A correlation between P-wave velocity,impact strength index,slake durability index and uniaxial compressive strength

Impact strength index, slake durability index and uniaxial compressive strength (UCS) are important properties of a rock mass which are used widely in geological and geotechnical engineering. In this study, the mechanical properties of one igneous, three sedimentary and three metamorphic rock types were determined in the laboratory and correlated with P-wave velocity. Empirical equations have been developed to predict the impact strength index, slake durability index and UCS from P-wave velocity, which may avoid the necessity for time-consuming and tedious laboratory testing. To check the sensitivity of the empirical relations, a t test was performed which confirmed the validity of the proposed correlations.      

Estimating the uniaxial compressive strength of pyroclastic rocks from the slake durability index

Because the preparation of standard samples may not always be possible for weak or soft rocks, the prediction of uniaxial compressive strength (UCS) from indirect methods is widely used for preliminary investigations. In this study, the possibility of predicting UCS from the slake durability index (SDI) was investigated for pyroclastic rocks. For this purpose, pyroclastic rocks were collected from 31 different locations in the Cappadocian Volcanic Province of Turkey. The UCS and SDI tests were carried out on the samples in the laboratory. The UCS values were correlated with the SDI values and a very strong exponential relation was found between the two parameters. Since some data were scattered over the UCS values of 20 MPa, the correlation plot was redrawn for above and below the UCS values of 20 MPa, respectively. Very strong linear correlations were developed for two cases. Our concluding remark is that the UCS of pyroclastic rocks can be estimated from the SDI.

     

Estimating the unconfined compressive strength of intact rocks from Equotip hardness

In order to utilize the Equotip hardness tester, originally developed in the field of metallic engineering, as an indirect method to predict the unconfined compressive strength (UCS) of rock, laboratory tests were undertaken to establish the UCS, Equotip hardness (L-value) and porosity of nine rock types. Using existing data from Verwaal and Mulder (Int J Rock Mech Min Sci Geomech Abstr 30:659–662, 1993) and the results of laboratory tests, an equation relating UCS (MPa) and L-value obtained from single impacts (L _s) and porosity (n %) was derived, which provides a close approximation of the UCS value. An equation to relate UCS and Equotip hardness is also presented, although this is less accurate. It is considered Equotip testing has advantages over the commonly used Schmidt hammer test.      

Correlation between slake durability and rock properties for some carbonate rocks

The paper reports a study to assess the relationship between slake durability indices and uniaxial compressive strength, Schmidt hardness, P-wave velocity, modulus of elasticity, effective porosity, water absorption and dry and saturated unit weight for seven types of carbonate rocks obtained from south west Turkey. It was found that the dry unit weight, saturated unit weight and Schmidt hardness gave the best relationship with first cycle slake durability (r = 0.99) while uniaxial compressive strength has a strong relationship with fourth cycle slake durability (r = 0.94). The results showed little difference in the correlation coefficients obtained after the fourth cycle. It is concluded that, for the rocks studied, the first and fourth cycles provide sufficiently good data on the durability for preliminary engineering/design works and that the second to fourth cycle results could be estimated using the first cycle slake durability index (r = 0.99–0.97).     

Fuzzy model for the prediction of unconfined compressive strength of rock samples

A data driven approach to the modeling of unconfined compressive strength of rock samples is presented. Fuzzy logic approach is used to represent a nonlinear relationship as a smooth concatenation of local linear submodels. The partitioning of the input space into fuzzy regions, represented by the individual rules, is obtained through fuzzy clustering. The numerical results are compared with a conventional statistical (multi-linear) model. It is shown that the fuzzy model is not only more accurate but as opposed to other black-box approaches (such as neural networks), it also provides some insight into the nonlinear relationship represented by the model.     

Predicting performance of impact hammers from rock quality designation and compressive strength properties in various rock masses

Predicting the performance of the impact hammers is one of the major subjects in determining the economics of the underground excavation projects in which they are utilized. Therefore, researchers have been attracted to developing performance prediction models for these machines. Physical and mechanical properties of rocks have been used to estimate the performance of impact hammers over the last few decades. In this study, the instantaneous breaking rate (IBR, m³/h) of an impact hammer used in construction of Levent-Hisarüstü metro tunnel (Istanbul) is recorded in detail. Sixty rock samples are obtained from tunnel route during the excavation of which the machine is employed. Physical and mechanical property tests are performed on the obtained samples. A data set including uniaxial compressive strength (UCS), rock quality designation index (RQD), Brazilian tensile strength (BTS), density (ρ), Schmidt hammer hardness (SHH), Shore scleroscope hardness (SSH), Cerchar abrasivity index (CAI), and IBR is formed. Regression analysis techniques are applied to the created data set in order to develop a performance prediction model. The investigation results in a model that can predict IBR based on UCS, RQD, and the output power of the impact hammer. The proposed model passes both F-test and t-test at 0.95 confidence level. The soundness of the model is successfully tested against two formerly developed models. Covering a wide range of application and requiring only two of the most common and versatile rock properties as input parameters are the other advantages of the suggested model.     

Rapid determination of soil unconfined compressive strength using reflectance spectroscopy

Bulletin of Engineering Geology and the Environment - Understanding the physical and especially mechanical properties of forest soils is very important in forest engineering operations including...     

Predicting uniaxial compressive strength,modulus of elasticity and index properties of rocks using the Schmidt hammer

The Schmidt hammer test is a non-destructive method which can be used in both laboratory and field to provide a quick and relatively inexpensive measure of rock hardness. The study investigated the relationship between the Schmidt hardness and modulus of elasticity, uniaxial compressive strength and index properties of nine types of rock including travertine, limestone, dolomitic limestone and schist. The empirical equations developed indicated the Schmidt hardness rebound values have a reliable relationship with the uniaxial compressive strength of rock (r = 0.92). Comparing the results with those reported by other researchers, it is concluded that no single relationship can be considered reliable for all rock types. Whilst the equations developed in this study may be useful at a preliminary stage of design, they should be used with caution and only for the specified rock types.      

Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness

This paper presents the application of a neural network for the prediction of the UCS from hardness tests on rock samples. To investigate the suitability of this approach, the results of the network are compared to predictions obtained by conventional statistical relations.The network was trained to predict the UCS based on the hardness, porosity, density, grain size and rock type information of a rock sample. A dataset containing 194 rock sample records, ranging from weak sandstones to very strong granodiorites, was used to train the network with the Levenberg–Marquardt training algorithm. Two sets, each containing 17 rock samples, were used to validate the generalization and prediction capabilities of the network.     

Predicting uniaxial compressive strength of serpentinites through physical,dynamic and mechanical properties using neural networks

The uniaxial compressive strength(UCS)of intact rock is one of the most important parameters required and determined for rock mechanics studies in engineering projects.The limitations and difficulty of conducting tests on rocks,specifically on thinly bedded,highly fractured,highly porous and weak rocks,as well as the fact that these tests are destructive,expensive and time-consuming,lead to development of soft computing-based techniques.Application of artificial neural networks(ANNs)for predicting UCS has become an attractive alternative for geotechnical engineering scientists.In this study,an ANN was designed with the aim of indirectly predicting UCS through the serpentinization percentage,and physical,dynamic and mechanical characteristics of serpentinites.For this purpose,data obtained in earlier experimental work from central Greece were used.The ANN-based results were compared with the experimental ones and those obtained from previous analysis.The proposed ANN-based formula was found to be very efficient in predicting UCS values and the samples could be classified with simple physical,dynamic and mechanical tests,thus the expensive,difficult,time-consuming and destructive mechanical tests could be avoided.     

Predicting the compressive strength and slump of high strength concrete using neural network

High Strength Concrete (HSC) is defined as concrete that meets special combination of performance and uniformity requirements that cannot be achieved routinely using conventional constituents and normal mixing, placing, and curing procedures. HSC is a highly complex material, which makes modelling its behavior very difficult task. This paper aimed to show possible applicability of neural networks (NN) to predict the compressive strength and slump of HSC. A NN model is constructed, trained and tested using the available test data of 187 different concrete mix-designs of HSC gathered from the literature. The data used in NN model are arranged in a format of seven input parameters that cover the water to binder ratio, water content, fine aggregate ratio, fly ash content, air entraining agent, superplasticizer and silica fume replacement. The NN model, which performs in Matlab, predicts the compressive strength and slump values of HSC. The mean absolute percentage error was found to be less then 1,956,208% for compressive strength and 5,782,223% for slump values and R² values to be about 99.93% for compressive strength and 99.34% for slump values for the test set. The results showed that NNs have strong potential as a feasible tool for predicting compressive strength and slump values.     

水泥土无侧限抗压强度室内试验研究

通过对水泥土室内配方试验结果的归纳与分析,研究了水泥土无侧限抗压强度与土的水泥掺量、龄期和含水率等影响因素的关系。试验结果表明,水泥搅拌桩能有效提高土体无侧向抗压强度,为今后水泥土搅拌桩的设计和施工提供了参考依据。     

Modeling the slake durability index using regression analysis, artificial neural networks and adaptive neuro-fuzzy methods

Clay bearing, weathered and other weak rocks cause major problems in engineering practice due to their interactions with water. The slake durability index (I _d2) is an important tool used to assess the resistance of these rocks to erosion and degradation, but sample preparation for this test is tedious. The paper reports an attempt to define I _d2 through statistical models using other parameters that are simpler to obtain. The main objective of this study was to define the best empirical relationship between the I _d2 and the point load strength index (I _s(50)), dry unit weight (γ _d) and fractal dimension (D) parameters of eight rock types by applying general multiple linear regression (GLM), artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The models obtained were evaluated using the R ², MSE, MARE and d parameters. The results indicate that the relationships between I _d2 and γ _d, I _s(50) and D were best obtained using ANN, followed by GLM and ANFIS. It is concluded that ANN modelling is a fast and practical method of establishing I _d2.     

Relation between hardness and compressive strength of polymer concrete

Experimental studies were made of the influence of mixing and casting techniques and of curing time upon the compressive strength and hardness of methyl methacrylate based polymer concrete. The Rockwell K and F scales were found to be suitable for the hardness determinations. Over a wide range of hardness and strength values, the average compressive strength (f_c) varied linearly with the average hardness (R_K) according to the relation f_c (psi) = 560 R_K - 12000, irrespective of casting and curing variables. The weaker materials exhibited greater variations in hardness. Hardness testing is found to provide a meaningful and convenient method for evaluating the quality of polymer concrete.     

Estimation of uniaxial compressive strength from point load strength,Schmidt hardness and P-wave velocity

Uniaxial compressive strength is considered one of the most important parameters in the characterization of rock material in rock engineering practice. The study investigated correlations between uniaxial compressive strength and point load index, P-wave velocity and Schmidt hardness rebound number together with the effects of core diameter size. A total of 150 core samples at five different diameters (54, 48, 42, 30 and 21 mm) were obtained from sandstone, limestone and cement mortar. Ten saturated samples at each diameter (length:diameter ratio 2:1) were prepared from each of the three materials. The best correlations were found between uniaxial compressive strength and point load or Schmidt hammer values. The closest relationship was observed for the 48 mm diameter cores.      

冻结钙质黏土无侧限抗压强度影响因素研究

对典型的钙质黏土进行人工冻土单轴抗压强度试验,分析了强度曲线的特征,得到了冻结钙质黏土抗压强度随冻结温度、应变速率的变化规律,及在不同养护时间、尺寸效应的影响规律,其结论对冻结法施工、冻结壁设计具有应用价值。     

水泥土强度及其微观结构的试验性研究

通过室内试验比较不同水灰比、不同龄期下水泥土无侧限强度的变化规律，从而探究电阻率结构参数法反映水泥土宏观强度的可行性，揭示出水泥土试块强度的形成机理及其抗压破坏过程的微观结构变化规律。     

Effects of freeze-thaw cycles on the unconfined compressive strength of straw fiber-reinforced soil

Although natural fibers can improve the strength behavior of frozen-thawed soil, the reinforcing mechanism is still not fully understood. To investigate the effects of freeze-thaw cycles on the strength of natural fiber-reinforced soil, unconfined compression tests, single-fiber pull-out tests and scanning electron microscopy (SEM) tests under 0, 3, 5, 10, 15, and 20 freeze-thaw cycles were conducted on cotton straw fiber-reinforced soil. It was found that the unconfined compressive strength (UCS) of fiber-reinforced soil decreases exponentially with the number of freeze-thaw cycles. In addition, fiber reinforcement weakens the softening degree of frozen-thawed soil under unconfined states. The UCS reduction in fiber-reinforced soil under freeze-thaw conditions is smaller than the strength reduction at the fiber-soil interface because fiber reinforcement is mainly governed not only by the fiber-soil interface but also by the spatial stress network established by discrete fibers. The complex spatial stress network, which improves the reinforcement of the fibers, is monitored by SEM after freeze-thaw cycles.