Point Load Strength Research Articles

Point Load Strength Index and Slake Durability Index of Marbles from the Kanti Highway, Central Nepal LesserHimalaya

Point Load Strength Index (PLSI) is determined to obtain the strength of the rock, while Slake Durability Index (SDI) offers quantitative data on the mechanical behavior of rocks exposed to cyclic wetting and drying. This study aims to find out such index properties of marbles from the Markhu Formation of Central Nepal, Lesser Himalaya. The study of the strength and durability of marbles is crucial to classifying strength and identifying resistance to weathering, deterioration patterns, and rock quality. Fresh rock samples were collected from 20 different locations within the Markhu Formation, exposed across the Kanti Highway. The SDI and PLSI tests were carried out. The study reveals that the PLSI ranges from 2.54 to 9.08 MPa, and the test samples belong to strong to very strong rock categories according to the calculated value of UCS, indicating that the marbles from the Markhu Formation are of good quality and are strong enough to withstand the load, contributing to rock mass quality. The marbles have extremely high second cycle SDI (Id2) of 98.92% to 99.50%, and therefore, a high resistance to the wetting and drying cycle, showing slow slaking behavior. Between the second and the fifth cycle, SDI (Id5) was deduced by about 1%. It implies that the marbles are highly resistant to weathering and erosion. Marble shows two kinds of disintegration after the second cycle and deteriorates under three types of patterns under five cycles.

Estimation of mode I fracture toughness of rocks exposed to different environmental conditions using simple and linear multiple regression

AbstractMode I fracture toughness (Kıc) is a critical parameter in rock mechanics that is essential for understanding how rocks behave under tensile loading and crucial for applications ranging from safety assessments to structural design in geotechnical engineering. This study comprehensively investigates the influence of various environmental conditions (dry, saturated, frozen, thermal shock and thermal aging) on the physico-mechanical properties and Kıc of rocks. The primary novelty of this study lies in its comprehensive modeling approach under diverse environmental conditions, providing a nuanced understanding of factors influencing rock fracture toughness. By extending analysis to less-studied conditions such as freezing and thermal shock cycles, the study enhances the predictive capacity of fracture toughness models in practical geotechnical applications. Physico-mechanical properties, including uniaxial compressive strength, point load strength, Brazilian tensile strength (BT), Schmidt hardness, and ultrasonic wave velocity were evaluated across different environmental scenarios. Simple and linear multiple regression models were developed using these properties to predict Kıc. Notably, BT emerged as a significant predictor in the simple regression analyzes. Ten linear multiple regression models were formulated using SPSS 20, combining mechanical tests (UCS, BT, PL) with non-destructive testing methods (Vp, Vs, SH), demonstrating robust predictive capabilities with R2 values exceeding 0.95. Performance metrics (mean absolute error, mean absolute percentage error, root mean square error) were used to verify the accuracy of the model.

Estimation of Uniaxial Compressive Strengths of Cappadocia Pyroclastic Rocks after Freeze–Thaw Cycles Using Point Load Strength Index

Estimation of Uniaxial Compressive Strengths of Cappadocia Pyroclastic Rocks after Freeze–Thaw Cycles Using Point Load Strength Index

Evaluation of Ni-Cu Ore from Zapolyarnoe Based on Mineralogical and Physical Properties before and after Comminution

For the effective comminution and subsequent enrichment of mineral ores, comprehensive knowledge of their mineralogical and physical properties is required. Using an integrated methodology, this study evaluated samples of polymetallic Ni-Cu ore from Zapolyarnoe, Russia. Several analytical techniques were utilised, including optical microscopy, microindentation with Vickers geometry, the Point Load Test, and Mineral Liberation Analysis (MLA). The purpose of this study was to determine mineral associations, physical features, and enrichment during jaw crusher comminution. The acquired properties included the Point Load Strength Index, Vickers Hardness Number, and fracture toughness. The MLA method characterised seven fractions in terms of particle size distribution, degree of liberation, association, and modal mineralogy. Magnetite, pyrrhotite, pentlandite, and chalcopyrite were calculated in terms of wt% and their textural features. The enrichment of each ore phase in fractions with particle sizes smaller than 400 µm was determined. The influence of this enrichment was discovered to be correlated with various textural and structural parameters, such as intergrowth, grain size, and crack morphologies after indentations. In addition, the chromium content of magnetite contributed to an increase in the fracture toughness values. Despite the complexities involved, even limited samples of materials provide valuable insights into processing behaviour, emphasising the importance of considering mineralogical parameters in comminution studies.

Comparison and combination of Leeb hardness and point load strength for indirect measuring tensile and compressive strength of rocks

Comparison and combination of Leeb hardness and point load strength for indirect measuring tensile and compressive strength of rocks

Effect of rock properties on wear and cutting performance of multi blade circular saw with iron based multi-layer diamond segments

This study is an attempt for comprehensive, combining experimental data with advanced analytical techniques and machine learning for a thorough understanding of the factors influencing the wear and cutting performance of multi-blade diamond disc cutters on granite blocks. A series of sawing experiments were performed to evaluate the wear and cutting performance of multi blade diamond disc cutters with varying diameters in the processing of large-sized granite blocks. The multi-layer diamond segments comprising the Iron (Fe) based metal matrix were brazed on the sawing blades. The segment’s wear was studied through micrographs and data obtained from the Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDS). Granite rock samples of nine varieties were tested in the laboratory to determine the quantitative rock parameters. The contribution of individual rock parameters and their combined effects on wear and cutting performance of multi blade saw were correlated using statistical machine learning methods. Moreover, predictive models were developed to estimate the wear and cutting rate based on the most significant rock properties. The point load strength index, uniaxial compressive strength, and deformability, Cerchar abrasivity index, and Cerchar hardness index were found to be the significant variables affecting the sawing performance.

Research on Concrete Compressive Strength Detection Technology Based on Intelligent Machine Vision

Abstract Concrete is the most common and important building material nowadays. Its compressive strength plays a crucial role in the result and safety of the building. To improve the efficiency of concrete compressive strength detection, this study combines intelligent machine vision technology to design a concrete compressive strength detection system. The features of concrete are extracted using the edge detection method. Then the extracted features are classified using the random forest method to complete the identification and localization of concrete. Based on this basis, the compressive strength of concrete is calculated and detected based on the conversion relationship between uniaxial compressive strength and point load strength. Finally, after testing the performance of the system, the practical effects of the system are examined. According to the results, the system’s detection rate is between 0.058 and 0.072 seconds, and the recognition accuracy and classification accuracy of the four different types of concrete detection exceed 80%. The relative error values for the detected compressive strength were 5.87% and 3.52%, respectively, and they passed the compressive strength detection of retardation diagrams in complex situations. The excellent performance of this study in real concrete detection meets the demand for concrete compressive detection in reality.

Reliability assessment of carbon fiber mortar: Combined pulse velocity, point load, and compressive strength tests

Cement mortar is used as an adhesive and plaster for brick walls, drainage channels or irrigation with river stones or mountain stones, and in the manufacturing of short revetments. The performance of cement mortar requires continuous improvement by construction materials researchers, scientists, and building contractors. Recently, carbon fiber sheet has gained popularity for use in retrofitting and improvement to the performance of building elements. This study aims to recycle small sheets of unused carbon fiber by cutting them into short carbon fiber (SCF) with a uniform length of 12 mm, which is used as an internal reinforcement in the cement mortar. All plain and fibrous mortar were prepared with a 50 mm cube mold. In addition, tests were conducted to provide reliable data regarding the application of recycled carbon fiber sheets as discrete fiber reinforcement in the improvement of cement mortar; these include ultrasonic pulse velocity (UPV), compressive strength, and point load strength index (PLSI) (known for performance rock testing in geotechnical and geological environments) tests. The test results for UPV, PLSI, and compressive strength showed that the addition of carbon fiber improves the performance of the mortar when compared to plain mortar. Furthermore, two simplified relationships are made: the first correlation was for PLSI with compressive strength, and the second correlation was for UPV with compressive strength. Finally, these correlations were compared with those of experimental tests obtained from published literature, thus demonstrating the correctness of the PLSI, compressive strength, and UPV values obtained in this study.

Evaluating the Effect of Diameter-to-Length Ratio in Point Load Index Test on Predicting Uniaxial Compressive Strength

Accurate determination of the strength properties of rock materials is very important in engineering projects. The most important parameter used to express the strength of rocks is the uniaxial compressive strength (UCS). However, in some cases it can be quite difficult to determine the UCS. For example, when it is difficult to obtain rock specimens of the required size for UCS testing, indirect methods such as point load strength and ultrasonic wave velocity are used to estimate UCS. If the UCS is determined incorrectly, this can lead to irreversible design errors, project delays and financial losses. PLI testing is performed on specimens of different shapes as well as on specimens of different sizes. This study investigates the ability of the values obtained as a result of PLI tests on specimens prepared with different diameter/length (D/L) ratios to predict UCS. For this purpose, PLI experiments were performed on seven different carbonate rocks prepared at different D/L ratios. The relationships between the obtained values and the UCS values of the rocks were analyzed. High correlations were obtained between PLI and UCS values and it was observed that D/L ratio has no significant effect on UCS estimation.

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).

Overview of the Empirical Relations between Different Aggregate Degradation Values and Rock Strength Parameters

The aggregates are essential materials in civil engineering, they are used for railway and road constructions, for hydraulic engineering but they are also the base material of concrete. The crushed stones are exposed to several effects during their lifespan. Therefore, several tests were developed to evaluate their performance. One of the most important aspects is the resistance to degradation. However, degradation tests require special types of equipment and usually take longer than common strength tests which are more likely to be available for rock materials. Therefore, the empirical connections between strength and degradation values can be extremely useful in practice. The paper aimed to collect all available relationships and datasets from the literature that presents the relations between these different parameters – such as Aggregate Impact Value (AIV), Aggregate Crushing Value (ACV), Ten Percent Fines Value (TFV), Los Angeles Abrasion Value (LAAV), and micro-Deval Coefficient (MDE) – and rock strength parameters – such as Uniaxial Compressive Strength (UCS) and Point Load Strength Index (IS(50)) – and to provide the best-fit formula for different rock types. The paper also highlights the difficulties and limitations of the compared relationships.

Experimental study on deterioration of bedrock strength and P-wave velocity by pressurized seawater

Bedrock has to be excavated before the construction of rock-socketed pile foundations for offshore wind power turbines, leading to the exposure of fresh and dry rocks to seawater. The pile-rock interface is partially separated under the long-term action of various cyclic loads during the operation stage of offshore wind turbines, and the infusion of seawater will further weaken the bedrock. To comprehensively investigate the deterioration of bedrocks by pressurized seawater, tests on point load strength and slake durability were carried out to study the long-term deterioration of mechanical properties of granite, sandstone, and tuff collected from an offshore wind power farm. The elastic P-wave velocity was measured to analyze the variation trend of P-wave velocity, the mechanism of which was revealed by means of Gassmann’s equation. The results of mechanical experiments indicate that granite is almost impervious to pressurized seawater, while the mechanical properties of sandstone and tuff are deteriorated by water, especially by the pressurized seawater. After 60 days of immersion in seawater at 0.5 MPa pressure, decreasing amplitude of point load strength and slake durability index for the sandstone specimens in seawater reaches 53.2% and 18.5%, respectively, and that for the tuff specimens is 14.6% and 1.52%, respectively. The elastic P-wave velocity of granite shows an upward trend with increasing immersion time in different environments, but there exists a different tendency between the P-wave velocity of sandstone and tuff specimens in water or seawater with normal pressure and that in seawater with 0.5 MPa pressure. The variation of P-wave velocity of different types of rocks is studied based on Gassmann’s equation that takes into account the change of saturation degree, porosity, and compressibility of the matrix material.

Prediction of uniaxial compressive strength of rock based on lithology using stacking models

Uniaxial compressive strength (UCS) of rock is an essential parameter in geotechnical engineering. Point load strength (PLS), P-wave velocity, and Schmidt hammer rebound number (SH) are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS. This study collected 1080 datasets consisting of SH, P-wave velocity, PLS, and UCS. All datasets were integrated into three categories (sedimentary, igneous, and metamorphic rocks) according to lithology. Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types. Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types. UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks. Nonetheless, the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks. The developed predictive models can be applied to quickly predict UCS at engineering sites, which benefits the rapid and intelligent classification of rock masses. Moreover, the importance of SH, P-wave velocity, and PLS were analyzed for the estimation of UCS. SH was a reliable indicator for UCS evaluation across various rock types. P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks, but it was not reliable for assessing the UCS of metamorphic rocks.

Strength and durability status of the Fagfog Quarzite from the Lower Nawakot Group, central Nepal Lesser Himalaya

Railways have played vital role in the development of many countries worldwide. Government of Nepal has already planned to establish various national and international railway networks within the nation. A substantial quantity of durable rock ballasts is required for building a sustainable railway network. Quartzites from central Nepal Lesser Himalaya can be a promising source to fulfill this requirement. Therefore, the quartzites from the stratigraphic unit Fagfog Quartzites of the Lower Nawakot Group located in Dhading District along the Kalu Pandey Highway have been assessed to determine physical, strength and durability characteristics of the ballasts. All quartzite ballasts obtained from the bedrocks were of Precambrian, medium-grained, bladed to prolate shaped, white to brownish white, crushed quartzites. Dry density and water absorption were respectively 2336-2641 kg/m3 and 0-2.5%. The bulk density varies from 1204 and 1387 kg/m3. Point load strength index varies from 2.04 to 11.55 MPa (medium strong to extremely strong). Aggregate Impact Value (AIV) and Aggregate Crushing Value (ACV) ranges from 14.85 to 22.77% and 15.51 to 22.17% respectively. Some 7% results of AIV and 20% of ACV results exceeded the established limiting value of 22%. The slake durability index (Id5) ranges from 95.19 and 99.11%, whereas Id2 ranged between 96.94 and 100%, which shows that 80% of the samples have very high slake durability whereas remaining 20% exhibit high slake durability. There was no significant change in slake durability index from the second to the fifth cycles. Los Angeles Abrasion Value (LAAV) varies from 12.07 to 31.34% and Sulphate Soundness Value (SSV) varies within a limited range of 0.20 and 4.59%. Conclusively, the comprehensive evaluation suggests that the quartzite ballast from the Fagfog Quartzite source display favourable qualities in terms of point load, crushing and impact strengths and durable against abrasion, slaking and frosting. Thus, the quartzites from Fagfog Quartzite hold significant potential as a suitable rock type for railway track ballasts.

Study on the mechanical properties of anisotropic red sandstone under point load strength test and uniaxial compression strength

The red sandstone of Xiaopu 3# branch Cave in the Yuxi section of the Central Yunnan Water Diversion Project was taken as the research object. The uniaxial compression strength and point load strength test are conducted from the perspectives of vertical and parallel stratifications, respectively, and then the anisotropy of red sandstone was analyzed in detail. The data obtained from the field and laboratory test was analyzed, and the conversion relationship between point load strength index and uniaxial compression strength is a power function under both parallel stratification and vertical stratification. The study results showed that: The mechanical properties of red sandstone have obvious anisotropy. The difference in its mechanical properties is particularly evident in UCS and point load tests and Is(50) and UCS under vertical stratification are significantly greater than Is(50) under parallel stratification. The point load strength correction index m of red sandstone is m = 0.4096 under parallel stratification, and m = 0.4408 under vertical stratification.

Railway ballast aggregate characterization through a new strength index: Preliminary suitability evaluation of an industrial by-product for a railway application

Ballast is a layer of the railway infrastructure that ensures the correct geometric position of track equipment, distributing vertical loads on the underlying layers. Ballast is composed by aggregate grains of adequate grading, containing a consistent number of voids and a medium–low fine content.The aggregates of ballast must be tested in the laboratory to ascertain the suitability for their use under construction or maintenance. In this work, the physical–mechanical characteristics of different types of aggregate (natural and “artificial”) were evaluated to verify their acceptability as ballast under the Italian specifications. All the aggregates came from Italian production plants. One “artificial” aggregate (steel slag) was used to assess if it can be used instead of the natural aggregate on a railway line in Italy. A mixture of natural and artificial aggregates was also tested for comparison purposes. An index from Point Load Test was also assessed to predict the main physical–mechanical characteristics provided by the laboratory tests.The artificial aggregate shows excellent physical–mechanical properties, compared to the natural one, indicating that the tested by-product can replace the natural aggregate. The mixture of natural and artificial aggregates can guarantee the preservation of natural resources and solve supply difficulties of by-products, maintaining very good physical–mechanical characteristics.In general, the Los Angeles index (both before and after the freeze-thawing cycles) shows a good correlation with the Point Load Strength Index for the tested ballast aggregates. Starting from this result, the Point Load Test could be used to characterize ballast aggregates. A statistical analysis was also performed on the other physical–mechanical parameters: a good correlation was found in some cases, indicating that the Point Load Strength Index can provide a first indication of the other physical–mechanical characteristics of ballast aggregates.

Evaluation of excavation method on point load strength of rocks with poor geological conditions in a deep metal mine

In the field of deep mining engineering, it poses a challenge to promptly determine the mechanical properties of rocks under poor geological conditions through in-situ tests. However, the indirect determination of uniaxial compressive strength (UCS) of rocks can be achieved through the point load strength index (PLSI) test on irregular samples. In the present study, laboratory uniaxial compressive and field PLSI tests were carried out on irregular ore and rock blocks extracted through mechanical mining methods from a stope at a lead–zinc mine in Yunnan Province, China, with a depth of approximately 1000 m. The effects of mechanical excavation and drilling-blasting methods on the PLSI of rocks and ores are compared. It is found that there are significant differences in the point load strength indexes obtained by different excavation methods, and the Is(50) obtained after the mechanical excavation method approximates the actual value of ore and surrounding rocks. Two correction methods were utilized to obtain the point load strength indexes Is(50)-1 and Is(50)-2 of irregular rock samples. The correlation factors (k) linking Is(50) to UCS and Brazilian splitting strength are derived based on testing results. The findings indicate that using the conversion factor (k) recommended by ISRM to predict the UCS of rocks may significantly underestimate the actual strength of rocks in intricate mining environments. This study can serve as a benchmark for analogous deep mining projects.

Effect of Defects on Uniaxial Compressive Strength and Point Load Strength for Rock

Effect of Defects on Uniaxial Compressive Strength and Point Load Strength for Rock

Machine learning models for predicting unconfined compressive strength: A case study for Precambrian basement complex rocks from Ado-Ekiti, Southwestern Nigeria

This study investigates the applicability of four distinct machine learning (ML) models – Random Forest (RF), Relevance Vector Machine (RVM), Support Vector Machine (SVM), and Artificial Neural Network (ANN) - for predicting the unconfined compressive strength (UCS) values of Precambrian basement rocks. The models employ input parameters such as rock aggregate index (Aggregate Crushing Value (ACV), Aggregate Impact Value (AIV), and Los Angeles Aggregate Value (LAAV)) and mechanical test index values (Schmidt Hammer Rebound Number (SHR) and Point Load Strength Index (IS(50)) for prediction purposes. Additionally, a multilinear regression (MLR) model was implemented to compare the predictive performance of the ML models against the traditional linear regression model. The developed models were validated using performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Adjusted R-Squared (Adj.R2). The Friedman statistical test was also employed to investigate the significance of the differences in the performance of the models. The results indicate that all the models performed well and showed better predictive performance compared to the MLR model during both the training and testing phases, except for the ANN model, which exhibited slightly poorer performance than the MLR model in the testing phase. However, the SVM model demonstrated the best predictive accuracy among all the models with MAE, RMSE, and Adj.R2 scores of 0.76 and 1.45; 1.16 and 1.8; 0.98 and 0.90, respectively, for the training and testing phases. The difference in the performance of the predictive models was statistically significant, as revealed by the Friedman statistical test. A feature importance analysis was conducted to verify the significance of each input parameter, revealing that all input features had a positive effect on the output of all the models. However, IS(50) had the most significant impact on the models.

CORRELATION OF UNIAXIAL COMPRESSIVE STRENGTH WITH BRAZILIAN TENSILE STRENGTH AND INDEX PROPERTIES FOR SOFT SEDIMENTARY ROCKS

Sedimentary Rocks composing the Thar Lignite basin Pakistan are of clastic origin. These rocks have comparatively low uniaxial compressive strength (UCS) values, and hence recognized as ‘soft sedimentary rocks’ in this study. UCS is a fundamental property of rocks, used by mine design engineers in designing the surface and underground excavations. The purpose of this study is to investigate the relationship between UCS with Brazilian tensile strength (BTS), and index properties of soft sedimentary rock formations at Thar Lignite basin. Various correlations between mechanical and physical properties of rocks have been developed previously. However, no significant correlation has been developed on UCS with BTS and index properties for soft sedimentary rocks. Numerous Rock samples from Two complete geotechnical drillholes at Block-IX Thar Coalfield were selected. Standard test procedures were implemented to determine the UCS, indirect Tensile and index properties such as point load strength, and shore Scleroscope hardness. The correlations between rock properties were established using simple and multiple regression techniques, and empirical equations were obtained. These equations can be used to predict the UCS and tensile strength of soft sedimentary rocks by performing simple index tests; which are quick, economical, and easier to be performed on the site.