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Reliable particle measurements are essential when investigating the size and shape of particles in various fields of study, including engineering geology. Common methods to determine the morphology of particles, such as 3D laser scanners, are typically expensive or require extensive preparation. Therefore, a setup-friendly, low-cost, and accurate approach is required. In this study, a practical Trick-Image-Capturing (TIC) method based on smartphone photogrammetry to obtain size and shape parameters of 3D coarse particles is proposed. The process of image acquisition and particle reconstruction of the TIC method is demonstrated. Then, the performance of the TIC method is comprehensively assessed under various conditions using a 3D laser scanner, including smartphone quality, particle shape, shooting method, and postprocessing software. An optimal setting of the TIC method is selected through the cloud-to-cloud distance. Finally, the size and shape parameters of 32 coarse particles using the TIC method are also evaluated. Results show that the optimum setup of the TIC method is using a smartphone camera with a resolution above 12 MP under the ICS mode with a 30° shooting angle with Metashape postprocessing software. The root mean square error (RMSE) of cloud-to-cloud distances and the average error ratios under the optimum setup are less than 0.075 mm and 3.5%, respectively. Due to its fast image capturing process, easy setup, and low cost, the TIC method is practical and we expect it to become one of popular methods of measuring 3D coarse particles. • A practical Trick-Image-Capturing (TIC) method for measuring 3D coarse particles is proposed. • The workflow, accuracy, and best practices of the method were comprehensively assessed. • The RMSE of cloud-to-cloud distances and the average error ratios are less than 0.075 mm and 3.5%, respectively. • This method is expected to become one of popular methods of measuring 3D coarse particles. [ABSTRACT FROM AUTHOR]

Physical model tests are considered to be useful tools and are widely used in landslide research. The three-dimensional (3D) surface deformation of slope models provides useful information for investigating the behaviour and mechanism of landslides. In this study, a measurement system called a multi-smartphone measurement (MSM) system was proposed and developed to measure the 3D surface deformation of slope models. This system includes nine identical smartphones, a USB hub, a control software package, and some postprocessing software. A series of high-quality images were simultaneously captured through the hardware devices and subsequently processed by using postprocessing software including 3D reconstruction and particle image velocimetry. A comparison with the results of a laser scanner was performed, and the performance of the MSM system was tested in two static models and one dynamic slope model. The system's accuracy was assessed using the root mean square error (RMSE) of the cloud-to-cloud distance. The results show that the accuracy of the MSM system for both the static and dynamic models is less than 1.1 mm. A short-period, low-cost, and easy-to-build system with millimetre accuracy has the potential to become a popular approach for measuring slope models. In addition, the performance of the MSM system is illustrated in three example applications. • A multi-smartphone measurement (MSM) system was demonstrated to measure the 3D shape and deformation in slope model tests. • The MSM system consists of nine smartphones, a USB hub, a computer, a control software package, and some postprocessing software. • The accuracy of the MSM system for both the static and dynamic slope models is less than 1.1 mm in the assessment tests. • The MSM system with short-period, low-cost, and easy-to-build has the potential to become a popular approach for measuring slope models. [ABSTRACT FROM AUTHOR]

Two artificial intelligence models-backpropagation neural network (BPNN) and support vector machines (SVM)-were created to investigate the effects of mesostructure characteristics on the shear mechanical behaviors of rock joints. This was achieved through learning training samples for the evaluation of the five basic geometrical parameters sensitivity (i.e., slope angle, horizontal orientation, elevation difference, curvature, and aperture distribution), and for determination of the shear failure regions when rock joints were subjected to low-normal and shear loads. First, the digital elevation models (DEMs) of rock joints were produced through point clouds collected using a laser scanning system. Five geometrical parameters were specified as the inputs for the artificial intelligence models, and an approach was developed to calculate the 3D aperture distribution using a point cloud registration algorithm. Shear failure regions were considered as the outputs, which were extracted from images taken after direct shear testing via the global threshold algorithm. Secondly, BPNN and SVM models were employed in order to establish relationships between the geometrical parameters and shear failure areas by machine learning on training samples. Thirdly, the information value (IV) algorithm was used to verify the two trained models. Results showed that the BPNN and SVM models made acceptable determinations of the shear failure areas, which corresponded to the real situation. The predictions from the BPNN and SVM models were more accurate than those from the IV algorithm. Furthermore, shear failure regions depended primarily on the aperture distribution of rock joints during the shearing process, followed by horizontal orientation, elevation difference, and then the slope angle. The effect of parameter curvature on shear failure was less than the other four parameters. • Two AI algorithms (BPNN and SVM) were used to determine the shear failure regions. • Five geometrical parameters were extracted from point clouds as the input parameters. • 3D aperture distribution was calculated via the feature-based registration algorithm. • Weights of five input parameters were estimated through trained BPNN and SVM models. [ABSTRACT FROM AUTHOR]

To better understand the shear mechanical behaviour, the information value (IV) and the fractal theory (FT) models are employed to determine the shear failure regions of rock joints under the low effective normal stress conditions. First, high density point clouds of rock joints at the initial stage (pre-shearing) were collected, using a laser scanning technology and four topography parameters (slope angle, horizontal orientation, elevation difference and curvature) were produced from point clouds, to represent the mesostructure features. Second, direct shear testing were conducted at the end stage (post-shearing), and shear striae on the rock joints were measured to assess shear failure regions, based on image segmentation. Third, by combining morphological features with experimental observations, correlation analyses are performed, to investigate the influences of topography parameters on shear failure behaviors of rock joints, based on IV and normalized weight W i. Similar findings are observed from the two algorithms, indicating that horizontal orientation has a larger impact on shear failure than the other three parameters. Furthermore, under the low normal loading, the shear failure of rock joints are more likely to occur in regions with a slope angle in the range of 30° to 40°, a horizontal orientation in the range of SE to SW (facing shear direction), an elevation difference in the range of 0.15 to 0.30 mm and a curvature in the range of −1000 to −200 mm−1 and 200 to 1000 mm−1. It is notable that the effect of normal loads level on shear failure regions should not be neglected. Eventually, a model is developed to predict the shear failure regions of rock joint and receiver operating characteristic (ROC) analyses show that shear failure determinations, based on the proposed model, match the actual situation. • Four topography parameters were produced from point clouds to represent the mesostructure features of rock joints. • Image segmentation was used to measure the shear failure regions of rock joints under low normal loads. • The relationships between the mesostructure parameters and shear failure regions were established based on IV and FT models. [ABSTRACT FROM AUTHOR]

To evaluate the possibility of utilizing commercially available devices for making joint roughness estimates, a digital single-lens reflex (DSLR) camera and a smartphone were used to reconstruct the three-dimensional models of 10 rock-joint specimens. The accuracy and efficiency were considered in investigating the performance of photogrammetry on the point clouds production and joint roughness estimation in the laboratory. Additionally, a handheld laser scanner was used in collecting high-resolution point clouds of the same specimens as reference data for evaluating the photogrammetry measurement accuracy. The interval for image capture was set at 8° with consideration for the data quality, point density, and computation time. The iterative closest point (ICP) algorithm was used to compare the surface topographies of the rock joints produced via photogrammetry and laser scanning. The point clouds generated by photogrammetry agreed with the laser-scanning results, with a small average difference (0.005 mm for the DSLR camera and 0.006 mm for the smartphone). Furthermore, two- and three-dimensional surface parameters were calculated using the three different sources of point clouds, which were in good agreement in the joint roughness estimates when comparing photogrammetry and laser scanning results. Comparative analyses revealed that both DSLR camera photogrammetry and smartphone photogrammetry can produce accurate point clouds of rock joints for roughness assessment; however, point clouds generated by the DSLR camera had higher accuracies than those generated by the smartphone. • The lens parameters determined in camera calibration differ from the nominal ones. • Interval between two camera positions affects the data accuracy and computation time. • Point clouds from photogrammetry are in good agreement with laser scanning data. • Similar estimates of joint roughness from photogrammetry and laser scanning data. • DSLR camera photogrammetry outperforms the smartphone for rock joint reconstruction. [ABSTRACT FROM AUTHOR]