Probabilistic prediction of intact rock strength using point load tests using a Bayesian formulation

This research paper is aimed to briefly highlight the correlation between unconfined compressive strength and point load index for jointed rocks based on joint asperity & orientation. In this observe, specimens were tested to obtain their unconfined compression strength and point load index for a different joint condition. The different joint conditions considered for this study were clean joint and joint filled condition. For both clean joint and joint filled specimens were prepared by various asperity angles of 30°, 45°, 60° and 90° with different orientation angles such as 0°, 30°, 45°, 60°, 90°. Plaster of Paris was used as model material to simulate weak rock mass in the field. By testing intact model specimens for unconfined compressive strength leads to revealing of optimum moisture content for further testing. The curing period for the model specimens is 3 days at room temperature. To simulate jointed rocks, various moulds of different orientation of joint with respect to major principal stress are prepared separately. The inner diameter of the mould is 50 mm and height is 100 mm. After casting, a rough joint was created by cutting the prepared sample using the cutter. The specimens are tested for both clean joint and joint filled condition to determine the favorable joint orientation and asperity angle. After curing, the specimens are tested for unconfined compressive strength and Point load index. The new multi-linear correlation for determining unconfined compressive strength with the help of point load index is developed and cross checked with equations formed for actual rock. On comparing both results it is found that the new equation can suitable for assessing the unconfined compressive strength of limestone and serpentinite rocks through point load index value.

In evaluation of engineering behavior of rock mass and rock materials, slaking of rocks is an important consideration. For the construction industry, a durable rock is usually preferred. About 75% of the rocks outcropping on continents are sedimentary rocks. To determine rock strength and deformation, direct tests such as uniaxial compressive strength are expensive and require considerable time. Hence there is need to explore relations through other indirect methods such as Slake Durability Index, Point Load Strength and Schmidt rebound hammer test. To investigate the correlation between Slake Durability and strength, multidisciplinary approach was adopted. For this study, one of the important industrial rock groups belonging to carbonate geology of Salt Range was selected. The Slake Durability Index test was performed on 32 rock samples collected from different parts of Salt Range and the test results were compared with indirect strength such as Point Load Strength and Schmidt Hammer Hardness. Data was statistically analyzed through linear regression analysis to determine the correlation coefficient and the variability of results for each test. A strong linear correlation of 1st cycle Slake Durability Index exists with Point Load Strength and Schmidt Hammer Hardness.

Correlation of soil parameters has undeniable benefit in the determination of engineering properties of soil to solve problems in geotechnical Engineering area. The tests were conducted within geotechnical laboratory. These tested soil parameters, used in the correlation analysis are unconfined compressive strength, bulk unit weight and dry unit weight. The aim of this study is proposing a relationship in between the strength parameter with some of the index properties of soils using statistical regression analysis. The linear regression analyses have been done for prediction of unconfined compressive strength (qu ) from bulk and dry unit weight as model-1 and model-2 respectively. And dry unit weight was predicted from bulk unit weight as model-3. Model-4 represents the multiple linear regression analysis to predict qu . The health of developed models is measured by coefficient of determination (R 2) values. Though, model-1, model-2, model-3 and model-4 have R – squared values of 0.9112, 0.9333, 0.9109 and 0.9452 respectively. Therefore, they are correlated strongly and positively. The prediction of unconfined compressive strength of these soils correlated in linear regression, are fairly determined with Model-2 compared with model-1 and model-4 (MLR).

Stabilization of sand by the deep mixing method increases its unconfined compressive strength (UCS) so that it can be used as a foundation for infrastructure. This paper presents research work on physicomechanical properties of soil–cement columns installed in the central area of Vietnam, which consists of sandy clay, fine sand, medium sand, and coarse sand. Two types of cement were mixed with these soils in amounts of 150–350 kg/m3 at a water-cement ratio of 0.6. Approximately 80 soil–cement samples were prepared and cured at standard moist conditions and then tested for unconfined compressive strengths and flexural strengths at 7, 14, 28, and 56 days. The maximum unconfined compressive strength of the soil–cement was 10 MPa after 56-day curing for the coarse sand mixed with 350 kg/m3 cement. The Bayesian Model Averaging and the Principal Component Analysis Methods were used to evaluate the influence of different variables on the UCS of the cement-stabilized soil. The analysis shows that the UCS of the soil–cement depended on the contents of SiO2, SO3, K2O in the soil, the soil grain size, the cement content, and the curing time. The prediction equations allow the determination of unconfined compressive strength and flexural strength of cement-stabilized sand by the wet mixing method used as a ground improvement method for foundations.

In this paper, stress-strain behaviour of sand-clay mixture stabilised with different cement and rice husk ash percentages, and reinforced with different polypropylene fibre lengths are evaluated. Mixtures are widely used in road construction for soil stabilisation. It is observed that replacing half of the cement percentage (in high cement contents) with rice husk ash will result in a higher unconfined compressive strength. In addition, the presence of 6 mm polypropylene fibres will help to increase the unconfined compressive strength of stabilised samples, while larger fibres cause reverse behaviour. In addition, introducing a new index for assessing the effect of curing days. Curing Improvement Index it is obtained that larger fibres show higher Curing Improvement Index values. Results gained for the effects of curing days, and fibre lengths are further discussed and interpreted using Scanning Electron Microscopy photos. Based on the conducted Unconfined Compressive Strength, Indirect Tensile Strength, and Flexural Strength tests and using evolutionary polynomial regression modelling, some simple relations for prediction of unconfined compressive strength, indirect tensile strength, and flexural strength of cement-rice husk ash stabilised, and fibre reinforced samples are presented. High coefficients of determination of developed equations with experimental data show the accuracy of proposed relationships. Moreover, using a sensitivity analysis based on Cosine Amplitude Method, cement percentage and the length of polypropylene fibres used to reinforce the stabilised samples are respectively reported as the most and the least effective parameters on the unconfined compressive strength of specimens.

The accurate prediction of unconfined compressive strength (UCS) in rock samples is critical for the successful planning, design, and implementation of mining and civil engineering projects. UCS is crucial in assessing the stability and durability of rock masses, which directly influences the safety, efficiency, and cost-effectiveness of construction and excavation operations. Here’s a refined version of your text for enhanced clarity and flow: in this part, the execution of the proposed model was compared for both single and hybrid configurations. Hybrid models included support vector regression (SVR) combined with the Seahorse Optimizer (SVSH) and SVR combined with the COOT optimization algorithm (SVCO). For training, 70% of the UCS dataset was utilized, while the remaining 30% was equally divided between testing (15%) and validation (15%). For the model evaluation, several metrics were considered in this work, including the R2, RMSE, WAPE, MAE, and RAE, which ensure fairness in the analysis. The closer the R2 value comes to 1, the better the performance. The error metrics should be close to 0 for better accuracy. From Table 2, one can observe that the result of the standalone SVR model gave an RMSE of 6.213 during training and 9.454 during testing, hence showing poor performance. However, the inclusion of optimization algorithms significantly improved the performance of the SVR framework. Among the hybrid models, the SVSH model had the best performance, with an R2 value of 0.998 and an RMSE of 1.261 during training. The SVCO model performed moderately, with an R2 value of 0.988 during training.

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.

본 연구에서는 생성 당시의 퇴적환경이 상이한 것으로 알려진 대구지역의 적색 셰일과 흑색 셰일의 물리적 및 역학적 특성을 확인하기 위하여 단위중량, 흡수율, 공극률, 초음파속도, 일축압축강도, 점하중강도, 슬레이크 내구성 및 열화특성 시험 등을 실시하였고, XRD 및 XRF 분석과 SEM 영상 분석을 수행하였다. 시험결과 두 셰일의 단위중량은 유사하였으나 흡수율과 공극률은 적색 셰일이 흑색 셰일보다 더 높았다. 특이하게도 공극률이 높은 적색 셰일의 초음파속도, 일축압축강도, 점하중강도지수 가 흑색보다 더 높은 값을 보였는데 이는 흑색 셰일 내에 발달된 엽층리의 영향인 것으로 판단된다. 열화시험에서는 용액의 산성도가 증가할수록 또 수침시간이 길어질수록 시료의 점하중강도지수와 슬레이크 내구성지수의 저감정도가 심하였으며, 흑색 셰일보다는 공극률이 큰 적색 셰일에서 더 크게 영향을 받는 것으로 나타났다. The physical and mechanical properties of red shale and black shale exposed in the Daegu area were investigated in tests conducted to determine unit weight, absorption ratio, porosity, ultrasonic velocity, unconfined compressive strength, point load strength, slake durability index, and deterioration characteristics. XRD, XRF, and SEM analyses were also performed on the shale specimens. While the unit weights of the two shales were similar, the absorption ratio and porosity were higher in the red shale than in the black shale. Despite the higher porosity of the red shale, the ultrasonic velocity, compressive strength, and point load strength were higher in the red shale, which is an unexpected result that may be due to the presence of fine laminations in the black shale. The deterioration rate, as determined from the point load strength and the slake durability index, increased with increasing immersion time and with the acidity of the immersion liquid. The deterioration rate was higher for the red shale than for the black shale because of the higher porosity of the former.

A reasonable knowledge of rock's physical and mechanical properties could save the cost of drilling and production of a reservoir to a large extent by selection of proper operating parameters. In addition, a master development plan (MDP) for each oilfield may contain many enhanced oil recovery procedures that take advantage of rock mechanical data and principles. Thus, an integrated rock mechanical study can be considered an investment in field development. The unconfined compressive strength (UCS) of rocks is the important rock mechanical parameter and plays a crucial role when drilling an oil or gas well. A drilling operation is an interaction between the rock and the bit and the rock will fail when the resultant stress is greater than the rock strength. UCS is actually the stress level at which rock is broken down when it is under a uniaxial stress. It can be used for bit selection, real-time wellbore stability analysis, estimation an optimized time for pulling up the bit, design of enhanced oil recovery (EOR) procedures, and reservoir subsidence studies. Rock strength can be estimated along a drilled wellbore using different approaches, including laboratory tests, core–log relationships, and penetration model approaches. Although this rock strength profile can be used for future investigation of formations around the wellbore, they are actually dead information. Dead rock strength data may not be useful for designing a well in a blind location (infill drilling). Rock strength should be predicted prior to drilling operations. These sort of data are helpful in proposing a drilling program for a new well. In this research, new equations for estimation of rock strength in Ahwaz oilfield are formulated based on statistical analysis. Then, they are utilized for estimation of the rock strength profile of 36 wells in a Middle Eastern oilfield. An artificial neural network is then utilized for prediction of UCS in any predefined well trajectory. Cross-validation tests showed that the results of the network were compatible with reality. This approach has proven to be useful for estimation of any designed well trajectory prior to drilling.

In this paper, the needle penetrometer test was utilized to explore the reliability of the Needle Penetration Index (NPI) for estimating the Unconfined Compressive Strength (UCS) of sedimentary rocks including gypsum, marl, siltstone and sandstone collected from the Qom Formation. Following the UCS and NP test, regression analyses were carried out to control the predictive performances of NPI. Statistic performance indices such as determination coefficient (R2) the Root Mean Square Error (RMSE) and Variance Account For (VAF) are calculated. Regression analyses suggest meaningful relationships between UCS and NPI, for gypsum, marl, siltstone and all rock types as whole. The results are reasonably meaningful, excluding for sandstone which NPI does match with values of UCS prediction. As NPI could only penetrate to a maximum of 10 mm depth, it is not representative of intrinsic properties (e.g. texture and mineralogy) that control their UCS. Cross-plot of UCS/NPI vs. UCS and regression analysis was carried out to overcome this uncertainty. Results obtained from this purposed approach suggest that it is more reliable than those achieved from UCS vs. NPI. The derived equations are in good corresponding with those suggested by other researchers. Additionally, control data test was applied to make sure their validation.

Durability study of a solidified mercury-containing sludge

Hydrothermal alteration, which alters the chemical composition and physical state of volcanic rocks, can weaken a volcanic edifice, potentially leading to instability and collapse, thereby endangering the livelihoods of neighbouring residents. Instability scenarios can be modelled using large-scale numerical models, the accuracy of which depends on acquiring the physical and mechanical properties of volcanic rocks from laboratory measurements. Routinely, laboratory studies provide measurements for small sample suites (< 10) and, as a result, we do not fully grasp the range of rock properties that describe a particular unit or volume of the volcano, nor is it clear whether the few samples collected are representative. Here, we introduce a method that can help us select the most appropriate value, or range of values, for the physical and mechanical properties of volcanic rocks for large-scale numerical models. We collected nearly 550 rocks from seven different sampling sites at La Soufrière de Guadeloupe, an active andesitic stratovolcano in the Eastern Caribbean. The rocks were assigned an alteration grade index, from 1 (least altered) to 5 (most altered), based on a visual assessment of their alteration. Their bulk densities were then measured in the field using the Archimedes method and, lastly, their strengths were measured using a point load tester, a field strength measuring apparatus. Alteration grade index ranges from 1 to 5 , bulk densities range from less than 1000 to 2700 kg/m3, and point load strength values span from 0.012 to 8.53 MPa . Point load strength and alteration distribution maps for the seven sampling locations show not only that rock physical properties vary greatly at an individual location, but also that the distributions of alteration and strength vary from place to place, highlighting the large heterogeneity of the dome at La Soufrière de Guadeloupe. To calibrate these field data, we took small samples of the tested rocks back to the laboratory for porosity and uniaxial compressive strength measurements. Porosity measurements, ranging from 0.02 to 0.8, exhibited a strong correlation with field density values. The extremely altered rocks, deemed alteration grade index 5, despite having porosities ranging from 0.11 to 0.8, did not exceed point load strength of 2 MPa, suggesting that strength was not solely dependent on porosity. We also found that strength decreases as a function of increasing porosity and alteration grade index. Although uniaxial compressive strength tests revealed synchronicity with point load strength data, our current goal is to perform further tests to better constrain this relationship. The point load test is a field method that can simplify the logistic problems behind strength assessments of volcanic domes, and likewise enlarge the sampling suite of individual studies. Calibrating field results to uniaxial compressive strength laboratory data will allow our data to be used in volcano stability models and accommodate direct conversion, from point load values to uniaxial compressive strength values, on-site. The potential abundance of available data, and spatial strength distributions, could make the large-scale models more realistic, and consequently more accurate and reliable.

Unconfined compressive strength (UCS) is a major mechanical parameter of the rock which has an essential role in developing geomechanical models. It can be estimated directly by lab testing of retrieved core samples or from well log data. These methods are very expensive and require huge efforts and time. Therefore, there is a need to develop a new technique for predicting UCS values in real-time. In this study, three artificial intelligence (AI) models were developed using artificial intelligence tools; artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) to predict UCS of the downhole formations while drilling based on real-time recording of the drilling mechanical parameters. These parameters include rate of penetration (ROP), mud pumping rate (GPM), stand-pipe pressure (SPP), rotary speed in revolution per minute (RPM), torque (T), and weight on bit (WOB). A dataset of 1771 points from a Middle Eastern field was used to build the developed models: for training and testing processes. A new UCS correlation was developed based on the optimized AI model. Another set of data (2175 data points unseen by the model) was used to validate the model and the developed UCS correlation. The developed ANN-model outperformed the ANFIS- and SVM-models with a correlation coefficient (R-value) of 0.99 and an average absolute percentage error (AAPE) of 3.48% between the predicted and actual UCS values. The new UCS correlation outperformed the available correlations for UCS prediction and it was able to predict the UCS with AAPE of 4.2% compared to the actual UCS values.

Point load tests and strength measurements for brittle spheres

This study undertook an experimental study of the comparative effects of Atterberg limits, particles size and compaction parameters on the unconfined compressive strength of selected soils. This was with a view to ascertaining which of the combinations of the soil properties will produce a good prediction of the unconfined compressive strength. To achieve this aim, soil samples were obtained from selected locations within Ife Central Local Government Area, Osun State, Nigeria. The following tests were conducted on the soil samples, following standard procedures: natural moisture content determination, specific gravity, Atterberg limits, compaction and unconfined compressive strength. Using Regression tool, the results obtained from the laboratory tests were used to develop the relationships between each of the index properties and unconfined compressive strength. Results showed that the natural moisture content of soil samples ranges between 18.48 % and 25.03 %; specific gravity ranges between 2.35 and 2.69; liquid limit ranges between 39.95 % and 83.98 %; plastic limit ranges between 29.32 % and 51.18 %; and plasticity index is between 8.74 % and 33.56 %. The maximum dry density ranges between 15.30kN/m3 and 19.30kN/m3 with their optimum moisture contents ranging between 13.80 % and 35.50 % while unconfined compressive strength is between 36.00 kN/m2 and 97.14 kN/m2. The results of regression analysis showed that effective size and coefficient of uniformity have the greatest effect (R2 = 0.82) on unconfined compressive strength of the tested soil samples. Therefore, the study concluded that effective size and coefficient of uniformity could be used to estimate the unconfined compressive strength of the soils.