Browsing by Author "Chandar, K.R."

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A Hybrid Random Forest optimized with the Dolphin Swarm Algorithm for predicting P-Wave Velocity of Sedimentary Rocks using Ball Mill Grinding Characteristics
(World Researchers Associations, 2025) Sahas, S.V.; Bijay, K.M.; Chandar, K.R.
Rock properties play a crucial role in mining, geotechnical engineering and various engineering projects. P-wave velocity helps in determining the quality and stability of rock masses, essential for tunnel excavation, slope stability and mining operations. P-wave velocity also provides critical input for designing foundations for dams, bridges and other rock structures. Accurate determination of P-wave velocity relies on high-quality samples. However, challenges such as preparation, cost and time constraints have prompted a growing reliance on computational methods for its prediction. Previous investigations predominantly leaned on laboratory-based tests and indirect methodologies for predicting rock properties including P-wave velocity. In contrast, this study introduces an innovative technique for predicting wave velocity (Vp) of sedimentary rocks, particularly limestone using ball mill grinding characteristics throughout the grinding procedure, an unconventional yet effective approach. A hybrid random forest model optimized with dolphin swarm algorithm was developed to predict Vp from grinding characteristics. The performance of the model in training and testing phases was assessed based on determination coefficients (R2), root mean-squared error (RMSE) and variance account for (VAF) which are 0.984, 96.204 m/s and 98.25% in training and 0.973, 102.32 m/s and 97.63% in testing phase respectively. © 2025, World Researchers Associations. All rights reserved.
A machine learning framework for predicting elastic properties of sedimentary rocks from ball mill grinding characteristics data
(CRC Press/Balkema, 2024) Swamy, S.V.; Harish, P.; Kunar, B.M.; Chandar, K.R.
Elastic properties of rocks like Youngâ€™s modulus and compressional P-wave velocity are vital for understanding their stress-strain response in mining and rock engineering applications. Traditional methods for determining these properties involve labor-intensive, expensive and time-consuming. To address these challenges, this study proposes a novel predictive method. It utilizes a multi-layer perceptron feed forward neural network (MLP-FFNN) trained on grinding characteristics of ball mill to predict Youngâ€™s modulus and compressional Pwave velocity in sedimentary rocks. Laboratory experiments on limestone and dolomite samples generated extensive data, enabling development of prediction models using the proposed MLPFFNN. The developed models demonstrate high predictive accuracy (R values: 0.952 for E, 0.987 for Vp) in training and good generalization (0.866 for E, 0.9707 for Vp) in testing, along with low Root Mean Squared Error (RMSE) values. These findings underscore the efficacy of neural network models in predicting E and Vp from grinding characteristics of ball mill. Â© 2024 The Author(s).
A review on stability analysis of coal mine dumps
(Inderscience Publishers, 2024) Harish, P.; Chandar, K.R.
Opencast mines are increasingly extracting deeper coal seams in large quantities, leading to a rise in mine depth and generation of substantial waste. Disposal of this waste becomes challenging due to the need for additional land, resulting in dumping excess waste on existing dumps, posing risks of dump failure, property damage, and loss of life. This paper aims the critical review of the stability of dump slope structures that are present on the weak or disturbed foundations which further leads the dumps to fail. Many researchers have concentrated on the irregular base, loose material presence in the foundation, sloping floors, improper compaction at the foundation level, presence of black cotton soil, etc., stating load of the dumps over the weaker foundations exerts more pressure on the foundation and causing the dumps to fail. It synthesises key findings on stability analysis approaches, design criteria, optimisation techniques, and critical parameters involved in numerical modelling-based design for secure dump slope structures. © © 2024 Inderscience Enterprises Ltd.
An overview of the applications of soft computing methods for predicting the physico-mechanical properties of rocks from indirect methods
(Inderscience Publishers, 2023) Bijay Mihir Kunar, S.; Chandar, K.R.
Rocks are widely used in infrastructure constructions like roads, tunnels, buildings, and dams. Understanding physico-mechanical properties of rocks is vital for selecting suitable rocks, yet some properties pose challenges in determination. High-quality core samples and precise instruments are necessary for accurate assessment. Predicting the physico-mechanical properties of rocks is a key research area in rock mechanics. Researchers have employed diverse methods, including laboratory tests, non-destructive tests, and mineralogical and petrographical characteristics, to determine rock properties. This article reviews the use of soft computing methods, artificial intelligence, and machine learning to predict rock properties through indirect methods. Indirect methods involve engineering indices tests, mineralogical and petrographical characteristics, and additional approaches such as electrical properties, crushability indices, thermal characteristics, and grinding characteristics. The article also proposes various artificial intelligence and machine learning techniques as potential future directions in prediction of rock properties. © © 2023 Inderscience Enterprises Ltd.
Development of an alert system in slope monitoring using wireless sensor networks and cloud computing technique – a laboratory experimentation
(Inderscience Publishers, 2023) Mittapally, M.S.; Chandar, K.R.
Opencast mine slope monitoring is crucial to prevent potential failures. Wireless sensor networks (WSNs) offer real-time data collection and analysis for effective slope monitoring to minimises monitoring costs and improves safety. Utilising cost-effective microelectromechanical sensors, slope conditions are wirelessly transmitted using internet of things (IoT), facilitating immediate insights. Monitoring parameters like moisture, vibration, and displacement predict slope behaviour. It is essential to test the sensors before designing and implementing a system for regular monitoring in the field to know the sensor’s performance and match them to the slope condition. The study entails moisture, vibration, and displacement measurements in clay slope models. ZigBee-enabled XBee SC2 modules transmit data to ThingSpeak, triggering PythonAnywhere alerts. As a result, if soil moisture sensor readings were over the predefined threshold value of 50%, an email alert was triggered at the time of the jump. It is concluded that the alert system was developed by using sensors in the clay model developed at a laboratory scale and suitable for field applications on a large scale. © © 2023 Inderscience Enterprises Ltd.
Effect of longwall workings on the stability of overburden dumps
(CRC Press/Balkema, 2024) Harish, P.; Swamy, S.V.; Chandar, K.R.
Extraction of coal is done in both opencast and underground methods. Opencast mining generates a huge amount of overburden while excavating the coal. Managing the overburden material, deposited as dumps at considerable heights to minimize ground coverage, is crucial in opencast mines, but it poses risks such as potential failures. Such failures can stop the mining activities, endanger personnel safety, and damage equipment. At times, limitations in space, the placement of overburden dumps over underground excavations, posing stability challenges due to pre-existing stresses from activities below the surface. This paper explores the stability prediction of overburden dumps above longwall workings, using Rocscience RS2 v19.2, a two-dimensional finite element analysis software. Strength Reduction Technique determines the factor of safety (FOS), revealing that the presence of underground longwall excavation induces a vertical deformation of 56.4mm for the critical strength reduction factor of 1.12, emphasizing the impact on overburden dump stability. Â© 2024 The Author(s).
Estimation of Strength Properties of Some Rocks using Ball Mill Grinding Characteristics
(World Researchers Associations, 2025) Sahas, S.V.; Bijay, K.M.; Chandar, K.R.
The strength properties of rocks namely uniaxial compressive strength and tensile strength are important in design and stability evaluation of various mining, geotechnical engineering and other rock engineering projects. Accurate determination of these properties relies on high-quality samples, but challenges like sample availability, preparation of sample, cost and time constraints have led to an increasing reliance on computational methods for prediction. In this context, an indirect approach is proposed for predicting rock strength properties, specifically the uniaxial compressive strength (UCS) and tensile strength (TS), using grinding characteristics of ball mill, an unconventional yet indirect approach. A predictive modelling using multivariate regression is carried out to estimate the relationship between UCS, TS and the grinding characteristics of ball mill. The developed models demonstrated high accuracy with R² values of 0.93 for UCS and 0.96 for TS. Performance evaluation metrics showed an RMSE of 6.03 MPa and a VAF of 93.45% for UCS and an RMSE of 0.99 MPa and a VAF of 96.47% for TS. The validation was performed using experimental UCS and TS values of basalt rocks along with ball mill grinding test data. The error analysis revealed that UCS prediction error ranged from 5.1% to 11.61% while TS prediction error varied between 4.26% and 16.39%. © 2025, World Researchers Associations. All rights reserved.
Evaluating Blast Fragmentation: A Comparative Study of Electronic and Shock-Tube Initiation Systems in a Limestone Mine
(World Researchers Associations, 2025) Vinith Kumar, P.V.; Raina, A.K.; Balamadeswaran, P.; Sambasivam, V.S.; Saravanan, K.; Chandar, K.R.
Explosive energy is the most widely used method for fragmenting rock masses and mineral deposits in mining operations. The fragmentation achieved during blasting significantly impacts downstream operations including loading, transportation, crushing and processing costs. Among the various factors affecting blast fragmentation, the initiation system plays a crucial role. A study was carried out to compare the performance of electronic detonators with shock-tube detonators, in terms of fragmentation in a limestone mine. Field experiments were conducted to assess the fragment size using digital image analysis technique (DIAT). The results indicated that electronic initiated blasts produced finer average fragment sizes (k50) ranging from 0.31-0.44 m, while as in non-electric shock-tube (NeSt) initiated blasts produced larger fragmentation with k50 values between 0.39-0.51 m. The analysis revealed that average k50 values of blasts initiated with electronic detonator were 20% less than that of non-electric shock tube (NeSt) initiated blasts. This is primarily due to precise delays planned and executed for the rock mass that aid in proper fragmentation. © 2025, World Researchers Associations. All rights reserved.
Geotechnical Investigations of Coal Mine Waste Dump Material
(World Researchers Associations, 2025) Harish, P.; Chandar, K.R.
Mining is an essential activity for obtaining the raw materials required for infrastructure and energy needs. During the extraction of coal, a significant amount of overburden (OB) material is generated. Managing the overburden produced by mining activities demands a substantial land for disposal which can be quite challenging in limited spaces. Consequently, it becomes imperative to design safe coal mine OB dumps, optimizing the use of available ground space. The challenge of ensuring the stability of overburden dumps is directly linked to the geotechnical properties of these materials. Therefore, it is crucial to characterize the properties of the dump material accurately to assess its engineering properties reliably. The focus of this study is to outline the various testing procedures necessary for the proper characterization of dump materials to ensure the stability of the dumps. Additionally, the study provides an analysis of laboratory test data and presents typical results from a case study. © 2025, World Researchers Associations. All rights reserved.
Influence of Underground Workings and Dump Height on the Stability of Overburden Dumps
(World Researchers Associations, 2025) Harish, P.; Satyanarayana, I.; Chandar, K.R.
Coal is extracted using both underground and opencast methods of working. During the coal extraction process, opencast mining produces a significant amount of overburden. In opencast mines, the removed overburden material is dumped at significant heights to reduce ground coverage. But overburden dumps with great heights are at risk and sometimes lead to failure of dumps causing loss of men and machinery. Stability issues will become more complicated when the overburden is dumped above the old underground workings. Complication arises because of redistribution of pre-existing stresses from underground activities affecting the overburden dumps. This study uses a two-dimensional finite element (FE) analysis program to understand the stability analysis of overburden dumps above old underground workings. The factor of safety (FoS) is determined using the strength reduction technique which highlights the impact of underground excavations on overburden dump stability by highlighting the required strength reduction factor (SRF). In order to analyse the overburden dumps with the presence and absence of old underground workings, numerical models were created for various dump heights. The overburden dumps with underground workings exhibited SRF values ranging from 1.78 to 2.05, while the dumps without underground workings had SRF values ranging from 1.81 to 2.55. The displacement of the overburden dump material, which results in 7 mm of horizontal displacement and 29 mm of vertical displacement, indicates a significant impact of underground workings on the stability of the overburden dumps. This study highlights the importance of considering underground workings in the design and management of overburden dumps to ensure safety and stability. © 2025, World Researchers Associations. All rights reserved.
Investigation into the Blast-Induced Damage in Cut and Fill Stoping Operation
(Books and Journals Private Ltd., 2022) Sangode, A.G.; Raina, A.K.; Bagde, M.N.; Chandar, K.R.
The paper presents the results of a comprehensive monitoring carried out to study the extent of blast-induced damage experienced by rockmasses extracted by cut and fill stoping in a manganese mine. Damage is related to strain generated by the blasting and it is found to correlate well with the particle velocity. The particle velocities were measured in the studied mine with seismographs. The attenuation equation for extrapolation of vibration to the near field was derived from the data thus acquired. The site-specific damage model for designing the safe blast parameters was thus devised to minimize the extent of the blast-induced damage to protect the hanging wall, footwall and friable orebody and thus overall improving the stoping environment. The presented work aims at improving the understanding of the influence of blasting on the backfilled area and hard rock in the stoping environment. The damage predicted by different methods and the final strategy for blasting for wall control and productivity are documented in the paper. © 2022, Books and Journals Private Ltd.. All rights reserved.
Predicting Rock Properties of Limestone Using Operating Parameters of Ball Mill
(Springer Nature, 2025) Swamy, S.V.; Kunar, B.M.; Chandar, K.R.
Rock properties are important for mining, geotechnical engineering, and other engineering projects. Accurate determination of these properties relies on high-quality samples, but challenges like sample availability, preparation of sample, cost, and time constraints have led to an increasing reliance on computational methods for prediction. Prior investigations predominantly relied on laboratory-based tests and indirect methodologies to predict properties of rocks. In contrast, this study introduces an innovative technique for predicting rock properties, specifically the P-wave velocity (Vp) and uniaxial compressive strength (UCS) by harnessing ball mill operational parameters throughout the grinding procedure an unconventional yet indirect approach. A multivariate regression model is established to connect operating parameters with the strength properties of limestone samples. The determination coefficients (R2) for Vp and UCS prediction models are 0.892 and 0.868, respectively. Moreover, an Analysis of Variance (ANOVA) is performed to ascertain the influence of significant parameters on the target variables. The accuracy and reliability of the prediction models are further validated through scatter plots and residual variations for both Vp and UCS models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques
(Nature Research, 2025) Swamy, S.V.; Kunar, B.M.; Chandar, K.R.; Alwetaishi, M.; Shashikumar, S.; Reddy, S.
Uniaxial Compressive Strength (UCS) is a fundamental parameter in rock engineering, governing the stability of foundations, slopes, and underground structures. Traditional UCS determination relies on laboratory tests, but these face challenges such as high-quality core sampling, sample preparation difficulties, high costs, and time constraints. These limitations have driven the adoption of indirect approaches for UCS prediction. This study introduces a novel indirect method for predicting uniaxial compressive strength, harnessing the grinding characteristics of a ball mill as predictive variables through supervised machine learning techniques. The correlation between grinding characteristics and UCS was examined to determine whether a linear relationship exists between them. A hybrid support vector machine-recursive feature elimination (SVM-RFE) algorithm is applied to identify the critical grinding parameters influencing UCS. Four supervised machine learning models viz., Multiple Linear Regression (MLR), k-Nearest Neighbor Regression (k-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR) were developed for UCS prediction, with hyperparameter optimization performed using RandomisedSearchCV technique. The Random Forest model outperformed others as the best prediction model, achieving a coefficient of determination (R²) of 0.95, followed by SVR (R² = 0.87), k-NNR (R² = 0.82), and MLR (R² = 0.758). Model robustness was further assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF). Internal validation by means of K-fold cross validation and external validation with independent datasets confirmed generalization capability, showing an average prediction error of ± 10%. The findings demonstrate that combining grinding characteristics with machine learning offers an accurate, cost-effective alternative to conventional UCS testing, with significant practical applications in rock engineering. © The Author(s) 2025.
Stability Analysis of Overburden Dumps over Old Underground Workings Using Artificial Neural Networks
(Pleiades Publishing, 2024) Harish, P.; Chandar, K.R.
Abstract: Stability of overburden dump slopes is a crucial aspect in designing secure and cost-effective dumps. The Strength Reduction Factor (SRF) serves as a widely used term to assess dump stability. This paper focuses on developing an Artificial Neural Network (ANN) model capable of predicting SRF for overburden dumps situated above existing underground workings. To construct the model, a dataset comprising 96 numerical simulations of overburden dumps generated through the finite element method was utilized. A neural network architecture with three layers of forward-backward propagation was utilized, containing hidden neurons to analyze simulations during training, validation and testing stages. The input parameters for studying overburden dump slopes over underground workings included dump slope height (Sh), dump slope angle (), cohesion (C), friction angle (Ø), unit weight () of the dump material, depth of working from the surface (D), centre-to-centre pillar distance in underground workings (C-C), and gallery width (Gw). The ANN predicted results were compared with the outcomes derived from numerical simulations of overburden dump slopes above underground workings. The study highlights that the developed ANN model in this research proves highly effective in handling and designing complex overburden dump slopes. The obtained results indicate a Mean Square Error (MSE) of 0.0595 and a coefficient of determination (R) of 0.883, both of which are considered acceptable. © Pleiades Publishing, Ltd. 2024.