The grain size analysis plays a significant role in any geotechnical study. The grain size analysis, by means of sieving, is usually used for coarse material of particle size > 75 μm. For the fine material; the sedimentation methods are frequently adopted (e.g., hydrometers). Other methods also exist such as electron microscopy, digital image analysis and laser diffraction. The fine geomaterials commonly undergo agglomeration which makes the recognition of individual grain size using digital image analysis or electron microscopy challenging. To facilitate and enhance the grain-size analysis, this study was conducted using the Laser Diffraction System (LDS). Seven samples with different nature (composition and texture) and sources were analyzed by hydrometer and LDS. For LDS, various factors were studied such as air pressure, sonication, dilution, refractive index, and distribution method (volume or number). The results were compared qualitatively and quantitatively based on soil classification systems, fractal dimensions, and other parameters. Furthermore, this study provided a novel criterion to determine which LDS distribution method (volume or number) is to be used depending on the Liquid Limit. A combined sieve-LDS system is recommended to obtain the entire grain size distribution. It is concluded that the LDS is a viable technique that can replace the time-consuming hydrometer method to assess the grain-size distribution.

The accurate determination of the ratio between design capacity and the measured capacity of piles is crucial for designing safe and cost-effective foundations. However, conventional methods for pile design rely on empirical equations which are unable to consider the complexity of soil-pile interactions due to lack of information about pile conditions and loading history. To overcome this issue, this study proposes a novel hybrid machine learning model named Tree-structured Parzen Estimator based Extreme Gradient Boosting (TPE-XGB) to estimate the effect of various pile and soil-related parameters including pile type, diameter, tip depth allowable pile on the ratio between design capacity from soil investigation and measured capacity from testing. For this purpose, 69 full-scale pile load tests were conducted, and GIS-based mapping was conducted to analyze spatial data. The TPE-XGB model was trained on the experimental data, and the results demonstrated the high efficacy of TPE-XGB model in predicting the output with 95% accuracy and minimal error (RMSE = 0.027). In addition, to interpret the findings of black-box TPE-XGB algorithm, Shapely Additive Analysis (SHAP) and Individual Conditional Expectation (ICE) analysis were used to identify the most important features and to explore the nonlinear relationships between input features and model output respectively. Finally, to facilitate the practical implementation of this study’s findings, a graphical user interface (GUI) was developed, allowing engineers to easily input site-specific parameters and obtain explainable model predictions. This data-driven approach offers a reliable tool for engineers to optimize pile design while ensuring transparency and reliability in decision-making.