## 3 Results and Discussions
### Models
#### 3.1.2 Calibrated Models  
The collected data from geotechnical investigations can be repurposed to generate calibrated models that estimate the spatial geotechnical properties of other sites for which no data are present. Once calibrated models have been developed, they can be deployed across multiple locations in a region, enabling the delineation of maps. Calibrated models were developed and characterized as soil strength, soil behavior, and liquefaction susceptibility were developed. In contrast to site-specific models, which lack an equation form, calibrated models are trained to include fewer parameters to represent the relationship between parameters via an equation. Site-specific models have the disadvantage of being less interpretable than calibrated models. By using an equation to represent the relationship between parameters, it is easier to comprehend how the input parameter changes affect the output. This makes calibrated models a valuable tool for comprehending soil behavior and strength and predicting the probability of liquefaction. In soil mechanics, the unit weight of the soil is an essential parameter that plays a significant role in numerous geotechnical engineering analyses. The unit weight is affected by several variables, including the soil's water content and the level of compaction. To obtain precise results, it is necessary to calibrate the unit weight parameter based on its location and soil group. To accomplish this, four distinct calibrated models for the unit weight of the soil were developed based on its location and soil group. These models include the wet unit weight of coarse-grained soil, the dry unit weight of coarse-grained soil, the wet unit weight of fine-grained soil, and the dry unit weight of fine-grained soil. The natural in-situ unit weight of soil located above the groundwater table is the moist unit weight. The data on moist unit weight were mined and calibrated to enable data manipulation and facilitate the transformation of raw data into novel insights. As a result, machine learning techniques were used to develop and train the unit weight models. The neural network regression model has the strongest positive relationship (R\({}^{2}\) = 0.70) among the competing algorithms. Between SPT N-values 0 and 15, a sudden increase in unit weight can be observed, indicating a range from very loose to loose. In addition, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Tree** & **Discriminant** & **Naive Bayes** & **Nearest Neighbor** & **Neural Network** \\ \hline Soil Type & 70.2 & 42.8 & 53.2 & 93.9 & 56.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy rates of the site-specific classification models. beginning with N-values between 15 and 50, the unit weight increases slowly but steadily, as demonstrated by Equations (4) and (5). \[\gamma_{moistCG}=0.2868\mathrm{N}+12.559 \tag{4}\]  
\[\gamma_{moistCG}=0.1066\mathrm{N}+15.171 \tag{5}\]  
where \(\gamma_{moistCG}\) is the moist unit weight of coarse-grained soils (\(\mathrm{kN}/\mathrm{m}^{3}\)), N is the SPT N-value, and the SPT N-value range: 1 \(\leq\) N \(\leq\) 15 for Equation (4), and 15 \(<\) N \(\leq\) 50 for Equation (5). A new trend in unit weight estimation has emerged that differs from previous trends established by notable researchers [23; 47; 48; 49; 50; 51]. These earlier studies examined the relationship between SPT N-values and the moist unit weight and discovered a consistent increase. However, a significant limitation of these earlier models was that they did not account for the situation in which an SPT value of \"0\" was present. For instance, both [PERSON]'s (2018) and [PERSON]'s (2019) models include an intercept when the SPT N-value is \"0,\" leading to unreliable estimates of 12.25 \(\mathrm{kN}/\mathrm{m}^{3}\) and 16 \(\mathrm{kN}/\mathrm{m}^{3}\), respectively. Therefore, this new trend in unit weight estimation is significant due to its capacity to address and rectify the flaws of previous models.