T. N. Singh

Estimation of elastic constant of rocks using an ANFIS approach

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Youngs modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Youngs modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Youngs modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Youngs modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.

Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks

Petrographic features of a rock are intrinsic properties, which control the mechanical behaviour of the rock mass at the fundamental level. This paper deals with the application of neural networks for the prediction of uniaxial compressive strength, tensile strength and axial point load strength simultaneously from the mineral composition and textural properties. Statistical analysis has also been conducted for prediction of the same strength properties and compared with the predicted values by neural networks to investigate the authenticity of this approach. The network was trained to predict the uniaxial compressive strength, tensile strength and axial point load strength from the mineralogical composition, grain size, aspect ratio, form factor, area weighting and orientation of foliation planes (planes of weakness). A data set having 112 test results of the four schistose rocks were used to train the network with the back-propagation learning algorithm. Another data set of 28 test results of the four schistose rocks were used to validate the generalization and prediction capabilities of the network.

A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks

The engineering properties of rocks play a significant role in planning and designing of mining and civil engineering projects. A laboratory database of mechanical and engineering properties of rocks is always required for site characterization and mineral exploitation. Due to discontinuous and variable nature of rock masses, it is difficult to obtain all physicomechanical properties of rocks precisely. Prediction of unconfined compressive strength from seismic wave velocities (Compressional wave, Shear wave) and density of rock using generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference systems (ANFIS) can be appropriate and alternate methods to minimize the time and cost of tests. GRNN and ANFIS models were trained with 41 data sets using conjugate gradient descent algorithms and hybrid learning algorithm, respectively. Performance of both the models was examined with 15 testing data sets. In the present study, obtained network performance indices such as correlation coefficient, mean absolute percentage error, root mean square error and variance account for indicate high performance of predictive capability of GRNN system and closer to actual data over the ANFIS.

Correlation Between Point Load Index and Uniaxial Compressive Strength for Different Rock Types

Uniaxial compressive strength (UCS) is one of the most significant geomechanical properties of rock, being of importance in civil engineering, mining, geotechnical, and infrastructure projects, etc. The UCS is a useful approximate parameter when considering a variety of issues encountered during blasting, excavation, and supporting in engineering works (Hoek 1977). UCS values are also employed in geomechanical classification of rock mass, viz. the rock mass rating (RMR) and Q system, which are used in designing and planning of underground works (Bieniawski 1976; Barton et al. 1974). There are standard methods for determination of UCS, proposed by various scientific agencies (ASTM 1984; ISRM 1979 1985), but all of them are tedious, time consuming, and expensive. Moreover, obtaining core samples of the desired geometry is often not possible, particularly in soft or highly jointed rock masses. Therefore, indirect tests such as the determination of the point load strength index (PLI) are widely used and accepted for estimation of the UCS value. Point load tests are preferred, as the test is quite flexible in terms of the sample to be used, ease of testing, and applicability in the laboratory as well as in the field. A number of researchers have attempted to provide empirical relations between UCS and PLI (D’Andrea et al. 1964; Broch and Franklin 1972; Bieniawski 1975; Hassani et al. 1980; Gunsallus and Kulhawy 1984; Panek and Fannon 1992; Singh and Singh 1993 and Kahraman 2001). These equations give quite similar results, although a few of them show wide variation. However, there is a need for more experimental work for better correlation, particularly for Indian rocks. The main objective of this study is to test and verify the empirical relation between PLI and UCS for some Indian rocks. All tests were performed on NX-size core samples of ten different rock types of igneous, sedimentary, and metamorphic origin from seven different lithostratigraphic units. A total number of 318 core samples were tested, and the average of three test results for each rock was used for analysis to reduce variation in the dataset.

An Empirical Correlation of Index Geomechanical Parameters with the Compressional Wave Velocity

The geomechanical strength of rockmass plays a key role in planning and design of mining and civil construction projects. Determination of geomechanical properties in the field as well as laboratory is time consuming, tedious and a costly affair. In this study, density, slake durability index, uniaxial compressive strength (UCS) and P-wave velocity tests were conducted on four igneous, six sedimentary and three metamorphic rock varieties. These properties are crucial and used extensively in geotechnical engineering to understand the stability of the structures. The main aim of this study is to determine the various mechanical properties of 13 different rock types in the laboratory and establish a possible and acceptable correlation with P-wave velocity which can be determined in the field as well as laboratory with ease and accuracy. Empirical equations were developed to calculate the density, slake durability index and UCS from P-wave velocities. Strong correlations among P-wave velocity with the physical properties of different rock were established. The relations mainly follow a linear trend. Student’s ‘t’ test and ‘F’ test were performed to ensure proper analysis and validation of the proposed correlations. These correlations can save time and reduce cost during design and planning process as they represent a reliable engineering tool.

SenSlide : a sensor network based landslide prediction system

Landslides are a serious geological hazard caused when masses of rock, earth, and debris flow down a steep slope during periods of intense rainfall and rapid snow melt. The western (Konkan) coast and the Himalayan region of India are subject to many such landslides every year. Landslides in these rocky regions are mainly caused by the increase in strain due to percolating rain water in rocks fissures, causing rocks to fracture and slide down the slope. According to government reports, from 1998 to 2001 alone, landslides have killed more than 500 people, disrupted the communication and transport for weeks and destroyed thousands of hectares of crop area. Existing solutions are restricted to landslide detection. A trip wire is installed along the landslide prone areas, and a break in the trip wire due to the falling rocks and debris triggers an alarm. Although this is an inexpensive solution for landslide detection, it is ineffectual in providing warning of the impending landslide. Typical sensors used for monitoring slope stability are multi-point bore hole extensometers, tilt sensors, displacement sensors, and volumetric soil water content sensors. These require drilling 20-30 meter holes into the surface making the installation very expensive (≈

Prediction of Blast Induced Air Overpressure in Opencast Mine

50 per meter)

A comparative study of ANN and Neuro-fuzzy for the prediction of dynamic constant of rockmass

Blasting is still considered to be the most economical technique for rock excavation and displacement either on the surface or underground. The explosive energy, which fractures the rockmass is not fully utilized and only 20-30% of the energy is utilized in actual breakage of the rockmass, and the rest of the energy is spread in the form of ground vibration, air blast, flying rock, back break, etc. Air blast is considered to be one of the most detrimental side effects due to generation of noise. A generalized equation has been proposed by many researchers but due to its site specific constants, it cannot be used in other geo-mining conditions. In the present paper, an attempt has been made to predict the air blast using a neural network (NN) by incorporating the maximum charge per delay and distance between blast face to monitoring point. To investigate the appropriateness of this approach, the predictions by a NN are also compared with generalized equation of air overpressure and conventional statistical relations. For prediction of air overpressure, the data set has been taken from two different limestone mines for training of the network while validation of the network has been done by Magnesite mine data set. The network is trained by 41 datasets with 50 epochs and tested by 15 dataset. The correlation co-efficient determined by a NN was 0.9574 while correlation co-efficient were 0.3811 and 0.5258 by generalized equation and statistical analysis respectively. The Mean Absolute Percentage Error (MAPE) for a NN was 2.7437, whereas MAPE for generalized equation and statistical analysis were 8.6957 and 6.9179 respectively.

Comparative study of cognitive systems for ground vibration measurements

Physico-mechanical properties of rocks have great significance in all operational parts in mining activities, from exploration to final dispatch of material. Compressional wave velocity (p-wave velocity) and anisotropic behaviour of rocks are two such properties which help to understand the rock response under varying stress conditions. They also influence the breakage mechanism of rock. There are different methods to determine thep-wave velocity and anisotropyin situ and in the laboratory. These methods are cumbersome and time consuming. Fuzzy set theory, Fuzzy logic and Neural Networks techniques seem very well suited for typical geotechnical problems. In conjunction with statistics and conventional mathematical methods, hybrid methods can be developed that may prove to be a step forward in modeling geotechnical problems. Here, we have developed and compared two different models, Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and Artificial neural network systems, for the prediction of compressional wave velocity.

A numerical simulation of landslide-prone slope in Himalayan region—a case study

This paper deals with the application of a support vector machine (SVM) optimization technique to predict the blast-induced ground vibration. Peak particle velocity (PPV) is an important parameter to be kept under control to minimize the damage caused due to the ground vibration. A number of previous researchers have tried to use different empirical methods to predict PPV, but these empirical equations have their limitations due to its less versatile application and acceptability from field conditions. Therefore, it is difficult to apply these empirical equations to predict PPV because they are based on limited parameters which does not really reflect and connect with real influencing parameters. In this paper, SVM technique is used for the prediction of PPV by incorporating blast design and explosive parameters, and the suitability of one technique over other has been tested based on the results. To avoid the biasness in man-made choice of parameters of SVM, we have used the chaos optimization algorithm to find the optimal parameters which can help the model to enhance the learning efficiency and capability of prediction. Datasets have been obtained from one of the large opencast mine from southeastern coalfield limited, Chhattisgarh, India. One hundred and twenty-seven datasets were used to establish SVM architecture, and 10 datasets have been randomly chosen for validation of SVM model to see its prediction potential. The results obtained have been compared with different vibration predictors, multivariate regression analysis, artificial neural network and the superiority of application on SVM over previous methodology. The mean absolute percentage error using SVM is very low (0.001) as compared to other predictors indicate its better prediction capability.