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Statistical analysis of physico-mechanical parameters of sandstones occurring in orogenic settings

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Journal of Mountain Science 2024年第4期21卷 1388-1402页

作者： PAPPALARDO Giovanna CARBONE Serafina MONACO Carmelo ZOCCO Giordana MINEO Simone Dipartimento Di Scienze Biologiche Geologiche E AmbientaliUniversitàDegli Studi Di CataniaCorso Italia 5795129 CataniaItaly

In northeastern Sicily(Italy),sandstone rock masses widely crop out as cover deposits over crystalline terrains belonging to the orogenic belt.Despite being part of the same geological formation,these sandstones are characterized by highly different features in terms of texture and physico-mechanical properties.This poses a scientific question on the possibility of tracing these rocks to a single statistical model,which could be representative of their main engineering geological properties.Therefore,it is worth investigating on the possible reasons of such differences,that should be searched either in the current geographical sandstone distribution or in the rock texture.For this study,sandstone samples were collected from different sites and were analyzed at both the hand and thin section scales.Three sandstone types were recognized,characterized by a different texture.Then,the laboratory characterization allowed estimating their main physico-mechanical and ultrasonic properties,such as porosity,density,mechanical strength,deformability,and ultrasonic velocities.The rock mechanical strength proved linked to the rock compactness and to the presence of lithic fragments,while pores and a pseudo-matrix between grains represent weakening features.Rock data were also statistically analyzed by grouping the specimens according to a geographical criterion,with respect to their sampling area,but no link was found between location and rock properties.Finally,with the aim of achieving mathematical laws that could be used to predict some rock properties from others,useful for practical purposes when dealing with such a high property variability,single and multiple regression analyses were carried out.Results show that the uniaxial compressive strength,porosity,and P-wave velocity are the best predictors for a quick,indirect estimation of the main physico-mechanical parameters.The methodological approach developed for this research can be taken as reference to study other worldwide cases,involving rocks characterized by a wi

关键词： Sandstone Laboratory test uniaxial compressive strength Porosity Rocks

Estimation of rock uniaxial compressive strength for an Iranian carbonate oil reservoir: Modeling vs. artificial neural network application

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Petroleum Research 2018年第4期3卷 336-345页

作者： Maryam Hassanvand Siyamak Moradi Moslem Fattahi Ghasem Zargar Mosayyeb Kamari Department of Petroleum Engineering Abadan Faculty of Petroleum EngineeringPetroleum University of TechnologyAbadanIran Department of Chemical Engineering Abadan Faculty of Petroleum EngineeringPetroleum University of TechnologyAbadanIran National Iranian South Oil Company AhvazIran

Estimation of rock mechanic parameters is an important issue in reservoir management.uniaxial compressive strength(UCS)and elastic modulus are the most important factors in determining the rock mechanic parameters in petroleum engineering studies.Accessibility to the parameters in fields such as designing fracture,analyzing of wellbore stability and drilling programming are very useful.The most accurate method to assign the aforementioned parameters is measuring these parameters in a laboratory.Laboratory determination of these parameters is problematic work due to technology issues,lack of laboratory equipment and coring problems in oil and gas wells,so indirect estimation of these parameters is required.Using well log data is the cheapest and most available approach in order to indirectly estimate these parameters.In this investigation,different models including multiple linear regression(MLR)and artificial neural network(ANN)(i.e.,multi linear perceptron(MLP)and radial basis function(RBF))were utilized for prediction of UCS via the three parameters of porosity,density and water saturation.These data were obtained from analysis of sonic,neutron,gamma ray and electric logs.The best results were obtained from a 3-15-1 MLP network which included one hidden layer and 15 neurons from the hidden layer using the trial and error method,and a 3-17-1 RBF which included 17 hidden neurons and a spread ò of 1.6.The core data from one of the carbonate Iranian oil fields(Asmari reservoir)were utilized for training,validation and testing of the networks,and correlation coefficients of 0.68,0.90 and 0.83 were obtained for MLR,MLP and RBF,respectively.

关键词： uniaxial compressive strength Artificial neural network Porosity Linear regression Water saturation

Effects of Loading Rate on Flexural-tension Properties and uniaxial compressive strength of Micro-surfacing Mixture

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Journal of Wuhan University of Technology(Materials Science) 2010年第4期25卷 656-658页

作者：陈筝 School of Materials Science & Engineering Xi'an University of Architecture & Technology

The major objective of this research was to discuss the effects of loading rate on the flexural-tension properties and uniaxial compressive strength of micro-surfacing mixture using three-point bending test and uniaxial compressive test respectively. As a preventive maintenance surface treatment on asphalt pavement, micro-surfacing was formed on the basis of the ISSA recommendation of an optimum micro-surfacing design. Tests were conducted over a wide range of temperature to investigate the difference of properties from low loading rate to a relatively high loading rate. Three-point bending test was used to study the flexural strength, strain and modulus of micro-surfacing mixture, and uniaxial compressive test was carried out to obtain the relationship between strength and the loading rate as well as temperature. The experimental results showed that flexural strength at high loading rate was larger than that at low loading rate. The flexural strength difference between low and high loading rate enlarged when the temperature rose. The flexural strain at high loading rate increased compared with results of the low loading rate. Results of the flexural modulus revealed that micro-surfacing mixture exhibited better anti-cracking characteristic at low temperature when given a relatively low loading rate. Results of uniaxial compressive test revealed that the strength difference of micro-surfacing among different loading rates increased with the increase of temperature. The logarithm relationship between the strength and loading rate over a wide range of temperature was obtained to compare the experimental and predicted values, which resulting in a reasonable consistency.

关键词： loading rate flexural-tension properties uniaxial compressive strength micro-surfacing

Prediction of uniaxial compressive strength of rock based on lithology using stacking models

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Rock Mechanics Bulletin

Rock Mechanics Bulletin 2023年第4期2卷 56-69页

作者： Zida Liu Diyuan Li Yongping Liu Bo Yang Zong-Xian Zhang School of Resources and Safety Engineering Central South UniversityChangsha410083China Nickel Cobalt Research and Design Institute Jinchuan Group Co.Ltd.Jinchang737104China Oulu Mining School University of OuluOuluFinland

uniaxial compressive strength(UCS)of rock is an essential parameter in geotechnical engineering.Point load strength(PLS),P-wave velocity,and Schmidt hammer rebound number(SH)are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS.This study collected 1080 datasets consisting of SH,P-wave velocity,PLS,and UCS.All datasets were integrated into three categories(sedimentary,igneous,and metamorphic rocks)according to lithology.Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types.Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types.UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks.Nonetheless,the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks.The developed predictive models can be applied to quickly predict UCS at engineering sites,which benefits the rapid and intelligent classification of rock masses.Moreover,the importance of SH,P-wave velocity,and PLS were analyzed for the estimation of UCS.SH was a reliable indicator for UCS evaluation across various rock types.P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks,but it was not reliable for assessing the UCS of metamorphic rocks.

关键词： uniaxial compressive strength Point load strength P-wave velocity Schmidt hammer rebound number Stacking models

Effects of mineralogical composition on uniaxial compressive strengths of sedimentary rocks

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Petroleum Science 2023年第5期20卷 3062-3073页

作者： Zhen-Liang Chen Huai-Zhong Shi Chao Xiong Wen-Hao He Hai-Zhu Wang Bin Wang Nikita Dubinya Kai-Qi Ge Beijing Key Laboratory of Optical Detection Technology for Oil and Gas China University of Petroleum-BeijingBeijing102249China State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum-BeijingBeijing102249China Basic Research Center for Energy Interdisciplinary College of ScienceChina University of PetroleumBeijing102249China Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences 123242MoscowRussia Science and Technology Center of Geophysics and Mineral Resources Moscow Institute of Physics and Technology141701DolgoprudnyRussia Shale Gas Exploration and Development Project Manager Department CNPC Chuanqing Drilling Engineering Co.Ltd.ChengduSichuan610051China

Figuring out rock strength plays essential roles in the sub ground mining activities,such as oil and gas well drilling and hydraulic fracturing,coal mining,tunneling,and other civil engineering scenarios.To help understand the effects of the mineralogical composition on evaluating the rock strength,this research tries to establish indirect prediction models of rock strength by specific input mineral contents for common sedimentary rocks.Using rock samples collected from the outcrops in the Sichuan Basin,uniaxial compression tests have been conducted to sandstone,carbonate,and shale cores.Combining with statistical analysis,the experimental data prove it true that the mineralogical composition can be utilized to predict the rock strength under specific conditions but the effects of mineralogical composition on the rock strength highly depend on the rock lithologies.According to the statistical analysis results,the predicted values of rock strengths by the mineral contents can get high accuracies in sandstone and carbonate rocks while no evidences can be found in shale rocks.The best indicator for predicting rock strength should be the quartz content for the sandstone rocks and the dolomite content for the carbonate rocks.Especially,to improve the evaluation accuracy,the rock strengths of sandstones can be obtained by substituting the mineral contents of quartz and clays,and those of carbonates can be calculated by the mineral contents of dolomite and calcite.Noticeably,the research data point out a significant contrast of quartz content in evaluating the rock strength of the sandstone rocks and the carbonate rocks.Increasing quartz content helps increase the sandstone strength but decrease the carbonate strength.As for shale rocks,no relationship exists between the rock strength and the mineralogical composition(e.g.,the clay fractions).To provide more evidences,detailed discussion also provides the readers more glances into the framework of the rock matrix,which can be further studied in the future.These findings

关键词： uniaxial compressive strength Quartz content Clay Sandstone Carbonate Shale

compressive fatigue behavior and failure evolution of additive fiber-reinforced cemented tailings composites

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International Journal of Minerals,Metallurgy and Materials 2022年第2期29卷 345-355页

作者： Jiajian Li Shuai Cao Erol Yilmaz Yunpeng Liu School of Civil and Resources Engineering University of Science and Technology BeijingBeijing 100083China State Key Laboratory of High-Efficient Mining and Safety of Metal Mines of Ministry of Education University of Science and Technology BeijingBeijing 100083China Department of Civil Engineering Geotechnical DivisionRecep Tayyip Erdogan UniversityFenerRize TR53100Turkey State Key Laboratory of Silicate Materials for Architectures(Wuhan University of Technology) Wuhan 430070China

The ordinary cemented tailings backfill(CTB)is a cement-based composite prepared from tailings,cementitious materials,and water.In this study,a series of laboratory tests,including uniaxial compression,digital image correlation measurement,and scanning electron microscope characteristics of fiber-reinforced CTB(FRCTB),was conducted to obtain the uniaxial compressive strength(UCS),failure evolution,and microstructural characteristics of FRCTB specimens.The results show that adding fibers could increase the UCS values of the CTB by 6.90%to 32.76%.The UCS value of the FRCTB increased with the increase in the polypropylene(PP)fiber content.Moreover,the reinforcement effect of PP fiber on the CTB was better than that of glass fiber.The addition of fiber could increase the peak strain of the FRCTB by0.39%to 1.45%.The peak strain of the FRCTB increased with the increase in glass fiber content.The failure pattern of the FRCTB was coupled with tensile and shear failure.The addition of fiber effectively inhibited the propagation of cracks,and the bridging effect of cracks by the fiber effectively improved the mechanical properties of the FRCTB.The findings in this study can provide a basis for the backfilling design and optimization of mine backfilling methods.

关键词： cemented tailings backfill uniaxial compressive strength combined fiber reinforcement digital image correlation microstructural characteristics

Development of 3D top coal caving angle model for fully mechanized extra-thick coal seam mining

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International Journal of Mining Science and Technology 2022年第5期32卷 1145-1152页

作者： Hongfei Duan Lijuan Zhao Haiyan Yang Yao Zhang Hongchun Xia China University of Mining and Technology Xuzhou 221116China Jinneng Holding Group Taiyuan 030000China College of Coal Engineering Shanxi Datong UniversityDatong 037000China College of Civil and Architectural Engineering Dalian UniversityDalian 116622China

During high-intensity,fully mechanized mining of extra-thick coal seam,the top coal would cave to a certain 3D form.Based on the data collected during drilling,a 3D model of top coal caving surface space was established to determine the relationship between the location of the stope roof and the caving surface,enabling the mathematical computation of the top caving angle(φ).The drilling method was employed to measure the top caving angle on two extra-thick fully mechanized coal caving faces under the conditions of three geological structures,namely,no geological structure,igneous rock structure,and fault structure.The results show that the value of top caving angle could be accurately estimated on-site with the 9-parameter 3D top coal caving surface model built with the drilling method.This method is a novel on-site measurement that can be easily applied.Our findings reveal that the characteristics of the coal-rock in the two mining faces are different;yet their caving angles follow the ruleφ_(igneous rock structure)uniaxial compressive strength and the top caving angle),it is found that the relationship between the two indexes satisfies an exponential decay function.

关键词： Space model Field measurement Top coal caving angle uniaxial compressive strength Exponential decay function

strength prediction model for water-bearing sandstone based on nearinfrared spectroscopy

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山地科学学报(英文) 2023年第8期20卷 2388-2404页

作者： ZHANG Xiu-lian ZHANG Fang WANG Ya-zhe TAO Zhi-gang ZHANG Xiao-yun State key laboratory for Geomechanics and Deep Underground Engineering Beijing 100083China School of Mechanics and Civil Engineering China University of Mining and TechnologyBeijing 100083China Collaborative Innovation Center for Prevention and Control of Mountain Geological Hazards of Zhejiang Province Shaoxing UniversityShaoxing 312000China Key Laboratory of Rock Mechanics and Geohazards of Zhejiang Province Shaoxing UniversityShaoxing 312000China

The strength of water-bearing rock cannot be obtained in real time and by nondestructive experiments,which is an issue at cultural relics protection sites such as grotto temples.To solve this problem,we conducted a near-infrared spectrum acquisition experiment in the field and laboratory uniaxial compression strength tests on sandstone that had different water saturation levels.The correlations between the peak height and peak area of the nearinfrared absorption bands of the water-bearing sandstone and uniaxial compressive strength were analyzed.On this basis,a strength prediction model for water-bearing sandstone was established using the long short-term memory full convolutional network(LSTM-FCN)method.Subsequently,a field engineering test was carried out.The results showed that:(1)The sandstone samples had four distinct characteristic absorption peaks at 1400,1900,2200,and 2325 nm.The peak height and peak area of the absorption bands near 1400 nm and 1900 nm had a negative correlation with uniaxial compressive strength.The peak height and peak area of the absorption bands near 2200 nm and 2325 nm had nonlinear positive correlations with uniaxial compressive strength.(2)The LSTM-FCN method was used to establish a strength prediction model for water-bearing sandstone based on near-infrared spectroscopy,and the model achieved an accuracy of up to 97.52%.(3)The prediction model was used to realize non-destructive,quantitative,and real-time determination of uniaxial compressive strength;this represents a new method for the non-destructive testing of grotto rock mass at sites of cultural relics protection.

关键词： Water-bearing sandstone Near-infrared spectroscopy Saturation degree uniaxial compressive strength Prediction model Dazu Rock Carvings

A machine learning model to predict unconfined compressive strength of alkali-activated slag-based cemented paste backfill

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第11期15卷 2803-2815页

作者： Chathuranga Balasooriya Arachchilage Chengkai Fan Jian Zhao Guangping Huang Wei Victor Liu Department of Civil and Environmental Engineering University of AlbertaEdmontonAlbertaT6G 2E3Canada

The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the relationships between a single design parameter and the UCS,independently of each other.Although machine learning(ML)methods have proven efficient in understanding relationships between multiple parameters and the UCS of ordinary Portland cement(OPC)-based CPB,there is a lack of ML research on AAS-based CPB.In this study,two ensemble ML methods,comprising gradient boosting regression(GBR)and random forest(RF),were built on a dataset collected from literature alongside two other single ML methods,support vector regression(SVR)and artificial neural network(ANN).The results revealed that the ensemble learning methods outperformed the single learning methods in predicting the UCS of AAS-based CPB.Relative importance analysis based on the bestperforming model(GBR)indicated that curing time and water-to-binder ratio were the most critical input parameters in the model.Finally,the GBR model with the highest accuracy was proposed for the UCS predictions of AAS-based CPB.

关键词： Alkali-activated slag Cemented paste backfill Machine learning uniaxial compressive strength