Attenuation Characteristics of Blast-Induced Stress Waves from Field Measurements - Kai Rong; Zong-Xian Zhang; José A. Sanchidrián; Adeyemi Aladejare; Li-Yuan Chi; Pablo Segarra (Geotechnical and Geological Engineering, 2026)
The attenuation characteristics of stress waves were studied based on 16 small-scale single-row blast tests at Otanmäki mine. In each test, the primary joint orientation was documented, two monitoring points were positioned at different distances from the blastholes, and the particle velocities in three orthogonal directions were measured. Seismic curves for the three components are shown under both short- and long-delay blasts. The attenuation laws of peak particle velocity ( PPV ) vector resultant derived from the radial, transverse and vertical particle velocity time histories, as well as seismic energy, with respect to scaled distance under three different primary joint orientations were analysed. The results show that the attenuation of stress waves is smallest when joints dipping into the face and greatest when joints dipping out of the face, and this difference may be attributed to the variations in the energy transmission efficiency of stress waves across joints under different incident angles. The geometric attenuation law of stress waves was analysed, which varies with the relationship between measuring distance and wavelength. According to this law, a modified charge scaling law was proposed in the form of a piecewise function. The validation results indicate that this law has practical applicability to a certain extent.

A probabilistic approach to characterize coal strength from point load and P-wave velocity tests - Adeyemi Emman Aladejare (Arabian Journal of Geosciences, 2026)
Obtaining the values of the uniaxial compressive strength (UCS) of coal is crucial to the design and analysis at a coal mine. The laboratory measurement of the UCS of coal is difficult and may be impossible because of the nature of coal and the sample preparation requirements of uniaxial compression tests, resulting in limited or no UCS test values at most coal sites. To overcome the challenge, a probabilistic characterization of the UCS of coal is developed using results from point load and P-wave velocity tests at a coal mine. The probabilistic characterization is based on Bayesian updating (BU) framework. The framework systematically incorporates prior knowledge and observational data, enabling dynamic updating as new data becomes available, which is an improvement over static empirical model. In addition, the framework provides a transparent method to update and quantify uncertainty in UCS predictions and improves interpretability of results. Using the parameters of point load strength (Is (50) ) and P-wave velocity (Vp) from a coal mine together with the ranges of UCS available in literature, the distribution and statistics of the UCS of coal is updated. The BU approach satisfactorily provides probabilistic characterization of the UCS of coal. The approach is further validated using simulated Is (50) and Vp data to investigate how number of input data, and different prior knowledge affect the approach. Such characterization allows mining engineers and practitioners to directly use the values of the UCS of coal from the approach to perform probability-based designs and reliability analyses at coal sites.

Uncertainty Quantification in Mineral Resource Estimation - Oltingey Tuya Lindi; Adeyemi Emman Aladejare; Toochukwu Malachi Ozoji; Jukka-Pekka Ranta (Natural Resources Research, 2024)
Mineral resources are estimated to establish potential orebody with acceptable quality (grade) and quantity (tonnage) to validate investment. Estimating mineral resources is associated with uncertainty from sampling, geological heterogeneity, shortage of knowledge and application of mathematical models at sampled and unsampled locations. The uncertainty causes overestimation or underestimation of mineral deposit quality and/or quantity, affecting the anticipated value of a mining project. Therefore, uncertainty is assessed to avoid any likely risks, establish areas more prone to uncertainty and allocate resources to scale down potential consequences. Kriging, probabilistic, geostatistical simulation and machine learning methods are used to estimate mineral resources and assess uncertainty, and their applicability depends on deposit characteristics, amount of data available and expertise of technical personnel. These methods are scattered in the literature making them challenging to access when needed for uncertainty quantification. Therefore, this review aims to compile information about uncertainties in mineral resource estimation scatted in the literature and develop a knowledge base of methodologies for uncertainty quantification. In addition, mineral resource estimation comprises different interdependent steps, in and through which uncertainty accumulates and propagates toward the final estimate. Hence, this review demonstrates stepwise uncertainty propagation and assessment through various phases of the estimation process. This can broaden knowledge about mineral resource estimation and uncertainty assessment in each step and increase the accuracy of mineral resource estimates and mining project viability.

Reliability of Monte Carlo simulation approach for estimating uniaxial compressive strength of intact rock - Adeyemi Emman Aladejare; Kayode Augustine Idowu; Toochukwu Ozoji (Earth Science Informatics, 2024)
The strength of rock has significant influence on its performance, and is, therefore, a key input during modelling and analysis of mining and geotechnical engineering structures. The uniaxial compressive strength (UCS), which is a popular parameter to quantifying rock strength can be determined in the laboratory using suggested method by International Society of Rock Mechanics (ISRM). However, the laboratory determination of UCS consumes time, it is costly, and sometimes may not be feasible to perform because of different conditions of rock. Hence, this study attempts to employ Monte Carlo simulation (MCS) approach to estimate UCS, and to overcome various uncertainties associated with UCS estimation. To use MCS approach for UCS estimation, block punch index (BPI), Brazilian tensile strength (BTS), point load index (IS (50) ), and P-wave velocity (Vp) were selected as the model inputs. A multiple linear regression (MLR) equation was developed and used to predict UCS by the MCS approach. The methodology was applied to estimate UCS using real BPI, BTS, Is (50) , and Vp data as inputs. The proposed approach simulated UCS values that are consistent with UCS values measured in the laboratory. The mean of the UCS values simulated through the MCS approach is 119.10 MPa, while the mean of the UCS values measured in the laboratory is 118.42 MPa. In addition, hypothesis testing revealed that the Brazilian tensile strength (BTS) is the parameter with the most influence on UCS of rock for the site investigated.

Probabilistic characterization of Brazilian tensile strength of rock from both direct and indirect measurements - Adeyemi Emman Aladejare (Arabian Journal of Geosciences, 2023)
The determination of Brazilian tensile strength (BTS) is an essential step in the analysis and design of mining structures at a particular site. The estimated design parameters are affected by the inherent variability of BTS, measurement errors arising from laboratory testing, and transformation uncertainty associated with the empirical model linking BTS to other rock properties when it is indirectly estimated. These uncertainties are usually lumped together as the total variability of BTS. However, it is the inherent variability resulting from natural geological factors, not the total variability, that directly affects the actual response of rock structures. Hence, there is a need for proper characterization of the inherent variability of BTS while the measurement errors and transformation uncertainty are explicitly incorporated. This paper develops a Bayesian approach which uses sequential updating, that is multi-input oriented for probabilistic characterization of the inherent variability of BTS of rock. The proposed approach systematically combines previous engineering experience and site information from both direct BTS data and data from indirect tests like point load test to inversely infer the inherent variability of BTS. The proposed approach quantitatively accounts for the effects of measurement errors and transformation uncertainty on the characterization of the inherent variability of BTS. The proposed approach is illustrated and validated using real-life data and simulated data. The result shows that the proposed approach provides a proper characterization of the inherent variability of BTS based on available information from multiple sources. Sensitivity studies are also performed to explore the effects of measurement errors on the performance of the proposed approach.

Effect of Characteristic Impedance in Estimating Specific Energy and Average Fragment Size at High Strain Rates of Some Peridotitic Rock Materials at Laboratory Scale - Toochukwu Ozoji; Zong-Xian Zhang; Aladejare Adeyemi; Yang Qiao; Li-Yuang Chi (Rock Mechanics and Rock Engineering, 2023)
In this study, the application of characteristic impedance in estimating specific energy and average fragment size of rocks was investigated during rock breakage at high strain rates. To achieve this, rock specimen was prepared in accordance with recommendations of the International Society for Rock Mechanics and broken at high strain rates using the split Hopkinson’s pressure bar system. Results reveal that although strain rate is well related to specific energy and average fragment size of broken rocks, the product of characteristic impedance and strain rate is more reliable for estimating the forementioned parameters. In addition, strain rate and dissipated energy generally increase at higher incident energies while the average fragment size of broken rocks reduces at higher strain rates. Based on these findings, more studies on indirect estimation of energy requirement for rock breakage to desired average fragment sizes is recommended from the product of characteristic impedance and strain rate.

Characteristic Impedance and Its Applications to Rock and Mining Engineering - Zong-Xian Zhang; Fengqiang Gong; Elena Kozlovskaya; Adeyemi Aladejare (Rock Mechanics and Rock Engineering, 2023)
The characteristic impedance of a rock is defined as the product of the sonic velocity and the density of the rock. Based on previous studies, this article finds that: (1) For an intact rock, its characteristic impedance is a comprehensive physical property, since it is closely related with strengths, fracture toughness, Young’s modulus, and Poisson’s ratio. (2) For rock masses, their characteristic impedances either increase markedly or slightly with increasing depth. (3) The bursts of intact rocks in laboratory are dependent on their characteristic impedances to a great extent, and strong rock bursts happen mostly in the rocks with large characteristic impedance. (4) Rock burst occurrence in tunnel and mines has a close relation with the characteristic impedances of the rocks. (5) Laboratory experiments on different rock samples show that seismic velocity increases as applied stress rises, and field monitored results from coal mines indicate that in the areas where rock bursts happened, the seismic velocity was increasing markedly before or during the bursts. (7) Drillability of rock depends on the characteristic impedance of the rock and the rock with larger impedance has lower drillability or lower penetration rate. (8) The potential applications of characteristic impedance include evaluation and classification of rock masses, and prediction of rock burst proneness and drillability.

Prediction of thermal conductivity of granitic rock: an application of arithmetic and salp swarm algorithms optimized ANN - Abiodun Ismail Lawal; Sangki Kwon; Minju Kim; Adeyemi Emman Aladejare; Moshood Onifade (Earth Science Informatics, 2022)

Soft Computing-Based Models for Predicting the Characteristic Impedance of Igneous Rock from Their Physico-mechanical Properties - Adeyemi Emman Aladejare; Toochukwu Ozoji; Abiodun Ismail Lawal; Zongxian Zhang (Rock Mechanics and Rock Engineering, 2022)
Rock properties are important for design of surface and underground mines as well as civil engineering projects. Among important rock properties is the characteristic impedance of rock. Characteristic impedance plays a crucial role in solving problems of shock waves in mining engineering. The characteristics impedance of rock has been related with other rock properties in literature. However, the regression models between characteristic impedance and other rock properties in literature do not consider the variabilities in rock properties and their characterizations. Therefore, this study proposed two soft computing models [i.e., artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)] for better predictions of characteristic impedance of igneous rocks. The performances of the proposed models were statistically evaluated, and they were found to satisfactorily predict characteristic impedance with very strong statistical indices. In addition, multiple linear regression (MLR) was developed and compared with the ANN and ANFIS models. ANN model has the best performance, followed by ANFIS model and lastly MLR model. The models have Pearson's correlation coefficients of close to 1, indicating that the proposed models can be used to predict characteristic impedance of igneous rocks.

Empirical estimation of rock mass deformation modulus of rocks: comparison of intact rock properties and rock mass classifications as inputs - Adeyemi Emman Aladejare; Toochukwu Malachi Ozoji; Musa Adebayo Idris; Abiodun Ismail Lawal; Moshood Onifade (Arabian Journal of Geosciences, 2022)
Deformation modulus of rock mass (Em) is an important parameter for the analysis and design of mining engineering projects. However, field tests for measuring deformation modulus of rock mass are difficult, time-consuming, and capital intensive. This has led to the development of numerous empirical models for estimating rock mass deformation modulus, which are in different forms and scattered in the literature. The numerous models available in the literature use different types of inputs. Therefore, this study provides a comprehensive compilation of different empirical models for estimating the deformation modulus of rock masses. The compiled models are grouped based on their type of input parameter(s) into three categories such as those using intact rock properties, rock mass classification indices, and combination of intact rock properties and rock mass classification indices. Then, a comparative analysis was performed using absolute average relative error percentage (AAREP) and variance accounted for (VAF) to assess the reliability of using different types of inputs for estimation of deformation modulus of rock masses using data from two sites. The results of the analyses show that rock mass classification indices are the most reliable indices for estimating the deformation modulus of rock masses among the categories considered for analyses. For AAREP analyses in the two illustrative examples considered in this study, models (7 out of 10) using rock mass classification indices in the estimation of Em have the best performances with AAREP values ranging from 24.07 to 55.15%. For VAF analyses in the two examples, models (8 out of 10) using rock mass classification indices in the estimation of Em have the best performances with values ranging from 59.81 to 88.11%. The lowest errors and highest deviation similarities from models using rock mass classification indices indicate that they produce the most reliable estimations of Em. It is important to note that the reliability of deformation modulus estimated from empirical models depends on the quality of input data as the models performed differently across the sites used in this study. This study therefore provides a compilation of available models for estimating deformation modulus, performance evaluation of available models for estimating deformation modulus, and guidelines for selecting appropriate model for estimating deformation modulus of rock mass.

An Overview of the Use of Rockbolts as Support Tools in Mining Operations - Kolapo Peter; Onifade Moshood; Praise Oluwatomisin Akinseye; Amwaama Martha; Said Omar Khadija; Jibril Abdulsalam; Lawal Abiodun Ismail; Aladejare Adeyemi Emman (Geotechnical and Geological Engineering, 2022)

Predicting the peak particle velocity from rock blasting operations using Bayesian approach - Adeyemi Emman Aladejare; Abiodun Ismail Lawal; Moshood Onifade (Acta Geophysica, 2022)
Measuring the blast-induced ground vibration at blasting sites is very important, to plan and avoid adverse effects of blasting in terms of the peak particle velocity (PPV). However, the measurement of PPV often requires time, cost, and logistic commitment, which may not be economical for small-scale mining operations. This has prompted the development of numerous regression equations in the literature to estimate PPV from a relatively easier to estimate scaled distance (SD) measurement. With numerous regression equations available in the literature, there is a challenge of how to select the appropriate model for a specific blasting site, more so that rocks behave differently from site to site because of different geological processes that rocks are subjected to. This study develops a method that selects appropriate models for specific blasting sites by comparing the evidence and occurrence probability of different regression models. The appropriate model is the model with the highest evidence and occurrence probability given the available blasting site SD data. The selected model is then integrated with prior knowledge and available blasting SD data in Bayesian framework for probabilistic characterization of PPV. The SD and PPV data at the opencast coal mine, Jharia coalfield in the Dhanbad district of Jharkhand, India, is used to illustrate and validate the approach. The mean and standard deviation of simulated PPV samples from the proposed approach are 12.38 mm/s and 7.36 mm/s, respectively, which are close to the mean of 12.03 mm/s and standard deviation of 9.24 mm/s estimated from the measured PPV at the site. In addition, the probability distribution of the simulated PPV samples is consistent with the probability distribution of the measured PPV at the blasting site.

Development of soft computing based mathematical models for predicting mean fragment size coupled with their Monte Carlo simulation estimations - Abiodun Ismail Lawal; Adeyemi Emman Aladejare; Moshood Onifade; Sangki Kwon (Arabian Journal of Geosciences, 2022)

On the Application of the Novel Thin Spray-on Liner (TSL): A Progress Report in Mining Operations - Peter Kolapo; Moshood Onifade; Khadija Omar Said; Martha Amwaama; Adeyemi Emman Aladejare; Abiodun Ismail Lawal; Praise Oluwatomisin Akinseye (Geotechnical and Geological Engineering, 2021)

Empirical Estimation of Uniaxial Compressive Strength of Rock: Database of Simple, Multiple, and Artificial Intelligence-Based Regressions - Adeyemi Emman Aladejare; Emmanuel Damola Alofe; Moshood Onifade; Abiodun Ismail Lawal; Toochukwu Malachi Ozoji; Zong-Xian Zhang (Geotechnical and Geological Engineering, 2021)
Empirical relationships for estimating Uniaxial Compressive Strength (UCS) of rock from other rock properties are numerous in literature. This is because the laboratory procedure for determination of UCS from compression tests is cumbersome, time consuming, and often considered expensive, especially for small to medium-sized mining engineering projects. However, these empirical models are scattered in literature, making it difficult to access a considerable number of them when there is need to select empirical model for estimation of UCS. This often leads to bias in estimated UCS data as there may be underestimation or overestimation of UCS, because of the site-specific nature of rock properties. Therefore, this study develops large database of empirical relationships between UCS and other rock properties that are reported in literatures. Statistical analysis was performed on the regression equations in the database developed. The typical ranges and mean of data used in developing the regressions, and the range and mean of their R 2 values were evaluated and summarised. Most of the regression equations were found to be developed from reasonable quantity of data with moderate to high R 2 values. The database can be easily assessed to select appropriate regression equation when there is need to estimate UCS for a specific site.

Characterization of the Petrographic and Physicomechanical Properties of Rocks from Otanmäki, Finland - Adeyemi Emman Aladejare (Geotechnical and Geological Engineering, 2021)
Intact rock properties are important for mining and geotechnical engineering because they are important design parameters for tunnels, rock foundations, rock slopes, among others. Some of the properties are also used as input parameters in some rock mass classification systems. Therefore, as rock properties are site-specific, there is need to investigate rock properties at a site especially when such site is used for engineering purpose. This paper documents the petrographic and X-ray fluorescence analyses, and physical and mechanical tests conducted to estimate the physical and mechanical properties of the two rock types (i.e. gabbro and granite) selected from quarries at Otanmäki, Finland. Mineral composition, grain size and chemical composition, water content, porosity, Brazilian tensile strength, uniaxial compressive strength, Young’s modulus, and Poisson ratio of the rock types were determined. Both gabbro and granite samples have fine to medium grained texture. The results of laboratory experiments conducted show that rock properties investigated have low to high variability. Simple and multiple regression analyses were performed and useful predictive models for estimating Brazilian tensile strength from physical test results were developed and validated.

Design and Sensitivity Analysis of Rock Slope Using Monte Carlo Simulation - Adeyemi Emman Aladejare; Victor Oluwatosin Akeju (Geotechnical and Geological Engineering, 2020)
A probabilistic approach that is based on Monte Carlo simulation (MCS) was developed in this study to design and perform sensitivity analysis of rock slope. The probabilistic approach uses MCS to perform a series of single objective optimizations for design of rock slope and to perform sensitivity analysis of rock slope stability. The MCS-based approach was used to evaluate the failure probability of a rock slope system and to determine a safe maximum slope height for rock slope design. To achieve this, the performance of different rock properties and rock slope conditions were explicitly considered towards achieving the target reliability index of the rock slope. The approach can achieve multiple rock slope design specifications using different target reliability indexes from a single run of MCS. The proposed probabilistic approach was illustrated through an example of rock slope design to determine feasible designs under different rock slope conditions. Also, sensitivity studies were performed to explore the effects of uncertainties in tension crack depth and water depth in tension crack, and variability in rock unit weight. The results show that the effects of uncertainties and variability on rock slope stability can be significant and should be incorporated during design analysis. Incorporating such uncertainties and variability in rock slope design is achieved with relative ease using the proposed approach.

Probabilistic Characterization of Hoek–Brown Constant mi of Rock Using Hoek’s Guideline Chart, Regression Model and Uniaxial Compression Test - Adeyemi Emman Aladejare; Yu Wang (Geotechnical and Geological Engineering, 2019)

Evaluating Variability and Uncertainty of Geological Strength Index at a Specific Site - Yu Wang; Adeyemi Emman Aladejare (Rock Mechanics and Rock Engineering, 2016)