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To investigate the effect of water on the mechanical properties and acoustic emission (AE) characteristics of coal in the failure and deformation processes. Coal samples of different content were subjected to uniaxial compression tests and AE signals were monitored. The characteristics of the AE signals were further analyzed using fractal analysis. The results show that saturated coal samples have substantially reduced mechanical properties such as uniaxial compressive strength (UCS), dissipation energy, peak stress, and elastic modulus. Under loading, stress–strain curves are characterized by five distinct stages: (1) compaction; (2) linear elastic; (3) crack stable propagation; (4) crack accelerating propagation; and (5) post-peak and residual stages. Using phase-space theory, a novel Grassberger Procaccia (GP) algorithm was utilized to find the AE fractal characteristics of coal samples in different stages. It is significant to note that AE energy does not exhibit fractal characteristics in either the first or second stages. Contrary to the first two stages, the third stage showed obvious fractal characteristics. Fractal analysis of AE time sequences indicates that fractal dimension values change as stress increases, indicating the initiation of complex microcracks in coal. In the fourth stage, the fractal dimension rapidly declines as the strength reaches its limit, indicating the occurrence of macrocracks. However, fractal dimensions continued to decrease further or increased slightly in the fifth stage. Consequently, the coal begins to collapse, potentially resulting in a disaster and failure. It is, therefore, possible to accurately predict coal and rock dynamic failures and microcrack mechanisms by observing the subsequent sudden drop in the correlation dimension of the AE signals in response to different stages of loading.

Water is one of the most effective agents that weaken the physio-mechanical properties of rock, trigger significant construction delays, endanger the construction operation, and lead to rock failure. Therefore, an early failure point (EFP) prediction of rock under such conditions is imperative for the robust and reliable implementation of underground engineering. In this research, an EFP of sandstone with different water contents was predicted based on infrared radiation and complex energy evolution during loading. The ratios of elastic to dissipation energy (KED) and elastic to total energy (KET) were proposed to predict EFP. The results show that KED and KET give EFP at the same time for sandstone with water contents 0%, 0.991%, 2.136%, and 3.109%, and the average time of EFP ahead 208 s, 250 s, 265.8 s, and 276.9 s than rock failure, respectively. Furthermore, the proposed KED and KET were predicted using an artificial neural network (ANN). The ANN models’ efficacy was evaluated using the performance coefficient (R2) and root-means-square error (RMSE). The findings revealed high R2 and low RMSE for KED and KET of sandstone with different water contents. The research findings can be used effectively to monitor disasters for the safe and efficient execution of engineering projects.

Purpose. To analyze the coupling effect between composite slope geometrical parameters. Methods. The slope angle and excavation depth are coupled with load which is waste dump in this case. Several models were created and analyzed to capture their coupling effect and interactions using FLAC/Slope. Findings. When the slope angle and excavation depth are coupled with a load such as that of a waste dump, the factor of safety decreases. But a suitable dumping position can prove vital to enhancing stability. The primary cause of this phenomenon is that the stresses induced by the waste dump decrease as the dump is moved away from the crest of the slope and the stress induced within the zone of active wedge beneath the dump reduces on the reference slip plane. Hence, it can be said that the position of the waste dump in the formation of a composite slope plays a key role in enhancing stability. Factor of safety have the same influence pattern for all parameters induced by stress as that of influence rule. The results obtained from the finite element stress analysis are the same with those obtained for the slope stability analysis. Originality. The current research presents for the first time the coupling effect of the composite slope geometrical parameters and the results of finite element stress analysis, which are similar to those of slope stability analysis. Practical implications. The current research results can be used to effectively analyze and design the composite slopes in soft rocks specially in surface mines.

In this paper, the dilatancy stress and mechanical characterization of sandstone were evaluated under uniaxial loading at different elastic modulus and porosity conditions. The prediction model of dilatancy stress was established using a regression equation and an artificial neural network based on a multilayer perceptron (ANN–MLP). The results indicate that: (1) The rock crack initiation stress, dilatancy stress and its elastic modulus are a power function relationship, while porosity is linearly negatively correlated. (2) σci/σmax hardly changes with the change of elastic modulus (E) and porosity (n); its value is about 0.443. σcd/σmax increase with the increase in the elastic modulus, but decrease with the increase in the porosity. (3) Most of the rock samples are observed as a tensile failure when the porosity is low, while they are a shear failure at medium porosity and tensile shear composite failure at high porosity. (4) The optimum value from the ANN–MLP model for dilatancy stress with architecture 6-5-1 having coefficient correlation (R2, 0.96%) was obtained at mean absolute error (MAE, 0.18981) and root mean square error (RMSE, 0.17016). It is worth mentioning that the research results will help and provide a reference for the related to rock mechanics test, rock engineering deformation and failure mechanism, and will also give specific guidelines significance for the efficient design of excavation and support in deep rock engineering.

Rock failure is the root cause of geological disasters such as slope failure, civil tunnel collapse, and water inrush in roadways and mines. Accurate and effective monitoring of the loaded rock failure process can provide reliable precursor information for water inrushes in underground engineering structures such as in mines, civil tunnels, and subways. The water inrush may affect the safe and efficient execution of these engineering structures. Therefore, it is essential to predict the water inrush effectively. In this paper, the water inrush process of the roadway was simulated by laboratory experiments. The multiparameters such as strain energy field and infrared radiation temperature field were normalized based on the normalization algorithm of linear function transformation. On the basis of analyzing the variation characteristics of the original parameters, the evolution characteristics after the parameters normalization algorithm were studied, and the precursor of roadway water inrush was predicted comprehensively. The results show that the dissipation energy ratio, the infrared radiation variation coefficient (IRVC), the average infrared radiation temperature (AIRT), and the variance of successful minor infrared image temperature (VSMIT) are all suitable for the prediction of roadway water inrushes in the developing face of an excavation. The intermediate mutation of the IRVC can be used as an early precursor of roadway water inrush in the face of an excavation that is being developed. The inflection of the dissipation energy ratio from a declining amount to a level value and the mutation of VSMIT during rock failure can be used as the middle precursor of roadway water inrush. The mutation of AIRT and VSMIT after rock failure can be used as the precursor of roadway imminent water inrush. Combining with the early precursor and middle precursor of roadway water inrush, the graded warning of “early precursor–middle precursor–final precursor” of roadway water inrush can be obtained. The research results provide a theoretical basis for water inrush monitoring and early warning in the sustainable development of mine, tunnel, shaft, and foundation pit excavations.

In rock mechanics, the dilatancy point is always occurring before rock failure during loading process. Water content plays a significant role in the rock physiomechanical properties, which also impact the rock dilatancy point under loading process. This dilatancy point significantly plays a warning role in the rock engineering structures stability. Therefore, it is essential to predict the rock dilatancy point under different water contents to get an early warning for effective monitoring of engineering projects. This study investigates the water contents effects on sandstone dilatancy point under loading in the presence of infrared radiation (IR). Furthermore, this IR was used for the first time as an input parameter for different artificial intelligence (AI) techniques to predict the dilatancy point in the stress-strain curve. The experimental findings show that the stress range in stress-strain curve stages (crack closure and unstable crack propagation) increases with water content. However, this range for deformation and stable crack propagation stages decreases with water content. The dilatancy stress, crack initiation stress, and elastic modulus are negatively linearly correlated, while peak stress and stress level are negatively quadraticaly correlated with a high (R2). The absolute strain energy rate, which gives a sudden increase at the point of dilatancy, is used as the dilatancy point index. The stress level is 0.86 σmax at the dilatancy point for dry rock and decreases with water content. This index is predicted from IR data using three computing techniques: artificial neural network (ANN), random forest regression (RFR), and k-nearest neighbor (KNN). The performance of all techniques was evaluated using R2 and root-means-square error (RMSE). The results of the predicted models show satisfactory performances for all, but KNN is remarkable. The research findings will be helpful and provide guidelines about underground engineering project stability evaluation in water environments. © 2022 Liqiang Ma et al. All Rights Reserved. This paper was supported by the National Natural Science Foundation of China (51874280) and the Fundamental Research Funds for the Central Universities (2021ZDPY0211).

In order to better understand the failure process of water-bearing rocks, samples of water-bearing sandstone were tested uniaxially. The failure process and the development of internal cracks were studied through the evolution characteristics of dissipated strain energy and particle flow simulation. In this study, we found that: (1) The presence of water in sandstone results in a reduction in energy storage capacity as well as strength. (2) The dissipated energy ratio curve of sandstone samples and simulated samples’ internal fracture development curve has obvious stages. The dissipated energy ratio turning point and the rapid fracture development point are defined as the failure precursor points of sandstone samples and simulated samples, respectively. In both sandstone samples and simulated samples, the ratio between failure precursor stress and peak strength remains almost unchanged under various water conditions. (3) The ratio of fracture to dissipated energy (RFDE) of sandstone is proposed, and interpreted as the increased number of cracks in the rock under the unit dissipated. On this basis, the fracture initiation dissipated energy (FIDE) of sandstone under different water cut conditions is determined, that is, the dissipation threshold corresponding to the start of the development of sandstone internal cracks. (4) The analysis shows that RFDE increases exponentially and FIDE decreases negatively with the scale-up in moisture content. Further, high moisture content sandstone consumes the same dissipative strain energy, which will lead to more fractures in its interior. The research in this paper can lay a theoretical and experimental foundation for monitoring and early warning of rock engineering disasters such as coal mining, tunnel excavation, slope sliding, and instability.

It is significant to monitor the leakage at the joints of the diaphragm walls of subway station foundation pits to check the weak links in the waterproof quality of the diaphragm wall structure. It is essential to take effective waterproof measurements timely to improve the overall waterproof quality of the diaphragm wall in the foundation pit to prevent accidents and reduce the operation and maintenance costs. This paper used ground penetrating radar (GPR) to detect the Lishan North Road Station section of Jinan Rail Transit Line R2 during construction. The abnormal waveform image is obtained after processing radar detection data with Reflexw software. This abnormal waveform image is used to identify the abnormal area. In order to accurately predict the location of leakage at the joint of diaphragm wall, MATLAB is used to calculate the average wave velocity amplitude and single channel signal of the electromagnetic wave velocity of geological radar at different mileages and draw the trend chart of average wave velocity amplitude with mileage and the corresponding relationship curve of electromagnetic wave amplitude and depth of radar. It is proposed that sudden changes in the area of the average wave velocity amplitude cause a change in the trend chart. Furthermore, the radar electromagnetic wave velocity amplitude curve is taken as the area where seepage may occur at the joints of the diaphragm wall, so as to determine the corresponding mileage and depth of the leakage area. On this basis, the grey correlation analysis for the analysis of the source of the water leakage at the joints of the diaphragm wall of the subway foundation pit is proposed. The research results show that the leakage water at the joints of the diaphragm wall of the subway foundation pit is not connected to the rivers around the foundation pit, which confirms that the construction of the subway station has not affected the groundwater resources around the station. The proposed approach has successfully predicted the location of the foundation pit leakage disaster and has been verified on the project site. The research results provide a reference for the monitoring and early warning of leakage at the joints of diaphragm walls in foundation pits with similar geological conditions.

The mechanical properties of rocks, such as uniaxial compressive strength and elastic modulus of intact rock, must be determined before any engineering project by employing lab or in situ tests. However, there are some circumstances where it is impossible to prepare the necessary specimens after exposure to high temperatures. Therefore, the propensity to estimate the destructive parameters of thermally heated rocks based on non-destructive factors is a helpful research field. Egyptian granodiorite samples were heated to temperatures of up to 800 °C before being treated to two different cooling methods: via the oven (slow-cooling) and using water (rapid cooling). The cooling condition, temperature, mass, porosity, absorption, dry density (D), and P-waves were used as input parameters in the predictive models for the UCS and E of thermally treated Egyptian granodiorite. Multi-linear regression (MLR), random forest (RF), k-nearest neighbor (KNN), and artificial neural networks (ANNs) were used to create predictive models. The performance of each prediction model was also evaluated using the (R2), (RMSE), (MAPE), and (VAF). The findings revealed that cooling methods and mass as input parameters to predict UCS and E have a minor impact on prediction models. In contrast, the other parameters had a good relationship with UCS and E. Due to severe damage to granodiorite samples, many input and output parameters were impossible to measure after 600 °C. The prediction models were thus developed up to this threshold temperature. Furthermore, the comparative analysis of predictive models demonstrated that the ANN pattern for predicting the UCS and E is the most accurate model, with R2 of 0.99, MAPE of 0.25%, VAF of 97.22%, and RMSE of 2.04.

The rock mass deformation modulus (Em) is an essential input parameter in numerical modeling for assessing the rock mass behavior required for the sustainable design of engineering structures. The in situ methods for determining this parameter are costly and time consuming. Their results may not be reliable due to the presence of various natures of joints and following difficult field testing procedures. Therefore, it is imperative to predict the rock mass deformation modulus using alternate methods. In this research, four different predictive models were developed, i.e., one statistical model (Muti Linear Regression (MLR)) and three Artificial Intelligence models (Artificial Neural Network (ANN), Random Forest Regression (RFR), and K-Neighbor Network (KNN)) by employing Rock Mass Rating (RMR89) and Point load index (I50) as appropriate input variables selected through correlation matrix analysis among eight different variables to propose an appropriate model for the prediction of Em. The efficacy of each predictive model was evaluated by using four different performance indicators: performance coefficient R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Median Absolute Error (MEAE). The results show that the R2, MAE, MSE, and MEAE for the ANN model are 0.999, 0.2343, 0.2873, and 0.0814, respectively, which are better than MLR, KNN, and RFR. Therefore, the ANN model is proposed as the most appropriate model for the prediction of Em. The findings of this research will provide a better understanding and foundation for the professionals working in fields during the prediction of various engineering parameters, especially Em for sustainable engineering design in the rock engineering field.

Uniaxial compressive strength (UCS) and the static Young’s modulus (Es) are fundamental parameters for the effective design of engineering structures in a rock mass environment. Determining these two parameters in the laboratory is time-consuming and costly, and the results may be inappropriate if the testing process is not properly executed. Therefore, most researchers prefer alternative methods to estimate these two parameters. This work evaluates the thermal effect on the physical, chemical, and mechanical properties of marble rock, and proposes a prediction model for UCS and ES using multi-linear regression (MLR), artificial neural networks (ANNs), random forest (RF), and k-nearest neighbor. The temperature (T), P-wave velocity (PV), porosity (η), density (ρ), and dynamic Young’s modulus (Ed) were taken as input variables for the development of predictive models based on MLR, ANN, RF, and KNN. Moreover, the performance of the developed models was evaluated using the coefficient of determination (R2) and mean square error (MSE). The thermal effect results unveiled that, with increasing temperature, the UCS, ES, PV, and density decrease while the porosity increases. Furthermore, ES and UCS prediction models have an R2 of 0.81 and 0.90 for MLR, respectively, and 0.85 and 0.95 for ANNs, respectively, while KNN and RF have given the R2 value of 0.94 and 0.97 for both ES and UCS. It is observed from the statistical analysis that P-waves and temperature show a strong correlation under the thermal effect in the prediction model of UCS and ES. Based on predictive performance, the RF model is proposed as the best model for predicting UCS and ES under thermal conditions.

The effects on geotechnical properties of rock mass from the constituent particle morphology have been acknowledged since long. Particle morphology is among the most important parameters affecting the mechanical characteristics of rock, such as shear strength, cohesion, friction angle, porosity, groundwater seepage, and angle of repose. This review summarizes the current advancement in numerical simulation-related particle morphology and its impact on the mechanical behavior of rock mass. Several scholars assessed the influence of particle morphology on macroscopic mechanical response using experimental and numerical simulation techniques. The clump particle approach based on discrete elements methods (DEM) reproduces mechanisms between rock particles to illustrate the correlation of mechanical performance and particle morphology. However, this approach presents the statistical limits of particle shape and size distribution of clump particles to rock particles. Particle shape has distinct effect on geotechnical properties of rock; conversely, each descriptor is case sensitive to a specific attribute of shape. The diversity of the outcomes of the conducted study in the geotechnical field revealed an ambiguity associated with the correlation of particle size distribution with the mechanical behavior of rock and granular material. Nevertheless, further research is essential to address some of the issues that have not yet been tackled, few of which are identified throughout this review article.

The risk of a coal burst rises with the excavation depth and other mining-related activities. These devastating coal burst activities are a major concern during deep coal mining. During such activities, the loading rate is a major cause of damage. Different indexes, including the elastic strain modulus index (Wet), bursting energy index (Ke), dynamic failure time index (DT), and compressive strength index (Rc), are used for coal bursting intensity; however, the loading rate and damage factors are not included in these indexes. In this study, a new coal bursting liability index called the elastic modulus damage index (EMDI) was developed using rock damage variables and the elastic strain modulus index, and is based on energy evolution characteristics under different loading rates. The results of this new index were compared with the existing indexes, and their range was proposed to evaluate coal bursting liability. The EDMI shows a positive polynomial second order degree relationship with Wet and Ke, having a determination factor of 0.99, while DT shows a negative polynomial second order degree relationship with a determination factor of 0.94. The EDMI and Rc show a positive power relationship having a determination factor of 0.99. The relationships with other indexes revealed that the EDMI can be effectively used in evaluating the coal bursting liabilities in different stress environments. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. Funding: The authors would like to thank the YUTP under grant numbers 015LC0-150 and 015LC0-326 for providing financial assistance.

During the stress-induced rock failure process, anomalies in the Infrared Radiation (IR) can be observed. To study the response relationships between rock IR temperature and spatial-temporal evolution process of cracks in sandstone, the IR and Acoustic Emission (AE) were both measured during uniaxial compression. The multi-frames accumulation average technology and background thermal noise correction method were used to correct the IR temperature. According to the AE results, the temporal evolution process of cracks was divided into six stages: initial crack closure, linear elastic deformation, micro-crack development, macro-crack initiation, macro-crack propagation, and macro-crack coalescence. It was found that the changing rate of the Average Infrared Radiation Temperature (AIRT) changed with the crack evolution stage changed, which could be regarded as a basis for dividing the evolution stages of rock cracks using IR. The accumulated energy density map of AE events was used to analyze the spatial distribution characteristics of cracks and determine the Low Damage Area (LDA) and High Damage Area (HDA) of the rock samples. It was found that the AIRT variations were consistent between LDA and HDA during the entire loading process. However, since the stage of micro-cracks development, the difference value in AIRT between LDA and HDA gradually increased with time. The research results can provide a reference for the study of the rock crack evolution process with the surface IR information and are helpful to the early warning of rock failure.

The influence of acid solutions was investigated on the mechanical properties of mudstone. Uniaxial compression tests on mudstone samples were conducted to determine the variations of relative mass, porosity, deformation, and strength characteristics of mudstone subjected to acidic solutions with different pH values. The change of pH, relative mass, and porosity of mudstone in the process of acid solution immersion was monitored during soaking. The mechanism of hydrochemical corrosion of mudstone samples was preliminarily discussed. The damage parameter was introduced based on the porosity rate. The results show that with increased solution acidity, the peak stress and elastic modulus decreased to different levels, while the peak strain increases in the rock samples. The increased chemical damage parameters reduce the mechanical parameters and increased the deformation parameters. On the basis of the mechanical test, considering the stress-strain relationship of rock in the compaction stage, a segmented damage constitutive model of rock based on chemical damage parameters is established, and the test results are verified. The results show that the correlation coefficient between the theoretical curve and the experimental data is as high as 0.98, and the model is suitable for the analysis of chemically corroded rock under the uniaxial compression test. The results provide a reference for the analysis and design of coal-bed methane wells where the rocks frequently become acidic during the production of methane.

The Infrared Radiation (IR) on the surface of a rock being loaded will change significantly during its failure process. To effectively monitor and predict rock failure by the IR method, in this paper, the Infrared Radiation Variation Temperature Field (IRVTF) of rock under uniaxial compression was identified. It was found that some IRVTF thermal images were characterized by obvious temperature rise in the later stage of rock loading. The temperature distribution characteristics of IRVTF were analyzed. It was observed that the temperature of the IRVTF exhibited the logarithmic linear distribution within the selected statistical temperature range, with an average coefficient of correlation greater than 0.96. On this basis, the index of “Infrared Radiation b-value (bIR-value)” of rock under uniaxial compression was defined for the first time, and the threshold for identifying its sudden change was proposed. It was found that the sudden changes of bIR-value could be observed with the generation of large-scale micro-cracks. The average amplitude of the bIR-value at first sudden change for all samples was 16.58 times of the mean amplitude before the sudden change on average, which indicated the significance of the bIR-value sudden change. The first sudden change of the bIR-value was 22.5 s ahead of the peak stress on average, which could be used as a precursor of rock failure and instability. The findings of the research can help with the monitoring and early warning of surrounding rock instability in mines, tunnels, and other geotechnical projects.

The rock failure process is always accompanied by dilatancy. This study investigated the average infrared radiation temperature (AIRT) characteristics in the rock dilatancy process. An Infrared Radiation (IR) dilatancy precursor point was found prior to the failure of the rocks under load. During the uniaxial loading process, the stress levels of upward-AIRT and downward-AIRT rock samples at the initial point of rock dilatancy were 0.74 σmax and 0.80 σmax, respectively. After rock dilatancy, the AIRT change rate of upward-AIRT sample increased by 1.48 × 10−4°C⋅s−1, and AIRT change rate of downward AIRT sample dropped by 1.29 × 10−4°C⋅s−1. From the initial point to the process reached rock failure, the AIRT change was almost linearly correlated with volumetric strain increment. Based on the IR thermogram, the IR hot spot was defined, and a new index—high temperature point scaling factor (HTPSF)—was proposed for qualitative analysis. The calculated HTPSF values of upward-AIRT and downward-AIRT rock samples at the IR dilatancy precursor point were 0.4 and 0.6, respectively. This study provides a new thought to determine the initial point of rock dilatancy.

The problem of water resources damage caused by coal mining has restricted the sustainable development of Yu-Shen mining area. Illustrating the relationship between mining and water resources carrying capacity is of great significance to solve this problem. In this study, the authors proposed an appraisal and prediction model of water resource carrying capacity in the mining area (WRCCMA) based on the analytic hierarchy process (AHP)-fuzzy comprehensive evaluation method. A triple-leveled structure model was developed, and the main influencing factors of the WRCCMA and the membership functions were analyzed. The prediction model was applied to Yubujie colliery to test its validity by investigating the changes of vegetation coverage and the ground deformation of the colliery and its adjacent coal mine before and after mining. Subsequently, we obtained the WRCCMA of the study area and zoning map of different grades of WRCCMA in the mining area by applying this model to the whole Yu-Shen mining area. Furthermore, three countermeasures to maintain the WRCCMA and realize water conservation coal mining (WCCM) were provided to collieries with different WRCCMA grades, including mining methods selection, mine water reutilization, and water-resisting layer reconstruction. Reasonable mining methods and water-resisting layer reconstruction can reduce the development of water conductive fractures and thus prevent groundwater from penetrating into the goaf. Mine water reutilization provides a source of water demand for collieries and families, contributing to the reduction of abstraction of water resources. These three countermeasures can help to maintain the WRCCMA. This paper successfully combines the fuzzy theory with mining engineering and provides theoretical and practical guidance for other mining areas in arid and semi-arid regions of Northwest China.

Water saturated rock strain energy evolution was measured using infrared radiation (IR) under uniaxial load of sandstone samples. The variation in the characteristics of total strain, elastic strain, dissipated strain energies and maximum IR of saturated rock under stress were analyzed. The results reveal the degree of saturation has a significant effect on rock strain energy. The total strain and elastic strain energies correlated linearly negatively, dissipative strain energy were correlated exponentially negatively, and maximum infrared radiation variance (IRV) correlated quadratically at peak stress point with different degree of saturation of rock samples. The relationship between total strain, elastic strain energies, and ΔIRV was a linear function under different saturated conditions. It was found that there was a linear energy transfer and linear energy storage function in the rock. Based on linear energy transfer and storage, two coefficient in the presence of IR were introduced, i.e., the Total Energy Infrared Radiation Coefficient (TEIRC) and Energy Storage Infrared Radiation Coefficient (ESIRC). These can be used with variation in infrared radiation variance (ΔIRV) to determine total strain and elastic strain energies of saturated rock. Furthermore, a quantitative analysis index of energy dissipation infrared radiation ratio (EDIRR), i.e. the ratio of dissipative strain energy to ΔIRV, has been proposed which can be used to predict and identify the failures of saturated rock. The inflection point of rock EDIRR from horizontal to rapid growth can be regarded as the precursor point for rock failure, and the EDIRR mutation after the precursor point can be used as the new criterion to monitor rock failure and instability.

Limestone is the main constituent as raw material used in manufacturing of cement. In this study the limestone deposits of Khyber Karimabad, Passu and Morkhun-Gircha Upper Hunza Valley, Gilgitbaltistan were evaluated for its suitability to be used raw material for cement industry through chemically and mineralogical techniques. The representative limestone's samples from different geological beds of the proposed area were collected. These samples were investigated petrographic study, X-ray diffraction (XRD), Scanning electron microscopy coupled with X-ray diffraction (SEM-EDX), X-ray fluorescence (XRF) and reserve estimation of the proposed deposit was carried out using Geological Information System (GIS). Geological review shows that there is a good potential for industrial-grade limestone, the composition of this limestone can be generally expressed in terms of CaO, MgO, Al2O3, and SiO2, that may be used in place of similar commodities of good quality cement raw materials. After analysis of results, it is concluded that limestone and other raw materials of Khyber Karimabad (KKLS) are is of good quality and can fulfills the international standards of cement and while limestone and other raw materials of Passu and Morkhun-Gircha is not suitable to be used as raw material for cement industry.