Schmidt Hammer Rebound Research Articles

Detection of corrosion effects on prestressed concrete bridge deck slabs from the champlain bridge through non‐destructive testing techniques

AbstractAs aging infrastructures raise public concerns, evaluating their performance is crucial for maintaining structural integrity, especially for corroding prestressed concrete members. These structures may experience substantial tendon cross‐sectional area loss before any visible deterioration becomes detectable. While various non‐destructive techniques (NDT) have proven effective in labs, correlating corrosion‐induced damage in field members remains a challenge. Establishing these correlations is key for understanding the overall performance of aging structural concrete elements and ensuring their continued safe operation through non‐invasive means. This paper investigates various NDTs on a concrete bridge deck, aiming to correlate results. Visual inspection, Schmidt rebound hammer, Ultrasonic Pulse Velocity (UPV), corrosion detection techniques, Ground Penetrating Radar (GPR), Ultrasonic Pulse Echo (UPE), and Impact Echo (IE) methods are evaluated for detecting concrete deck damage. Results show the methods' capabilities in detecting defects to a certain extent, highlighting their potential in assessing aging concrete infrastructures.

A NOVEL WAY TO CLASSIFY THE SLAKE DURABILITY INDEX OF ROCKS

Slaking, the disintegration of rocks upon exposure to moisture and air, poses significant challenges in engineering applications, such as in slope stability and embankment design. This study improves the slake durability index test by introducing several enhancements to better reflect the field conditions of rocks. A new test methodology is developed which includes a revised slake durability classification table, a novel Discard Method for calculating weight retention in the test drum, and an updated classification chart. The improved methodology considers parameters such as porosity and strength, evaluated through index, Schmidt hammer rebound and point load tests. The new classification table categorizes rocks into durability classes according to the field observations and laboratory test results. The Discard Method improves accuracy by eliminating broken pieces from the durability calculation, addressing the limitations in the suggested method. A Configured Discard Method equation further defines the durability estimation by combining porosity, strength and slake durability data. Field investigations and laboratory tests conducted with various rock types demonstrate that the new classification system provides more accurate and practical assessments of rock durability. This study highlights the importance of effective porosity and strength impact which enhance the reliability of slake durability evaluations, offering a revised classification framework that simplifies field assessments while maintaining the accuracy. Future research should focus on developing these methods and expanding their applicability to diverse geological contexts by including more material to test.

The Laser Scanner Technique: A Tool for Determining Shear Strength Parameters of Rock Mass Discontinuities

When evaluating the shear strength of rock mass discontinuities, certain challenges arise due to the difficulty in quantifying the roughness characteristics of surfaces and the strength of asperities. Recent research has focused on enhancing techniques for assessing these characteristics and exploring the application of laser scanning to aid in evaluating discontinuity features. The analysis of reflectivity values (I) obtained through a laser scanner survey presents an efficient method for assessing mechanical characteristics, such as joint compressive strength (JCS). Reflectivity measurements demonstrate correlations with Schmidt hammer rebound values (r). The laser scanner technique would enable the measurement of JCS without the direct application of the Schmidt hammer on rocks in areas where rebound values (r) measurements are unavailable. The use of a laser scanner allows for the acquisition of high-precision geometrical information concerning the 3D roughness and anisotropy of rock surfaces. In this study, an innovative technique was introduced that utilizes laser scanner data from six previous experimental surveys conducted on rock formations in Southern Italy. This technique facilitates the evaluation of roughness profiles, considering potential variations along kinematically admissible sliding directions, allowing for the estimation of the Joint Roughness Coefficient (JRC). This new methodology aids in evaluating the parameters of Barton’s equation to determine the strength characteristics of rock mass discontinuities.

Crack Assessment on a Residential Building Due to Peat Soil Settlement at Ayer Hitam, Muar

Frequent problems in structure that build on the peat soil is cracking. This is due to the settlement that occurred by the peat soil as it has low in bearing capacity. The crack occurred on the residential building have become the main concern as crack happened on building showed the earliest sign of degradation. The increasing number of visible cracks at residential building with varies in length and width has a doubtful taught either the residential building is safe or not to be used. Apart from cracking, the concrete strength of the residential building is a concerned as the crack occurred may reduce the concrete strength. A single-storey residential building at Kompleks Penghulu Mukim Ayer Hitam, Muar is used to perform field measurement with respect to the cracking and concrete strength. 25 number of visible cracks with different length and width has been detected while monitoring the building. The crack length and size were measured using ruler and measuring tape. For measuring the concrete strength, a non-destructive testing (NDT) was used using Schmidt Rebound Hammer and Ultrasonic Pulse Velocity (UPV). The total 15 point was tested on column and wall inside the residential house by using two modes of transmission for UPV testing which was semi-direct and indirect transmission. The result of both rebound number and UPV has been analyze using statistical evaluation. The result is then being compared with the previous researcher that using the same regression mode of linear and non-linear mode when doing the analysis. From the result, it is found that the regression model for rebound hammer is more reliable to be used as the regression coefficient value got is nearly to one. For overall cracking and concrete strength quality, it is also found that the residential building needs proper maintenance as the settlement still happened and it is affected the residential building’s safety.

Unconfined compressive strength prediction of rock materials based on machine learning

It is costly, time-consuming, and difficult to measure unconfined compressive strength (UCS) using typical laboratory procedures, particularly when dealing with weak, extremely porous, and fractured rock. By efficiently choosing the variables from a subset of the dataset that includes the Schmidt hammer rebound number (SRn), bulk density (BD), bulk tensile strength (BTS), dry density (DD) test, p-wave velocity test (Vp), and point load index test (Is(50)), this study seeks to establish predictive models for the UCS of rocks. A prediction model for UCS was prepared using K-nearest neighbor (KNN). KNN was preferred over machine learning algorithms because it is simple, versatile, and interpretable. It is particularly useful when it has limited training time, faces non-parametric data with changing distributions, or requires straightforward explanations for predictions. In order to improve KNN’s prediction performance in this research, two optimization procedures (namely, Alibaba and the Forty Thieves (AFT) and Improved Manta-Ray Foraging Optimizer (IMRFO)) were used. Through comparison of KNN single modal performance with that of optimized versions, it is concluded that the KNIM (KNN model optimized with IMRFO) is an excellent possible applicant for the forecast of the UCS of rocks. This study’s results showed that the KNIM model is more suitable than the KNN single model and its counterpart KNAF in terms of accuracy as its correlation of determination (R2) values were 1.1% and 2% higher than KNN and KNAF and its root mean squared error (RMSE) values were 37.9% and 43.7% lower than KNN and KNAF. The improvement in R2 and RMSE values for the KNIM model compared to KNN and KNAF is highly significant for the reliability and accuracy of the predictive model. R2, measuring the proportion of variance predictable in the dependent variable (UCS of rocks) from the independent variables (model predictions), signifies a better fit to observed data. The elevated R2 values for KNIM indicate a stronger correlation with actual UCS values, enhancing the model’s accuracy in representing underlying patterns. Additionally, the reduction in RMSE values for KNIM implies that its predictions are, on average, closer to actual UCS values, contributing to a more accurate and reliable estimation of rock strength.

Bayesian optimization-enhanced ensemble learning for the uniaxial compressive strength prediction of natural rock and its application

Engineering disasters, such as rockburst and collapse, are closely related to structural instability caused by insufficient bearing capacity of geological materials. Uniaxial compressive strength (UCS) holds considerable significance in rock engineering projects. Consequently, this study endeavors to devise efficient models for the expeditious and economical estimation of UCS. Using a dataset of 729 samples, including the Schmidt hammer rebound number, P-wave velocity, and point load index data, we evaluated six algorithms, namely Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Extra Trees (ET) and utilized Bayesian Optimization (BO) to optimize the aforementioned algorithms. Moreover, we applied model evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Variance Accounted For (VAF), Nash-Sutcliffe Efficiency (NSE), Weighted Mean Absolute Percentage Error (WMAPE), Coefficient of Correlation (R), and Coefficient of Determination (R2). Among the six models, BO-ET emerged as the most optimal performer during training (RMSE ​= ​4.5042, MAE ​= ​3.2328, VAF ​= ​0.9898, NSE ​= ​0.9898, WMAPE ​= ​0.0538, R ​= ​0.9955, R2 ​= ​0.9898) and testing (RMSE ​= ​4.8234, MAE ​= ​3.9737, VAF ​= ​0.9881, NSE ​= ​0.9875, WMAPE ​= ​0.2515, R ​= ​0.9940, R2 ​= ​0.9875) phases. Additionally, we conducted a systematic comparison between ensemble and traditional single machine learning models such as decision tree, support vector machine, and K-Nearest Neighbors, thus highlighting the advantages of ensemble learning. Furthermore, the enhancement effect of BO on generalization performance was assessed. Finally, a BO-ET-based Graphical User Interface (GUI) system was developed and validated in a Tunnel Boring Machine-excavated tunnel.

Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data

The unconfined compressive strength (UCS) of intact rocks is crucial for engineering applications, but traditional laboratory testing is often impractical, especially for historic buildings lacking sufficient core samples. Non-destructive tests like the Schmidt hammer rebound number and compressional wave velocity offer solutions, but correlating these with UCS requires complex mathematical models. This paper introduces a novel approach using an artificial neural network (ANN) to simultaneously correlate UCS with three non-destructive test indexes: Schmidt hammer rebound number, compressional wave velocity, and open-effective porosity. The proposed ANN model outperforms existing methods, providing accurate UCS predictions for various rock types. Contour maps generated from the model offer practical tools for geotechnical and geological engineers, facilitating decision-making in the field and enhancing educational resources. This integrated approach promises to streamline UCS estimation, improving efficiency and accuracy in engineering assessments of intact rock materials.

Enhancing geochemical background estimation using rock weathering

The chemical composition of stream sediments is heavily influenced by weathering processes occurring in upstream lithological units. Despite their importance, these processes are frequently ignored in the examination of stream sediment geochemical data when identifying mineralized zones. Instead, existing geochemical background estimation procedures rely on the area size of upstream rocks as an indicator of their contribution to elemental concentrations of sediments. To address this limitation, a stream sediment geochemical survey was conducted in a pilot area in central Iran, and the physico-mechanical properties of upstream rock types, including elastic velocities, porosity, and Schmidt hammer rebound values, were evaluated. A new weathering index, “Wij,” was developed, utilizing proxies of rock weathering to estimate the geochemical background attributed to lithology at sediment sampling sites. The regression model was constructed by combining weathering proxies (Wij) with other independent variables, including the presence or absence of rocks in catchments, area size (Xij), and the weathering-corrected contributing factor (XijWij). The results suggested the significant capability of weathering proxies in explaining the geochemical variability of major elements such as Al, K, Fe, Ca, and Na. However, the regression model using weathering-corrected contributing factors delivered better overall accuracy than other independent variables. Nevertheless, our results also identified some limitations of the application of regression model in stream sediment data analysis.

Damage to monumental masonry buildings in Hatay and Osmaniye following the 2023 Turkey earthquake sequence: The role of wall geometry, construction quality, and material properties

Damage to monumental masonry buildings in Hatay and Osmaniye following the 2023 Turkey earthquake sequence: The role of wall geometry, construction quality, and material properties

Comparative study on convolutional neural network and regression analysis to evaluate uniaxial compressive strength of Sandy Dolomite

Sandy Dolomite is a kind of widely distributed rock. The uniaxial compressive strength (UCS) of Sandy Dolomite is an important metric in the application in civil engineering, geotechnical engineering, and underground engineering. Direct measurement of UCS is costly, time-consuming, and even infeasible in some cases. To address this problem, we establish an indirect measuring method based on the convolutional neural network (CNN) and regression analysis (RA). The new method is straightforward and effective for UCS prediction, and has significant practical implications. To evaluate the performance of the new method, 158 dolomite samples of different sandification grades are collected for testing their UCS along and near the Yuxi section of the Central Yunnan Water Diversion (CYWD) Project in Yunnan Province, Southwest of China. Two regression equations with high correlation coefficients are established according to the RA results, to predict the UCS of Sandy Dolomites. Moreover, the minimum thickness of Sandy Dolomite was determined by the Schmidt hammer rebound test. Results show that CNN outperforms RA in terms of prediction the precision of Sandy Dolomite UCS. In addition, CNN can effectively deal with uncertainty in test results, making it one of the most effective tools for predicting the UCS of Sandy Dolomite.

Prediction of rock fragmentation in a fiery seam of an open-pit coal mine in India

Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes. This condition, known as hot-hole blasting, is dangerous due to the increased possibility of premature explosions in loaded blastholes. Thus, it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of blastholes. Additionally, it will help achieve the desired fragment size. This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam. Data on blast design, rock mass, and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational techniques. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), variance account for (VAF) and coefficient of efficiency in percentage (CE) were calculated to validate the results. It indicates that the random forest algorithm (RFA) outperforms the artificial neural network (ANN), response surface method (RSM), and decision tree (DT). The values of R2, RMSE, MAE, MSE, VAF, and CE for RFA are 0.94, 0.034, 0.027, 0.001, 93.58, and 93.01, respectively. Multiple parametric sensitivity analyses (MPSAs) of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment size. The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting. The optimum factor values for RFA of S/B, ld/B and ls/ld are 1.03, 1.85 and 0.7, respectively.

Comprehensive study on the Python-based regression machine learning models for prediction of uniaxial compressive strength using multiple parameters in Charnockite rocks

The strength of rock under uniaxial compression, commonly known as Uniaxial Compressive Strength (UCS), plays a crucial role in various geomechanical applications such as designing foundations, mining projects, slopes in rocks, tunnel construction, and rock characterization. However, sampling and preparation can become challenging in some rocks, making it difficult to determine the UCS of the rocks directly. Therefore, indirect approaches are widely used for estimating UCS. This study presents two Machine Learning Models, Simple Linear Regression and Step-wise Regression, implemented in Python to calculate the UCS of Charnockite rocks. The models consider Ultrasonic Pulse Velocity (UPV), Schmidt Hammer Rebound Number (N), Brazilian Tensile Strength (BTS), and Point Load Index (PLI) as factors for forecasting the UCS of Charnockite samples. Three regression metrics, including Coefficient of Regression (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were used to evaluate and compare the performance of the models. The results indicate a high predictive capability of both models. Notably, the Step-wise model achieved a testing R2 of 0.99 and a training R2 of 0.988 for predicting Charnockite strength, making it the most accurate model. The analysis of the influential factors indicates that UPV plays a significant role in predicting the UCS of Charnockite.

Complex erosional response to uplift and rock strength contrasts in transient river systems crossing an active normal fault revealed by 10Be and 26Al cosmogenic nuclide analyses

AbstractUnderstanding the influence of bedrock lithology on the catchment‐averaged erosion rates of normal fault‐bounded catchments and the effect that different bedrock erodibilties have on the evolution of transient fluvial geomorphology remain major challenges. To investigate this problem, we collected 18 samples for 10Be and 26Al cosmogenic nuclide analysis to determine catchment‐averaged erosion rates along the well‐constrained Gediz Fault system in western Türkiye, which is experiencing fault‐driven river incision owing to a linkage event ~0.8 Ma and has weak rocks overlying strong rocks in the footwall. Combined with existing cosmogenic data, we show that the background rate of erosion of the pre‐incision landscape can be constrained as <92 mMyr−1, and erosion rates within the transient reach vary from 16 to 1330 mMyr−1. Erosion rates weakly scale with unit stream power, steepness index and slip rate on the bounding fault, although erosion rates are an order of magnitude lower than slip rates. However, there are no clear relationships between erosion rate and relief or catchment slope. Bedrock strength is assessed using Schmidt hammer rebound and Selby Rock Mass Strength Assessments; despite a 30‐fold difference in erodibility, there is no difference in the erosion rate between strong and weak rocks. We argue that, for the Gediz Graben, the strong lithological contrast affects the ability of the river to erode the bed, resulting in a complex erosional response to uplift along the graben boundary fault. Weak covariant trends between erosion rates and various topographic factors potentially result from incomplete sediment mixing or pre‐existing topographic inheritance. These findings indicate that the erosional response to uplift along an active normal fault is a complex response to multiple drivers that vary spatially and temporally.

Comparatively Study of Non-Destructive test with different methods in various curing days

In non-destructive testing, materials, components, or assemblies are inspected, tested, or evaluated without being destroyed. In nondestructive testing, materials, components, and assemblies are evaluated for quality and integrity without damaging them. The use of non-destructive testing (NDT) is a popular method for assessing the strength and durability of existing concrete structures. An ultrasound pulse velocity test and a rebound hammer test are included in the NDT test. An NDT test includes both ultrasound pulse velocity testing and rebound hammer testing. Strength tests are conducted with rebound hammers, while quality tests are conducted with ultrasonic pulse velocity tests. A lab-made concrete cube was tested on three different ages-seven, fourteen, and 28 days-without destroying it. There were 15 cubes that were treated using non-destructive methods. Using both Schmidt rebound hammer and ultrasonic pulse velocity tests, we determined which non-destructive testing method was faster. In addition to surfaces, Schmidt rebound hammers are the toughest tools to use. Rebound number and concrete strength seem to be connected. Schmidt hammers were employed in both vertical and horizontal settings. Concrete’s ultrasonic pulse velocity is mostly determined by its identity and modulus of elasticity, that are motivated by the substances used in making the concrete in addition to its placement, compaction, and curing techniques.

Compressive Strength Prediction Model of High Strength Concrete by Destructive and Nondestructive Technique

Concrete’s compressive strength can be tested in a laboratory before construction begins. Since concrete is a natural material and cannot be destroyed, it is not possible to determine its compressive strength through destructive testing. Rebound hammers are typically used in the field to evaluate the structural elements’ ability to withstand hardened concrete. As part of the current study, a comparison was made between concrete’s compressive strength measured by destructive testing and its surface hardness measured by rebound hammering. Tests were conducted on laboratory-made concrete cubes in this study to determine destructive and non-destructive behavior. Minitab software was used for regression analysis. Schmidt rebound hammer tests, a type of nondestructive testing (NDT), were shown to have very strong relationships with concrete destructive compression tests. Schmidt rebound hammers are commonly used to measure the surface hardness of concrete, since the hammer rebound number and concrete strength are theoretically correlated. Utilising a Schmidt hammer, it was applied. Standard concrete cubes with crushing strengths between 20 and 30 MPa were created using various mix proportions. Using regression analysis, destructive and non-destructive values are correlated. The linear regression equation is well suited for obtaining the compressive strength using rebound value by using linear regression equation.

Engineering Characterization of Rocks from Selected Quarries in Abuja, Nigeria.

The study aims to understand the strength, durability, and suitability of Abuja rock for planning, design, and construction of engineering projects. Three quarries in Abuja (Kubwa, Mpape, and Lugbe) were selected, and four samples were observed to be the most dominant, covering 90%-95% of the rock used in manufacturing aggregates for engineering construction. Geotechnical engineering properties of these rocks were analyzed in an engineering laboratory. Experimental tests included uniaxial compression test, Schmidt hammer rebound test, Los Angeles Abrasion test, Water absorption/moisture content test, and Specific gravity test. The compressive strength ranges from 21.32Mpa to 28.91Mpa, classified as sedimentary rock with moderate hardness. Schmidt rebound numbers range from 21.94 to 35.28, providing immediate access to rock strength. This shows on correlation with R²= 0.409 means the uniaxial compressive strength value is nearly proportional to the Schmidt rebound number. The specific gravity and water absorption relationship showed a negative correlation, with values of 2.43, 2.32, 2.32, and 2.28 on specific gravity and 0.15%, 0.22%, 0.20%, 0.17% on water absorption/water content from porphyritic granite, porphyroblastic gneiss, biotite granite, and granite gneiss, respectively. It shows in a correlation that water content is inversely proportional to the specific gravity with value of R²=0.400. Los Angeles abrasion values were within the allowable range (20-30) which reveals values from 21.00 to 23.00. The study reveals that the engineering characteristics of the sampled rocks are within the standard range for construction purposes. Porphyritic gneiss and granite gneiss are suitable for building and road construction due to their higher compressive strength and low water absorption, providing good strength and durability under different prevailing environmental conditions.

Mechanical properties of ignimbrites of Tenerife Island employed as building stone and their correlation with some physical properties

The aim of this research work was to determine and analyse the mechanical properties of a group of four varieties of ignimbrites, which are differentiated according to their colour: brown, violet, grey and green. These ignimbrites are mined in the southern part of Tenerife (Canary Islands) and are widely used as building stone. Mechanical characterisation was carried out by determining: ultrasound propagation velocity, uniaxial compressive strength, flexural strength under concentrated load, point load strength, Schmidt hammer rebound number and rupture energy. To evaluate the influence of the anisotropy planes and the degree of correlation between the different directions, all tests were done in more than one direction, except for the rupture energy. A simple regression analysis incorporating the physical properties available in the literature is included, with special emphasis on those with the highest correlation coefficient. Finally, a multiple regression analysis is performed to find the best fitting equations to predict the compressive strength. Of the four varieties studied, the green ignimbrite proved to be the one with the highest mechanical strength, with some properties three times higher than those obtained for the brown ignimbrite and almost twice as high as those obtained for the violet ignimbrite and the grey ignimbrite, the latter varieties having a similar behaviour to each other. In most cases, the behaviour of the ignimbrites studied is consistent with their lithology, but some properties of the green ignimbrite exceed the range reported in the literature, thus providing a new reference for this lithotype. Simple regression analysis revealed strong correlations between uniaxial compressive strength and water absorption (r = −0.89) and point load strength (r = 0.85). Flexural strength showed very strong correlation with ultrasonic velocity (r = 0.91) and point load strength (r = 0.95). Applying the multiple regression method, the best estimators of compressive strength found were a model with four independent variables; apparent density, open porosity, water absorption and Schmidt hammer rebound index (R2 = 0.91) and a model with five variables, adding point load resistance (R2 = 0.93).

Study of the Physical Properties and Compressive Strength of Karst Rocks in the Leang Londrong Cave Area Bantimurung Bulusaraung National Park

Research has been carried out on the physical properties and compressive strength of karst rocks in the Leang Londrong cave area. The purpose of this research was to analyzr the value of physical properties and compressive strength of rocks including density and porosity and also calssify limestone types based on the value of physical properties and compressive strength. The compressive strength value obtained from a non-destructive method, namely by using a Schmidt Hammer. The process starts from taking 30 rock samples in the Leang Lonrong cave area at 10 predetermined points. Based on the physical properties test of karst rocks in the Leang Londrong cave area, the rock density values are in the range of 2,60 Â to 2,62 , while the porosity value are in the range 0-6 %. So that the type of rock in the Leang Londrong cave area is dolomite limestone. The compressive strength value of the rock is obtained from the conversion of the Schmidt Hammer rebound value. The value of the compressive strength of karst rocks is obtained in the range of values fro, 20 MPa to 62 MPa. So that the rocks in the Leang Londrong cave area are quite hard or medium types of rock.

Building a bimodal landscape: bedrock lithology and bed thickness controls on the morphology of Last Chance Canyon, New Mexico, USA

Abstract. We explore how rock properties and channel morphology vary with rock type in Last Chance Canyon, Guadalupe Mountains, New Mexico, USA. The rocks here are composed of horizontally to near-horizontally interbedded carbonate and sandstone. This study focuses on first- and second-order channel sections, where the streams have a lower channel steepness index (ksn) upstream and transition to higher ksn values downstream. We hypothesize that differences in bed thickness and rock strength influence ksn values, both locally by influencing bulk bedrock strength and also nonlocally through the production of coarse sediment. We collected discontinuity intensity data (the length of bedding planes and fractures per unit area), Schmidt hammer rebound measurements, and measured the largest boulder at every 12.2 m elevation contour to test this hypothesis. Bedrock and boulder mineralogy were determined using a lab-based carbonate dissolution method. High-resolution orthomosaics and digital surface models (DSMs) were generated from drone and ground-based photogrammetry. The orthomosaics were used to map channel sections with exposed bedrock. The United States Geological Survey (USGS) 10 m digital elevation models (DEMs) were used to measure channel slope and hillslope relief. We find that discontinuity intensity is negatively correlated with Schmidt hammer rebound values in sandstone bedrock. Channel steepness tends to be higher where reaches are primarily incising through more thickly bedded carbonate bedrock and lower where more thinly bedded sandstone is exposed. Bedrock properties also influence channel morphology indirectly, through coarse sediment input from adjacent hillslopes. Thickly bedded rock layers on hillslopes erode to contribute larger colluvial sediment to adjacent channels, and these reaches have higher ksn values. Larger and more competent carbonate sediment armors both the carbonate and the more erodible sandstone and reduces steepness contrasts across rock types. We interpret that in the relatively steep, high-level ksn downstream channel sections, the slope is primarily controlled by the coarse alluvial cover. We further posit that the upstream low-level ksn reaches have a base level that is fixed by the steep downstream reaches, resulting in a stable configuration, where channel slopes have adjusted to lithologic differences and/or sediment armor.

Weathering Effects on Engineering Geological Properties of Trachydacitic Volcanic Rocks from the Monte Amiata (Southern Tuscany, Italy)

Variability in lithology and weathering degree affects physical and mechanical properties of rocks. In this study, we investigated the relationships between weathering degree and engineering geological properties of trachydacitic volcanic rocks from Monte Amiata (central Italy) by coupling field and laboratory analyses. We collected in situ Schmidt hammer tests in the field. We evaluated weathering quantifying the percentage of secondary minerals through thermal analysis in the laboratory. We also determined dry density (ρd), specific gravity of solids (Gs), porosity (n) and two-dimensional (2D) porosity as resulted from scanning electron microscopy investigations. The results of our study indicate a negative linear correlation between Schmidt hammer rebound values and secondary mineral percentage. This correlation provides a tool to quantitatively estimate the deterioration of rock uniaxial compressive strength (UCS) as weathering increases. Moreover, thermal analysis turned out to be a quantitative and reproducible method to evaluate weathering degree of magmatic rocks.