Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.

Effects of mineralogical composition on uniaxial compressive strengths of sedimentary rocks

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 can help understand the effects of mineralogical composition on the rock strengths,explain the contrasts in the rock strength of the responses to the same mineral content(e.g.,the quartz content),and provide another indirect method for evaluating the rock strength of common sedimentary rocks.

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

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.

Inconsistency of changes in uniaxial compressive strength and P-wave velocity of sandstone after temperature treatments

It is generally accepted that the uniaxial compressive strength(UCS)and P-wave velocity of rocks tend to decrease simultaneously with increasing temperature.However,based on a great number of statistical data and systematic analysis of the microstructure variation of rocks with temperature rising and corresponding propagation mechanism of elastic wave,the results show that(1)There are three different trends for the changes of UCS and P-wave velocity of sandstone when heated from room temperature(20C or 25C)to 800C:(i)Both the UCS and P-wave velocity decrease simultaneously;(ii)The UCS increases initially and then decreases,while the P-wave velocity decreases continuously;and(iii)The UCS increases initially and then fluctuates,while the P-wave velocity continuously decreases.(2)The UCS changes at room temperaturee400C,400Ce600C,and 600Ce800C are mainly attributed to the discrepancy of microstructure characteristics and quartz content,the transformation plasticity of clay minerals,and the balance between the thermal cementation and thermal damage,respectively.(3)The inconsistency in the trends of UCS and P-wave velocity changes is caused by the change of quartz content,phase transition of water and certain minerals.

Evaluation of empirical estimation of uniaxial compressive strength of rock using measurements from index and physical tests

The uniaxial compressive strength(UCS) of rock is an important parameter required for design and analysis of rock structures,and rock mass classification.Uniaxial compression test is the direct method to obtain the UCS values.However,these tests are generally tedious,time-consuming,expensive,and sometimes impossible to perform due to difficult rock conditions.Therefore,several empirical equations have been developed to estimate the UCS from results of index and physical tests of rock.Nevertheless,numerous empirical models available in the literature often make it difficult for mining engineers to decide which empirical equation provides the most reliable estimate of UCS.This study evaluates estimation of UCS of rocks from several empirical equations.The study uses data of point load strength(Is(50)),Schmidt rebound hardness(SRH),block punch index(BPI),effective porosity(n) and density(ρ)as inputs to empirically estimate the UCS.The estimated UCS values from empirical equations are compared with experimentally obtained or measured UCS values,using statistical analyses.It shows that the reliability of UCS estimated from empirical equations depends on the quality of data used to develop the equations,type of input data used in the equations,and the quality of input data from index or physical tests.The results show that the point load strength(Is(50)) is the most reliable index for estimating UCS among the five types of tests evaluated.Because of type-specific nature of rock,restricting the use of empirical equations to the similar rock types for which they are developed is one of the measures to ensure satisfactory prediction performance of empirical equations.

Random field characterization of uniaxial compressive strength and elastic modulus for intact rocks

Rock properties exhibit spatial variabilities due to complex geological processes such as sedimentation,metamorphism, weathering, and tectogenesis. Although recognized as an important factor controlling the safety of geotechnical structures in rock engineering, the spatial variability of rock properties is rarely quantified. Hence, this study characterizes the autocorrelation structures and scales of fluctuation of two important parameters of intact rocks, i.e. uniaxial compressive strength(UCS) and elastic modulus(EM).UCS and EM data for sedimentary and igneous rocks are collected. The autocorrelation structures are selected using a Bayesian model class selection approach and the scales of fluctuation for these two parameters are estimated using a Bayesian updating method. The results show that the autocorrelation structures for UCS and EM could be best described by a single exponential autocorrelation function. The scales of fluctuation for UCS and EM respectively range from 0.3 m to 8.0 m and from 0.3 m to 8.4 m.These results serve as guidelines for selecting proper autocorrelation functions and autocorrelation distances for rock properties in reliability analyses and could also be used as prior information for quantifying the spatial variability of rock properties in a Bayesian framework.

Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were determined.To predict the UCS,simple regression(SRA),multiple regression(MRA),artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and genetic expression programming(GEP)have been utilized.The obtained UCS values were compared with the actual UCS values with the help of various graphs.Datasets were modeled using different methods and compared with each other.In the study where the performance indice PIat was used to determine the best performing method,MRA method is the most successful method with a small difference.It is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA,while these values are 2.44,2.33,and 2.22 for GEP,ANFIS,and ANN techniques,respectively.The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others.According to the performance index assessment,the weakest model among the nine model is P7,while the most successful models are P2,P9,and P8,respectively.

Predicting uniaxial compressive strength of serpentinites through physical,dynamic and mechanical properties using neural networks

The uniaxial compressive strength(UCS)of intact rock is one of the most important parameters required and determined for rock mechanics studies in engineering projects.The limitations and difficulty of conducting tests on rocks,specifically on thinly bedded,highly fractured,highly porous and weak rocks,as well as the fact that these tests are destructive,expensive and time-consuming,lead to development of soft computing-based techniques.Application of artificial neural networks(ANNs)for predicting UCS has become an attractive alternative for geotechnical engineering scientists.In this study,an ANN was designed with the aim of indirectly predicting UCS through the serpentinization percentage,and physical,dynamic and mechanical characteristics of serpentinites.For this purpose,data obtained in earlier experimental work from central Greece were used.The ANN-based results were compared with the experimental ones and those obtained from previous analysis.The proposed ANN-based formula was found to be very efficient in predicting UCS values and the samples could be classified with simple physical,dynamic and mechanical tests,thus the expensive,difficult,time-consuming and destructive mechanical tests could be avoided.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

Effects of Loading Rate on Flexural-tension Properties and Uniaxial Compressive Strength of Micro-surfacing Mixture

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.

Estimating uniaxial compressive strength of rocks using genetic expression programming

The aim of this paper is to estimate the uniaxial compressive strength（UCS） of rocks with different characteristics by using genetic expression programming（GEP）.For this purpose,five different types of rocks including basalt and ignimbrite（black,yellow,gray,brown） were prepared.Values of unit weight,water absorption by weight,effective porosity and UCS of rocks were determined experimentally.By using these experimental data,five different GEP models were developed for estimating the values of UCS for different rock types.Good agreement between experimental data and predicted results is obtained.

Estimate of Uniaxial Compressive Strength of Hydrothermally Altered Soft Rocks Based on Strength Index Tests

The purpose of this study was to clarify the relationships between results of index tests and uniaxial compressive strength (UCS) in hydrothermally altered soft rocks of the Upper Miocene, which are typical of the soft rock found in northeastern Hokkaido, Japan. Index tests were performed using point load testing machine and needle penetrometer with irregular lump specimens under forced-dry, forced-wet, and natural-moist states. The relationships between irregular lump point load strength (IPLS) index and UCS, and needle penetration (NP) index and UCS were “UCS = approximately 19 IPLS index” and “UCS = 0.848 (NP index)0.619”, respectively, in soft rocks with a UCS below 25 MPa. These relationships could be applied to on-site tests of rocks with natural moisture content. The UCS could be calculated from IPLS and NP tests on soft rocks only when UCS was below 25 MPa, using the equations obtained as a result of this study.

Strength and failure characteristics of sandstone containing two circular holes filled with two types of inclusions under uniaxial compression

Plate shaped sandstones containing two fabricated circular holes that were filled with gypsum and high-strength concrete respectively were prepared for studying the effects of ligament length L ligament incline angle α, as well as filling modes on their strength properties and failure modes. The results show that the initial cracks can be categorized as wing crack, axial tensile crack and curved tensile crack. The failure modes of ligaments can be categorized as mode of single inclined crack, mode of single axial crack and mode of two parallel cracks. The final failure modes of all specimens can be categorized as the tension-shear mixed failure and shear failure. The strength of inclusions shows little influence on the final failure modes of specimens, while the failure modes vary with L and α. When α is a fixed value, the peak strength σc and elastic modulus Ec of tested specimens increase firstly with increasing L and reaches to the maximum value at L of 16 mm, then declines. When L is a fixed value, σc declines firstly and then turns to increase as α increases to 75° from 45°, while Ec increases linearly. The axial stress σp performs the similar variation trends with those of σc versus increasing L and α when ligaments fail.

Revisiting factors contributing to the strength of cemented backfill support system:A review

This paper provides a review of the intrinsic and extrinsic factors affecting the uniaxial compressive strength(UCS)of cemented tailings backfill(CTB).The consideration is that once CTB is poured into underground stopes,its strength is heavily influenced by factors internal to the CTB as well as the surrounding mining environments.Peer-reviewed journal articles,books,and conference papers published between 2000 and 2022 were searched electronically from various databases and reviewed.Additional sources,such as doctoral theses,were obtained from academic repositories.An important finding from the review is that the addition of fibers was reported to improve the UCS of CTB in some studies while decrease in others.This discrepancy was accounted to the different properties of fibers used.Further research is therefore needed to determine the“preferred”fiber to be used in CTB.Diverging findings were also reported on the effects of stope size on the UCS of CTB.Furthermore,the use of fly ash as an alternative binder may be threatened in the future when reliance on the coal power declines.Therefore,an alternative cementitious by-product to be used together with furnace slag may be required in the future.Finally,while most studies on backfill focused on single-layered structures,layered backfill design models should also be investigated.

Strength characteristics of rock anchored by NPR bolt with different preloads

This study compares the strength characteristics of rocks anchored by NPR bolts and ordinary bolts with varied preloads,based on the mechanical properties of NPR bolts(with a negative Poisson’s ratio).The results show that the uniaxial compressive stress-strain curve of ordinary anchored rocks exhibits noticeable abrupt changes.After reaching peak strength,the bolt breaks,whereas the stress-strain curve of NPR-anchored rocks is smoother.The NPR bolt enters the stage of continuous resistance after reaching maximal strength and does not break.As the preload increases,the strength of the anchored rock grows linearly.A calculation equation for the strength of the anchored rock is proposed based on the preload.The theoretical equation fits the test results well,and the fitted parameters show that NPR bolts can better increase the strength of the rock.The concept of dynamic toughness UC of anchored rock is proposed to reflect the comprehensive mechanical properties of anchored rock,including strength and plasticity.As the preload increases,the UC of ordinary anchored rock first decreases and then increases,while the UC of the NPR anchored rock does not change significantly with the preload when the strain is small,and the UC increases with the increase of the preload when the strain is large.

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

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.

Study on Strength Reduction Law and Meso-Crack Evolution of Lower Layered Cemented Tailings Backfill

The green disposal of tailings solid waste is a problem to be solved in mine production.Cemented tailings filling stoping method can realize the dual goals of solid waste resource utilization and mined-out area reduction.However,the volume of the mined-out area of the open-pit method is larger than the filling capacity,resulting in the complex stratification of the underground backfill,and the strength of the backfill cannot meet the requirements.In this paper,according to the delamination situation,the specimens of horizontal and inclination angle layered cemented tailings backfill(LCTB)is made for a uniaxial compression test,and the failure process of delamination backfill is reduced by PFC.The results show that the corresponding reduction factorφof horizontal LCTB is 0.560–0.932,and the correspondingφvalue of inclination angle LCTB is 0.338–0.772.The failure mode of backfill in different layers is mainly manifested as a tensile failure.The PFC numerical simulation results are consistent with laboratory test results,which verifies the correctness of backfill failure.The research results provide a reliable theoretical basis for the strength design of backfill in goaf,which is of great significance for solid waste utilization and environmental protection.

化学溶液长期浸泡作用下灰岩溶蚀劣化研究

在酸性(pH=3)、中性(pH=7)、碱性(pH=11)3种溶液环境中开展了灰岩浸泡溶蚀试验,测试了不同浸泡周期灰岩的单轴抗压强度、表面微观形貌、浸泡溶液的pH值及Ca^(2+)浓度,并讨论了灰岩宏观力学性能的劣化机制。研究表明:灰岩在酸性环境中溶蚀劣化程度最大,中性环境次之,碱性环境最小;在试验初期,3种化学溶液的pH值均变化剧烈,并逐渐向中性转化,最终呈弱碱性;在酸性溶液中试验初期Ca^(2+)溶出速率较快;而在中性和碱性环境下则溶蚀反应缓慢;随着浸泡时间的增加,溶蚀灰岩在微观形貌上表现为微孔洞扩展,表面粗糙程度加剧;从溶蚀后灰岩微观损伤区域的统计结果来看,3种溶液环境下灰岩的单轴抗压强度均随损伤区域比的增加而减小,酸性环境中的损伤区域比最大,中性环境次之,碱性环境最小。

热压型煤成型条件优化试验研究方法

针对当前煤矿瓦斯动力学物理模拟试验中型煤材料存在低强度和高渗透率问题,建立了一套热压型煤成型条件优化试验研究方法。首先,自主研发了热压型煤试验系统,并对试验系统优势和今后改进方向进行了汇总,同时基于Horsfield致密堆积理论创建了型煤材料最优配制方案,最后形成了以马氏距离度量法和黄金分割法相结合的成型条件优化方法。为了验证试验方法的效果,通过控制成型温度为311.8℃、升温速率为5℃/min和保温时间为5.3 h,开展了不同成型压力条件下热压型煤试验研究,研究了不同成型压力条件下的热压型煤微观结构、物理力学特性和渗透特性等响应特征。结果表明:增加成型压力,总孔隙度逐渐减小,单轴抗压强度呈先增大后减少的变化趋势,破坏形式以块状剥落和纵向破裂为主,初始渗透率呈先减小后增大、最小渗透率则呈先减后增再减的变化趋势。以各成型条件的具体数值为试验点、热压型煤和原煤的关键参数为评价参量构建样本矩阵,计算各成型条件下热压型煤和原煤之间的马氏距离,再结合黄金分割法对试验区间进行优化求解,优化后的最佳成型压力为80 MPa,在此成型条件下制作的热压型煤密度、单轴抗压强度和初始渗透率分别为1.137 g/cm^(3)、12.21 MPa、1.32×10^(-15)m^(2),与原煤的1.132 g/cm^(3)、12.83 MPa、1.08×10^(-15)m^(2)相似性极高,达到了提高型煤强度、降低型煤渗透率的目的。