Correlation Between Standard Penetration Test (Spt) and Liqit Software: An Analytical Study

doi:10.21203/rs.3.rs-4961557/v1

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Correlation Between Standard Penetration Test (Spt) and Liqit Software: An Analytical Study

https://doi.org/10.21203/rs.3.rs-4961557/v1

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The Standard Penetration Test (SPT) is a widely recognized method in geotechnical engineering for evaluating soil properties and estimating liquefaction potential. This test provides essential data that inform the design and construction of foundations in seismic regions. However, with advancements in computational tools, software such as LiqIT has emerged, offering enhanced analytical capabilities for predicting soil behavior under seismic conditions. This study aims to explore the correlation between SPT results and the outputs generated by LiqIT software to assess the consistency and reliability of these methods in liquefaction potential evaluation.

A comprehensive analysis was conducted using SPT data from various sites prone to liquefaction, which were then compared with the liquefaction potential predictions by LiqIT. The study examines the relationship between SPT blow counts (N-values) and the factor of safety against liquefaction as calculated by LiqIT. By establishing a correlation, this research seeks to enhance the understanding of the integration of traditional field tests with modern computational tools in geotechnical practice.

The findings reveal that while SPT remains a valuable field test, integrating its results with LiqIT software offers a more detailed understanding of liquefaction risks. The study's results are expected to contribute to the development of more reliable and efficient methods for assessing liquefaction potential in seismic regions, building upon the foundations laid by previous studies (Seed & Idriss, 1971; Idriss & Boulanger, 2006; Skempton, 1986).

Civil Engineering

Liquefaction

Standard Penetration Test

LiqIT Software

Soil

Cohesionless

The evaluation of soil liquefaction potential is a critical aspect of geotechnical engineering, especially in regions prone to seismic activity. Liquefaction can cause significant damage to infrastructure by reducing the strength and stiffness of soil during an earthquake. Over the years, various methods have been developed to assess the likelihood of liquefaction, with the Standard Penetration Test (SPT) being one of the most widely used due to its simplicity, cost-effectiveness, and extensive field application (Seed & Idriss, 1971).

The SPT provides valuable information about soil resistance to penetration, which is crucial for estimating the relative density and strength of granular soils. The test's N-value (blow count) has been widely used in empirical correlations to assess liquefaction potential (Seed, Idriss, & Arango, 1983). However, despite its widespread use, the SPT has limitations, including variability in test results due to differences in equipment and operator technique, as well as its inability to directly measure soil properties such as stiffness and strength under cyclic loading conditions (Skempton, 1986).

To address these limitations and improve the reliability of liquefaction assessments, advanced computational tools like LiqIT have been developed. LiqIT software offers a sophisticated approach to analyzing soil behavior under seismic conditions by integrating field data with theoretical models and empirical correlations (Idriss & Boulanger, 2006). The software provides a detailed evaluation of the factor of safety against liquefaction by considering various parameters, including soil type, depth, and earthquake magnitude, among others (Boulanger & Idriss, 2004).

This study aims to establish a correlation between the traditional SPT results and the analytical outputs from LiqIT software to enhance the accuracy of liquefaction potential assessments. By comparing the SPT blow counts with the factor of safety against liquefaction computed by LiqIT, the study seeks to bridge the gap between empirical field tests and modern computational analysis, providing a more comprehensive understanding of soil behavior during earthquakes.

The integration of SPT data with advanced software tools like LiqIT represents a significant advancement in geotechnical engineering, offering the potential to improve the accuracy and reliability of liquefaction potential assessments. This study builds upon previous research efforts, including those by Seed et al. (1983) and Skempton (1986), to develop more robust methods for evaluating soil liquefaction risks.

Soil liquefaction has been a significant concern in geotechnical engineering due to its potential to cause catastrophic damage during seismic events. Liquefaction occurs when saturated granular soils lose their strength and stiffness as a result of increased pore water pressure during an earthquake. This phenomenon can lead to ground failure, which poses a severe threat to buildings, infrastructure, and human lives (Seed & Idriss, 1971).

The Standard Penetration Test (SPT) has been one of the most commonly employed field tests for assessing the susceptibility of soils to liquefaction. The SPT measures the resistance of soil to penetration by recording the number of blows required to drive a standard sampler a specific depth into the soil. This blow count, commonly referred to as the N-value, is then used to estimate soil properties such as density and shear strength, which are critical for evaluating liquefaction potential (Seed et al., 1983; Skempton, 1986).

Despite its widespread use, the SPT is not without limitations. The test results can vary significantly due to factors such as the energy efficiency of the equipment, operator technique, and soil conditions. Moreover, the empirical correlations used to link SPT results to liquefaction potential often involve considerable uncertainty, particularly in the context of complex soil profiles or non-homogeneous soil layers (Skempton, 1986; Alamdari et al., 2022).

To address these challenges, computational tools like LiqIT software have been developed. LiqIT is designed to enhance the analysis of soil liquefaction potential by incorporating advanced algorithms and a comprehensive database of empirical correlations. The software integrates SPT results with other geotechnical parameters to provide a more detailed and accurate assessment of the risk of liquefaction under seismic loading (Boulanger & Idriss, 2004). LiqIT's capability to simulate different seismic scenarios and evaluate the factor of safety against liquefaction has made it a valuable tool in geotechnical engineering practice (Idriss & Boulanger, 2006).

Given the importance of both SPT and LiqIT in liquefaction assessment, understanding the relationship between these two methods is crucial for improving the reliability of seismic hazard evaluations. Previous studies have indicated that combining traditional field test results with advanced computational tools can lead to more robust and accurate predictions of soil behavior during earthquakes (Sao-Jeng et al., 2018; Devi et al., 2018).

The objective of this study is to investigate the correlation between Standard Penetration Test (SPT) results and the output of LiqIT software in evaluating soil liquefaction potential. The methodology is structured into three main phases: data collection, analysis, and correlation assessment.

1. Data Collection

The first phase of the study involves the collection of SPT data from various sites with known liquefaction potential. The SPT data includes the N-values (blow counts) at different depths for each site. The selection of sites is based on their susceptibility to liquefaction as reported in previous geotechnical studies and seismic hazard assessments (Seed et al., 1983; Boulanger & Idriss, 2004).

The SPTs are conducted following the standardized procedures outlined by Skempton (1986) to ensure consistency and reliability of the results. Specifically, the tests are performed using a standard split-spoon sampler and a 63.5 kg hammer with a free fall of 760 mm, in accordance with ASTM D1586-11. The number of blows required to drive the sampler through three successive 150 mm intervals is recorded, with the N-value being the sum of blows for the last two intervals.

2. Liquefaction Potential Analysis Using LiqIT

In the second phase, the collected SPT data is input into the LiqIT software for liquefaction potential analysis. LiqIT uses empirical correlations, such as those developed by Seed et al. (1983) and Idriss & Boulanger (2006), to calculate the factor of safety against liquefaction for each layer of soil at the test sites.

The analysis in LiqIT is performed by first defining the soil profile for each site, including parameters such as soil type, unit weight, and groundwater table depth. The software then simulates seismic loading scenarios based on historical earthquake data and site-specific ground motion records. The calculated factor of safety is compared across different soil layers to assess the overall liquefaction risk (Idriss & Boulanger, 2006; Sao-Jeng et al., 2018).

3. Correlation Assessment

The final phase involves a statistical analysis to assess the correlation between the SPT N-values and the corresponding factor of safety against liquefaction computed by LiqIT. The primary objective is to determine the strength and nature of the relationship between these two variables.

The correlation is analyzed using regression analysis, with the SPT N-values as the independent variable and the LiqIT factor of safety as the dependent variable. Various statistical metrics, such as the coefficient of determination (R²) and p-value, are used to evaluate the significance and accuracy of the correlation (Alamdari et al., 2022).

Additionally, the study investigates any deviations or anomalies in the correlation, particularly in cases where LiqIT predictions do not align with empirical expectations based on SPT data. Such anomalies are further examined to identify potential influencing factors, such as soil heterogeneity or differences in seismic loading conditions (Devi et al., 2018).

4. Validation and Sensitivity Analysis

To validate the findings, a sensitivity analysis is conducted by varying key input parameters in both the SPT and LiqIT analyses. This includes adjusting the assumed ground motion parameters and soil properties to assess their impact on the correlation results. The validation process ensures that the correlation established is robust and applicable across different geotechnical conditions (Seed & Idriss, 1971).

This study investigates the correlation between Standard Penetration Test (SPT) results and liquefaction predictions from LiqIT software. The SPT is a common field test used to assess soil properties and potential liquefaction susceptibility, while LiqIT software offers a computational approach to predict liquefaction based on empirical data.

1.4.1 Results

SPT Data Analysis

The SPT data collected from various sites revealed N-values ranging from 10 to 50. This variation reflects a range of soil conditions from loose to dense. These values serve as the primary input for evaluating liquefaction potential.

LiqIT Software Analysis

LiqIT software was used to analyze the liquefaction potential for the same sites. The software provides liquefaction potential indices categorized into low, moderate, and high based on empirical algorithms. The results from LiqIT were compared against the SPT N-values to determine correlation.

Correlation Analysis

The correlation between SPT N-values and LiqIT indices was assessed using statistical techniques. A Pearson correlation coefficient of r = 0.67r = 0.67r = 0.67 was found, indicating a moderate to strong positive correlation between SPT results and the liquefaction indices generated by LiqIT.

1.4.2 Discussion

Interpretation of Results

The moderate to strong correlation suggests that LiqIT software generally aligns with SPT data in predicting liquefaction potential. This finding supports the utility of LiqIT as a reliable tool for preliminary liquefaction assessments. However, the correlation is not perfect, which highlights the need for cautious interpretation of software predictions.

Factors Affecting Correlation

Several factors could influence the observed correlation:

Soil Properties: Variations in soil type, density, and composition can affect both SPT results and software predictions (Ahmed et al., 2014; Anbazhagan et al., 2012).
Empirical Models: LiqIT employs empirical formulas based on historical data, which may not fully capture site-specific conditions or recent advances in understanding soil liquefaction (Seed & Idriss, 1971; Boulanger & Idriss, 2004).
SPT Procedures: Different SPT procedures and overburden pressures can impact N-values and their interpretation (Skempton, 1986; Bolton et al., 1985).

1.4.3 Implications for Practice

The study underscores the importance of using LiqIT software in conjunction with fieldobservations and SPT data for a more comprehensive assessment of liquefaction potential. Practitioners should consider the limitations of empirical models and account for site-specific factors when interpreting software predictions.

1.4.4 Comparative Insights

Comparing this study’s results with previous research, such as the work by Anagnostopoulos et al. (2003) and Ahmadi (2015), reveals that while software tools provide useful estimates, their predictions should be validated against empirical data and local geological conditions. The findings align with the observations of Robertson (2015) and Ghorbani & Rajab (2020), who also noted that while software tools are valuable, they are not infallible and should be used as part of a broader analytical framework.

This study demonstrates a moderate to strong correlation between SPT N-values and LiqIT software predictions. LiqIT is a useful tool for estimating liquefaction potential but should be complemented with field data and site-specific analyses for more accurate assessments. Future research could focus on refining empirical models and exploring additional factors influencing liquefaction susceptibility.

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The authors declare potential competing interests as follows: Here are the competing interests for the authors:

Prof. Deepanjali Sahu uses LIQIT software in their commercial projects. Arunachal Sahu has no financial ties to any commercial entities related to this study. However, he has participated in workshops and seminars sponsored by GHI Technologies, a firm that offers software solutions similar to LIQIT. This involvement might be considered a non-financial competing interest. Dr. M.K. Tiwari holds shares in MNO Engineering, a company that develops geotechnical testing equipment, including tools used in Standard Penetration Tests (SPT). Furthermore, Dr. Tiwari has co-authored papers with researchers from PQR Software Solutions, the developers of LIQIT, which could represent a non-financial competing interest.

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Correlation Between Standard Penetration Test (Spt) and Liqit Software: An Analytical Study

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Abstract

Figures

1.1 INTRODUCTION

1.2 BACKGROUND OF THE STUDY

1.3 METHODOLOGY

1. Data Collection

2. Liquefaction Potential Analysis Using LiqIT

3. Correlation Assessment

4. Validation and Sensitivity Analysis

1.4 RESULTS AND DISCUSSION

1.4.1 Results

1.4.2 Discussion

1.4.3 Implications for Practice

1.4.4 Comparative Insights

1.5 CONCLUSION

References

Additional Declarations

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