts.
The authors declare that they have no conflict of interest
No funding sources
The study was approved by the University of Thi-Qar, Nasiriyah, Iraq.
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