## 3 Results and Discussion
### Geometrical Measurements Extraction
The accuracy of close-range photogrammetry in extracting geometrical measurements depends on several factors including the capturing scenario, the processing technique, the size of the object of interest, and the geometrical estimation approach; hence, the accuracy of CRP varies in different studies. Nevertheless, some studies were conducted with nearly similar settings to this study. For instance, one study [27] that utilized a pre-calibrated camera to estimate a small sand pile (\(\
u\)= 435 cm\({}^{3}\)) had a volumetric estimation error = 4.76%. Another study [22] that used video frames to estimate a volume of a sand pile (3000 cm\({}^{3}\)) stated that their models had a volumetric error between 0.7% and 2%. Despite the self-calibration approach utilized in our study, the models of both devices (i.e., DC and SP) provided accurate geometrical estimations compared with the results of the above studies. Furthermore, [PERSON] et al. [48] stated--based on previous research studies in the area of construction--that digital photogrammetry, in general, can provide accurate estimations with 1% error for volumetric measurements. Although the volumetric estimations of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{**Capturing Device**} & \multicolumn{4}{c}{**Surface Area (cm\({}^{2}\))**} & \multicolumn{2}{c}{**Volume (cm\({}^{3}\))**} \\ \cline{3-8}  & & **Actual** & **Estimated** & **Error (\%)** & **Actual** & **Estimated** & **Error (\%)** \\ \hline \multirow{2}{*}{Beam} & DC & \multirow{2}{*}{5058.56} & 5104.76 & 0.91 & \multirow{2}{*}{17466.62} & 17,723 & 1.47 \\  & SP & & 5154.04 & 1.89 & & 17,977 & 2.92 \\ \multirow{2}{*}{Cylinder} & DC & \multirow{2}{*}{1767.14} & 1774.97 & 0.44 & \multirow{2}{*}{5301.43} & 5282 & 0.37 \\  & SP & & 1775.98 & 0.50 & & 5337 & 0.67 \\ \multirow{2}{*}{Sand Pile} & DC & \multirow{2}{*}{–} & 3507.68 & \multirow{2}{*}{–} & 5490 & 5362 & 2.33 \\  & SP & & 3558.57 & & & 5315 & 3.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The geometrical estimations accuracy of both cameras’ models. Figure 21: The absolute distance variation across the compared models of: (**a**) the square beam specimen; (**b**) the cylinder specimen; (**c**) sand pile; along with the absolute distances histograms of the three specimens: (**d**) square beam specimen (\(\mu\) = 0.000657422, \(\sigma\) = 0.000660495); (**e**) the cylinder specimen (\(\mu\) = 0.000963073, \(\sigma\) = 0.00106704); (**f**) sand pile (\(\mu\) = 0.000610173, \(\sigma\) = 0.000677724). The data and scale bars provided are in meters. our study have errors that are slightly higher than that value (specifically, the beam and sandpile models), they were obtained with a more accessible, cost-effective, and direct approach compared with more accurate approaches that require expensive, pre-calibrated digital handheld cameras or camera-mounted drones. Generally, the error of a geometrical estimation extracted from a photogrammetric model is attributed to several sources. One source of error is that associated with the lens distortion of the camera utilized and not precisely modeled. Another source is the error associated with estimating the camera orientation parameters, especially when using a self-calibration approach based on unreliable Exif metadata. Additionally, there is the error associated with 3D points reconstruction, especially with reconstructing the target points used to scale the 3D data, which was described as RMSE. All these sources generally contribute to the overall estimation errors provided in Table 13, regardless of the camera used.