Assessing the degree of granite weathering is essential for evaluating slope stability and ensuring the long-term performance of civil infrastructure in tropical environments. This study presents an integrated colorimetric and mechanical framework for quantifying granitic rock weathering by combining CIELAB color analysis with digital image processing and geomechanical testing. Field investigations were conducted at Kuad Quarry, Penang, Malaysia, where 128 in-situ color measurements (L*, a*, b*) were collected across six quarry slopes and incorporated into slope-scale image analysis framework for rock weathering classification. The colorimetric results show a strong linear relationship between the a* and b* parameters (R2 = 0.899), indicating consistent chromatic trends associated with progressive surface weathering. Image-based classification reveals that more than 70

The monitoring of slope stability like rock slopes and embankments usually takes lots of manpower and costly equipment for just one location. With the application of satellite sensing technologies, monitoring multiple wide locations simultaneously is now possible. However, as Synthetic Aperture Rader (SAR) images taken by the SAR-enabled satellite are generally complex and data-overwhelming, making use of the massive amount of data and properly extracting useful data are complicated and time consuming. To address the current issue of extracting valuable information from satellite sensing data, the authors performed satellite sensing analysis on a slope failure location and then applied a trend filtering method which has not been introduced to the SAR sensing industry before: ℓ1 trend filtering technique. As ℓ1 trend filtering technique was not introduced prior, critical parameters such as lambda (λ) must be calibrated before the algorithm can be applied. Hence, this paper discussed the satellite sensing principles, ℓ1 trend filtering principles, the hyperparameters tuning as well as the trend filtering result. As a result, the application of ℓ1 trend filtering effectively filters out important slope behavior and as well as dates where the slope changes its behavior.

During mountain tunnel construction, it is important to quickly and quantitatively evaluate the mechanical properties of rock for rational and safe excavation. Recently, several methods have been used to quantitatively asses rock hardness by analyzing perforation data from drill jumbos in medium-hard to harder rocks where blasting is used for excavation. On the other hand, there are currently no methods to quantitatively evaluate rock strength in soft rock and sandy mountains where rock breakers are generally used for excavation. Thus, the authors of this study aim to develop an evaluation method to quickly and quantitatively measure strength in softer rock mass. The authors proposed the use of vibration acceleration and sound pressure generated by rock breakers during excavation and verified the feasibility of this approach by performing laboratory tests on concrete specimens. Finally, the authors applied this measurement technique on rock masses of varying strengths and verified that this method is able to quantitatively evaluate rock strength. This paper presents the findings of this study.

This study proposes ℓ1 trend clustering, a novel method for analyzing spatiotemporal ground displacements obtained from satellite remote sensing techniques such as InSAR. The method integrates two machine learning algorithms—k-means clustering and ℓ1 trend filtering—into a unified framework that simultaneously performs clustering, trend estimation, and change point detection. The effectiveness of ℓ1 trend clustering was evaluated through three numerical examples, where it successfully estimated the true number of clusters, identified representative trends, and detected change points in noisy time-displacement curves. Additionally, its applicability was demonstrated using real subsurface settlement data from InSAR. The results indicate that ℓ1 trend clustering can effectively distinguish areas of high and low settlement risk while accurately estimating representative trends. ℓ1 trend clustering offers a simple yet powerful approach for analyzing spatiotemporal data from satellite remote sensing and can contribute to more accurate geotechnical risk assessment and improved geoinfrastructure management.

Multimodal image analysis and colourimetry offer valuable tools for measuring and analyzing colour variations associated with rock weathering. As weathering progresses, mineral composition changes, leading to different weathering grades. This study focuses on clastic sedimentary rock slopes and combines the CIELAB colour space with advanced image analysis techniques to develop a quantitative method for assessing weathering. By enhancing traditional visual inspection methods, this approach allows for a more precise and quantitative evaluation of weathering grades. The image analysis by threshold segmentation converts the RGB image to CIELAB to classify the weathering grades. The findings reveal a significant increase in the green–red value a* and blue-yellow value, b* of CIELAB colour space. This increasing value corresponds with a decrease in rock strength, indicating a direct relationship between colorimetric changes and geomechanical properties. The image analysis also highlights the percentage zoning of different weathering grades across the rock surface. The integration of image analysis provides a clear visual representation of the distribution and intensity of weathering, which is crucial for practical applications such as slope stability analysis. Results further reveal the clastic sedimentary rock shows high b* values in areas of high oxidation. The probability of failure is strongly correlated with rock strength and modulus. Low strength and low modulus generally lead to a high probability of all types of failures due to the material’s inability to withstand stress and its tendency to deform. This study successfully demonstrates a new approach to weathering assessment, providing significant improvement over traditional methods.

Short-term soil deformations present challenges for Interferometric Synthetic Aperture Radar (InSAR) analysis due to decorrelation, atmospheric noise, and limited satellite revisit intervals. These limitations hinder prompt detection of rapid, localized slope failures. This study proposes an integrated framework combining PS Reduction, l1 Trend Filtering, and the Inverse Velocity method to enhance short-term deformation analysis. We focused on decorrelation induced data gaps and analysed the scatter data before they were filtered out. The framework was applied to a slope failure case in Singapore, where the combined methods provided an estimated failure window as early as 7 days before the actual event. The approach demonstrates that integrating signal loss patterns, trend denoising, and failure timing estimation improves early warning capabilities for fast, small-scale geohazards.

Significant rock mass deformation exemplified by squeezing and/or swelling during tunnelling under adverse geological and stress conditions can lead to the failure of rock bolts. To address this issue, a deformationcontrolled rock bolt (DC bolt) was developed in a previous study. The DC bolt was designed to accommodate large ground deformations while resisting applied load, thereby controlling the shear displacement at the mortar-bolt interface and ensuring tunnel stability. This study experimentally and numerically investigated the effectiveness of a DC bolt under various rock mass and stress conditions. It considers the evolution of the radial confining pressure acting on the DC bolts during tunnel excavation to evaluate its impact on the bond strength and mechanical behaviour of the anchor-mortar interface. To achieve this, a three-dimensional numerical model that explicitly considers the mechanical behaviour of boreholes for rock bolts was constructed. After evaluating the mechanical parameters of the DC bolt based on a laboratory experiment, a tunnelling simulation was performed considering the deformation of the boreholes and their influence on the bolt-mortar interface strength. The results indicate that the radial confining pressure between the bolt and the mortar increased to more than 5 MPa with the tunnelling depth and progression of excavation, increasing the bond strength and therefore influencing the axial force of the bolts. Furthermore, the degree of influence of the confining pressure on the axial force depended on the stiffness of the rock mass. For rock masses with very low stiffness, the effect of the confining pressure was minimal because the large deformation of the rock mass caused bond failure immediately after installation of the bolt, prior to the increase in the bond strength. Conversely, the effect is pronounced for rock masses with relatively large stiffness values, increasing the axial force by a maximum of 40 % compared to the case without the radial-confining-pressure-dependent shear strength model. This study provides fundamental insights into the design of rock bolts, the mechanical behaviour of which is dictated by the anchor-mortar shear behaviour and failure, under various stress and ground conditions.

This research improved the traditional method of visually assessing macroscopic weathering by using image analysis based on CIELAB color data input. Focusing on the granite rock slope surface assessment in Malaysia, the current study combined the use of CIELAB colour space and image analysis to develop a rapid preliminary quantitative weathering assessment. The obtained results revealed the increase of a* and b* values with the weathering outcomes. The analysis of 50 measurement points of the studied rock slope revealed that a* and b* were positively correlated (R2 of 0.9027). The results of image analysis further revealed the susceptibility of the major zones (74

This research assesses the stability of sedimentary rock slopes in Teloi, Sik, Kedah, by focusing on the mechanical properties of the rock layers and their susceptibility to weathering. Key tests include the slake durability index (SDI), elastic modulus of knocking ball (Ekb), and electrical resistivity tomography (ERT). The incorporation of electrical resistivity tomography (ERT) data through the virtual reality platform facilitates the visualization of subsurface conditions. The variability of strength characteristic of interbedded sedimentary rocks leads to the differential weathering of rock layers, which causes deterioration on the slope structure. The testing revealed significant variability in rock strength, with sandstone displaying higher durability (Id > 17.1

Joint roughness coefficient (JRC) is an important criterion to determine the shear strength of rock, for example, as one of the inputs for the Barton–Bandis model. Conventionally, the Barton comb profilometer is widely used in the field but it is labour-intensive, has limited accessibility and involves potential hazard. This study aims to evaluate the structure from motion photogrammetry technique in producing reliable JRC measurements. To achieve this, a sample from a rock slope is used to determine the JRC readings. A drone is used to take a high-quality image of the rock slope using unmanned aerial vehicle photogrammetry method. Image processing consists of four quality ratings: low, medium, high and ultra-high. Digitalisation of the JRC of the rock slope takes place to create a 3D model using photogrammetry. The JRC measurement results are compared with the manual Barton comb profilometer method to verify the photogrammetry technique. As a result, the JRC of the rock slope can be produced by using the image analysis technique. The ultrahigh quality has the most accurate measurement as actual length with 0% error compared to actual measurements using Barton comb. For low, medium and high quality, the errors were 10.26%, 7.69% and 2.56%, respectively, to the actual length of the selected lines. However, the medium quality is the most efficient way because it can produce the reliable JRC measurement within a short period and can be used in fieldwork.

Rock slope instability causes rock slope failure with intense weathering processes in tropical regions. In addition to the various advanced numerical rock slope stability assessment techniques that have been developed thus far, simple evaluation techniques characterizing the rock slope weathering degree based on color changes of the rock surface remain one of the main techniques used by most geologists onto the rock slope stability assessment. The present study proposed a new evaluation method for weathering assessment of the limestone rock slope with computer-assisted image processing technologies to reduce human-induced errors based on a manual charac-terization process. The color changes of the limestone rock referring the CIELAB color space, which indirectly indicated the differential weathering of the rock mass, were measured in-situ using the FRU colorimeter with a 40 mm aperture. The Schmidt hammer was used to determine the intact rock strength and validate the color changes of the limestone rock to the International Society For Rock Mechanics (ISRM) weathering grade stan-dard. Results indicated that a* and b* correlated strongly with R2 = 0.7404, underscoring the consistency of the CIEL*a*b color space data obtained in situ. The a* and b* correlated strongly with the uniaxial compressive strength result obtained via the rock Schmidt hammer test, with R2 of 0.7058 and 0.7011, respectively. The results were verified with the weathering grades analyzed using image analysis technique. In conclusion, the CIELAB color space is an effective tool in the preliminary assessment of rock mass weathering, particularly for slightly vegetated rock slope outcrops.

The elastic modulus of rock masses is a critical parameter for many engineering applications. However, in situ measurement of the elastic modulus can be challenging and time-consuming. This study established a new in situ method to measure the elastic modulus of rock masses, enhancing prior technology using Hertz's theory. The method involves using a knocking ball, which strikes the rock surface with a spherical steel hammer, providing an instantaneous measurement. The knocking ball method was tested on a variety of rock types, and the results showed a significant correlation (R2 > 0.8) between the elastic modulus of the knocking ball (Ekb) and the in situ uniaxial compressive strength (UCS-Schmidt). The knocking ball method overcomes the limitations of the Schmidt hammer by offering rapid, direct, and multipoint measurements. The knocking ball method is non-destructive, handheld, and easy to use, making it ideal for use in field applications.

This study proposes a new measurement system based on Structure from Motion (SfM) photogrammetry to evaluate tunnel faces. The main goal is to determine the tunnel face discontinuity patterns and orientations for the tunnel face stability evaluation. A 3D point cloud model is generated from overlapping images of the tunnel face 3D polystyrene model through SfM photogrammetry. The discontinuity patterns and orientations are determined through facet extraction of the K-D tree plugin in CloudCompare. The same sets of overlapping images were analyzed in four different quality settings (low, medium, high, and ultra-high) with various sets of point cloud number that control the accuracy of the discontinuity's measurement. In this study, the high-quality setting shows the consistent measurement of the discontinuity patterns and orientations compared with the real 3D polystyrene tunnel face model. Validation of the pattern results from CloudCompare was carried out using JudGeo. Cluster analysis shows two sets of discontinuity with means of dip and dip direction of 82°/164° and 84°/307°, respectively. Both orientations were manually verified with a geological compass. The high-quality point cloud and manual compass measurement of the orientation highlight the similarity of the concentration for both discontinuity sets for the replica tunnel face. This tunnel face proposed method has the advantages of evaluating inaccessible areas and avoiding professional bias.

In drill and blasting tunneling method, a quantitative evaluation of geological conditions by drilling energy obtained from computer jumbos is being applied. In this study, a multivariate normal distribution model was developed to estimate geotechnical properties from drilling energy, and tunnel excavation analysis was conducted using the developed multivariate normal distribution model. The results confirmed that the confidence zone of the predicted values for both settlement and convergence covered the measured values. Furthermore, results of an attempt to update the multivariate normal distribution model using a data assimilation technique suggested the possibility of improving the prediction accuracy.

This study proposed a probabilistic generic transformation model between two rock mass properties, specific fracture energy, E v , and P-wave velocity, V P . To build the transformation model, 12 pairwise data sets of E v and V P were collected from six different construction sites involving construction of mountain tunnels in Japan. This database is labeled as “RockMass/2/350”. A probabilistic transformation model was built based on a bivariate standard normal distribution with these 350 data points. The model is generic, because it is based on a variety of sites. The performance of the constructed transformation model was evaluated through a cross-validation. It was found that 98.2% of the validation data fell within the computed 95% confidence interval of the model estimation, and this result provides a preliminary validation of the probabilistic transformation model. Unlike existing deterministic transformation models for estimating V P from E v , the proposed model can explicitly evaluate the transformation uncertainty with a quantitative metric such as a percentile. For practical application, a 3D model of the spatial distribution for Young’s modulus, E, was visualized based on the proposed transformation model. Since the proposed model is probabilistic, it can provide the spatial distribution for percentiles of E values. The constructed 3D model presented in this paper can be directly used as an input data for finite element or finite difference analysis, and probabilistic evaluation of excavation simulation is feasible based on the proposed probabilistic model. The quantitative information on such uncertainty can be useful in decision-making for tunnel constructions, such as selection of a cautious characteristic value.

The in-situ elastic modulus (E) is a vital parameter for describing rock strength in many engineering projects on rock slopes. The elastic modulus and uniaxial compressive strength (UCS) are typically investigated via laboratory tests using core samples. However, the direct determination of E is costly and time-consuming in preparing many intact samples from highly weathered Setul limestone. The knocking ball testing method is a non-destructive test that can quickly and easily obtain the elastic modulus of rock in-situ by striking the surface of a rock mass with a spherical steel hammer. This study presents the relationship between the elastic modulus of knocking ball (Ekb) and the uniaxial compressive strength of the Schmidt hammer (UCS-Schmidt). Results show that the regression coefficient correlation, R-2 is 0.851, indicating a positive trend with a few outliers. The measured Ekb were also verified with mineral properties and correlated to differential weathering grades to confirm the accuracy of measurement results. The finding compared to previous similar studies tested on several types of rock show a statistically significant. This research highlights the effectiveness of the knocking ball method for determining the modulus of rock slope at different weathering grades. A high elastic modulus corresponds to a high uniaxial compressive strength, verified by the laboratory test. This study shows that the knocking ball can be useful for predicting in-situ elastic modulus.

Weathering degree is one of the key criteria used to determine tunnel support with the main difficulty is in assessing the weathering grade of the tunnel face. This study applied CIEL*a*b colour space and image analysis to a rock mass tunnel face. The authors used the Japanese Highway (JH) Rock Mass Classification System to classify the rock mass tunnel face. JudGeo converted the RGB photographs to L*a*b values. This study compared the quantitative evaluation system's performance to manual tunnel face mapping by geologists using six JH Rock Mass Classification samples (Class B, CI, CII, DI, DII, and E). The analysis showed good correlations between four (B; CI; CII; DI) of six rock mass classes. The tunnel face mapping showed two rock mass classes above grade (DII and E). This technique can effectively identify the degree of weathering grades of the tunnel face, especially for fresh to moderately weathered tunnel faces.

Crack is a common condition that affects water-rich loess tunnels, and it negatively influences the reliability and safety of the tunnel. Jointed rock masses are commonly found during underground excavation. Many under-ground openings have failed during excavation and operation. The evaluation of tunnel faces by using a new measurement system based on structure from motion (SfM) photogrammetry is proposed in this study. The main objective is to determine the discontinuity pattern and orientation of tunnel faces as an input for tunnel face stability evaluation. A set of overlapping images obtained from a tunnel face replica using 3D polystyrene and the SfM photogrammetry approach is utilized to generate a 3D point cloud model. The discontinuity pattern and orientation are determined via facet extraction of the KD-tree plugin in CloudCompare. The same set of over-lapping images is analyzed in four different quality settings (low, medium, high, and ultrahigh) with different sets of point cloud numbers that control the accuracy of the measurement of discontinuity. Results show that the high-quality setting presents a consistent measurement of the discontinuity pattern and orientation in contrast to the real 3D polystyrene tunnel face model. 2D pattern results from CloudCompare are validated using the fractal analysis method in an image analysis software, where the image is converted into a binary-one. Two sets of discontinuity are derived. The means of dip and dip direction from the cluster analysis are 82 degrees/164 degrees and 84 degrees/ 307 degrees, respectively. The orientations are verified through manual compass measurement. The orientations for manual compass measurement for Sets 1 and 2 are 86 degrees/151 degrees and 80 degrees/303 degrees, respectively. The discontinuities obtained from the high-quality setting of the point cloud and manual measurement of the orientation by using a geological compass highlight the similarity of the discontinuity plane in both discontinuity sets for the tunnel face replica. The proposed method for tunnel face evaluation has good judgment for tunnel support and the prevention of biased analyses by professionals.

We carried out comparative data acquisition of active and passive surface wave methods using distributed acoustic sensing (DAS) and three-component velocity meters (geophone) at a research field located in Richmond, California, U.S. Several different fiber optic sensor cables were deployed at several different depths in a trench of 100 m length. Three component geophones were deployed on the ground surface along the trench with 2 m spacing. A sledge hammer was used for active data acquisition. Seismic ambient noises were also recorded over several days for passive measurements. Active seismic data obtained from geophone was converted to strain time histories and quantitatively compared with DAS data. The strain time history obtained from DAS agreed very well with those obtained from the 2 Hz geophones. We compared dispersion curves of active and passive surface wave data obtained from DAS and geophones based on a multichannel analysis of surface waves (MASW) and a spatial autocorrelation (SPAC). Dispersion curves obtained from DAS are reasonably consistent with those obtained from the radial component of geophones in a frequency range between 4 and 35 Hz in active data and 2 and 20 Hz in passive data. Processing results of passive measurements demonstrated that a low sensitivity cable used for telecommunication, and a cable on the ground surface loosely covered with soil, provided consistent dispersion curves with geophones. The results imply that DAS can be applied to various types of geotechnical monitoring.