Unconfined compressive strength and failure behaviour of completely weathered granite from a fault zone

Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.

Effect of compressive strength on the performance of the NEMO-LIM model in Arctic Sea ice simulation

Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice variations remains highly challenging.For improving model performance,sensitivity experiments were conducted using the coupled ocean and sea ice model(NEMO-LIM),and the simulation results were compared against satellite observations.Moreover,the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed.The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant(C^(rhg)).By reducing the C^(rhg) constant,the sea ice compressive strength increases,leading to improved simulated sea ice states.The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength.Meanwhile,dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration,reducing the simulation bias in the central Arctic Ocean in summer.The root mean square error(RMSE)between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution.The ice thickness,especially of multiyear thick ice,was also reduced and matched with the satellite observation better in the freezing season.These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.

Failure behavior and strength model of blocky rock mass with and without rockbolts

To better understand the failure behaviours and strength of bolt-reinforced blocky rocks,large scale extensive laboratory experiments are carried out on blocky rock-like specimens with and without rockbolt reinforcement.The results show that both shear failure and tensile failure along joint surfaces are observed but the shear failure is a main controlling factor for the peak strength of the rock mass with and without rockbolts.The rockbolts are necked and shear deformation simultaneously happens in bolt reinforced rock specimens.As the joint dip angle increases,the joint shear failure becomes more dominant.The number of rockbolts has a significant impact on the peak strain and uniaxial compressive strength(UCS),but little influence on the deformation modulus of the rock mass.Using the Winkler beam model to represent the rockbolt behaviours,an analytical model for the prediction of the strength of boltreinforced blocky rocks is proposed.Good agreement between the UCS values predicted by proposed model and obtained from experiments suggest an encouraging performance of the proposed model.In addition,the performance of the proposed model is further assessed using published results in the literature,indicating the proposed model can be used effectively in the prediction of UCS of bolt-reinforced blocky rocks.

Prediction Model of Compressive Strength of Fly Ash-Slag Concrete Based on Multiple Adaptive Regression Splines

Accurate prediction of compressive strength of concrete is one of the key issues in the concrete industry. In this paper, a prediction method of fly ash-slag concrete compressive strength based on multiple adaptive regression splines (MARS) is proposed, and the model analysis process is determined by analyzing the principle of this algorithm. Based on the Concrete Compressive Strength dataset of UCI, the MARS model for compressive strength prediction was constructed with cement content, blast furnace slag powder content, fly ash content, water content, reducing agent content, coarse aggregate content, fine aggregate content and age as independent variables. The prediction results of artificial neural network (BP), random forest (RF), support vector machine (SVM), extreme learning machine (ELM), and multiple nonlinear regression (MnLR) were compared and analyzed, and the prediction accuracy and model stability of MARS and RF models had obvious advantages, and the comprehensive performance of MARS model was slightly better than that of RF model. Finally, the explicit expression of the MARS model for compressive strength is given, which provides an effective method to achieve the prediction of compressive strength of fly ash-slag concrete.

Modeling Wood and Fly Ash Behaviour as Partial Replacement for Cement on Compressive Strength of Self Compacting Concrete

Wood and fly ash were observed to have significant qualities that could improve the strength of self compacting concrete.The material was applied to increase the compressive strength of concrete strength.This material could be the demanding material for partial replacement for ce­ment.The study observed the behaviour of the material from experts that applied these material through experimental investigation,but the study monitored the behaviour of this material by applied modeling and simula­tion to determine other effect that could influence the behaviour of these materials in compressive strength.This was to determine the significant effect on the addictive applied as partial replacement for cement.Lots of experts have done works on fly ash through experiment concept,but the application of predictive concept has not been carried out.The adoption of this concept has expressed other parameters that contributed to the effi­ciency of wood and fly ash as partial replacement for cement on self com­pacting concrete.The study adopting modeling and simulation observed 10 and 20%by weight of cement as it is reflected on its performance in the simulation,from the simulation wood recorded 10%as it was ob­served from the growth rate of this self compacting concrete reflected from the trend.The simulation for model validation was compared with the works of the studies carried out[20].And both values developed best fits correlation.

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.

Effect of Recycled Coarse Aggregate on Concrete Compressive Strength

The effect of recycled coarse aggregate on concrete compressive strength was investigated based on the concrete skeleton theory. For this purpose, 30 mix proportions of concrete with target cube compressive strength ranging from 20 to 60 MPa were cast with normal coarse aggregate and recycled coarse aggregate from different strength parent concretes. Results of 28-d test show that the strength of different types of recycled aggregate affects the concrete strength obviously. The coarse aggregate added to mortar matrix plays a skeleton role and improves its compressive strength. The skeleton effect of coarse aggregate increases with the increasing strength of coarse aggregate, and normal coarse aggregate plays the highest, whereas the lowest concrete strength occurs when using the weak recycled coarse aggregate. There is a linear relationship between the concrete strength and the corresponding mortar matrix strength. Coarse aggregate skeleton formula is established, and values from experimental tests match the derived expressions.

Machine learning based models for predicting compressive strength of geopolymer concrete

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important properties.To facilitate engineering work on the material,an efficient predicting model is needed.In this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process.The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is ensured.The effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting process.The three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and SVM.The numerical results suggest that the DNN model generally outperforms the other two models.

Machine learning methods for predicting the uniaxial compressive strength of the rocks:a comparative study

The uniaxial compressive strength(UCS)of rocks is a critical index for evaluating the mechanical properties and construction of an engineering rock mass classification system.The most commonly used method for determining the UCS in laboratory settings is expensive and time-consuming.For this reason,UCS can be estimated using an indirect determination method based on several simple laboratory tests,including point-load strength,rock density,longitudinal wave velocity,Brazilian tensile strength,Schmidt hardness,and shore hardness.In this study,six data sets of indices for different rock types were utilized to predict the UCS using three nonlinear combination models,namely back propagation(BP),particle swarm optimization(PSO),and least squares support vector machine(LSSVM).Moreover,the best prediction model was examined and selected based on four performance prediction indices.The results reveal that the PSO–LSSVM model was more successful than the other two models due to its higher performance capacity.The ratios of the predicted UCS to the measured UCS for the six data sets were 0.954,0.982,0.9911,0.9956,0.9995,and 0.993,respectively.The results were more reasonable when the predicted ratio was close to a value of approximately 1.

Bayesian optimization-enhanced ensemble learning for the uniaxial compressive strength prediction of natural rock and its application

Engineering disasters, such as rockburst and collapse, are closely related to structural instability caused byinsufficient bearing capacity of geological materials. Uniaxial compressive strength (UCS) holds considerablesignificance in rock engineering projects. Consequently, this study endeavors to devise efficient models for theexpeditious and economical estimation of UCS. Using a dataset of 729 samples, including the Schmidt hammerrebound number, P-wave velocity, and point load index data, we evaluated six algorithms, namely AdaptiveBoosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), LightGradient Boosting Machine (LightGBM), Random Forest (RF), and Extra Trees (ET) and utilized Bayesian Optimization (BO) to optimize the aforementioned algorithms. Moreover, we applied model evaluation metrics suchas Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Variance Accounted For (VAF), Nash-SutcliffeEfficiency (NSE), Weighted Mean Absolute Percentage Error (WMAPE), Coefficient of Correlation (R), and Coefficient of Determination (R2). Among the six models, BO-ET emerged as the most optimal performer duringtraining (RMSE ¼ 4.5042, MAE ¼ 3.2328, VAF ¼ 0.9898, NSE ¼ 0.9898, WMAPE ¼ 0.0538, R ¼ 0.9955, R2 ¼0.9898) and testing (RMSE ¼ 4.8234, MAE ¼ 3.9737, VAF ¼ 0.9881, NSE ¼ 0.9875, WMAPE ¼ 0.2515, R ¼0.9940, R2 ¼ 0.9875) phases. Additionally, we conducted a systematic comparison between ensemble andtraditional single machine learning models such as decision tree, support vector machine, and K-NearestNeighbors, thus highlighting the advantages of ensemble learning. Furthermore, the enhancement effect of BO ongeneralization performance was assessed. Finally, a BO-ET-based Graphical User Interface (GUI) system wasdeveloped and validated in a Tunnel Boring Machine-excavated tunnel.

The effects of compressibility and strength on penetration of long rod and jet

The approximate compressible model is adopted to study the effects of strength and compressibility on the penetration by WHA long rod and copper jet into semi-infinite target in detail. For WHA rod penetrating PMMA at 2 km/s <V <5 km/s, the compressibility has a significant effect on the penetration efficiency. We clarify how compressibility affects the penetration efficiency by changing the stagnation pressures of the rod and target. For WHA rod penetrating 4340 Steel and 6061-T6 Al at 2 km/s < V < 10 km/s, the effect of strength is strong and the effect of compressibility is negligible at lower impact velocity, whilst the effect of strength is weak and the effect of compressibility becomes stronger at higher impact velocity. For the copper jet penetrating 4030 Steel, 6061-T6 Al and PMMA. the virtual origin model is adopted, and the compressibility and strength are implicitly considered by the linear relation between the penetration velocity and impact velocity. The effects of compressibility and target resistance on penetration efficiency are studied. The results show that the target resistance has a significant effect on the penetration efficiency. Howver PMMA is much more compressible than copper and the huge difference of compressibility has a significant effect on the penetration by hypervelocity copper jet into PMMA.

Compressive behavior of Zn-22Al closed-cell foams under uniaxial quasi-static loading

Zn-22 Al alloy closed-cell foams were fabricated by melt foaming process using hydride foaming agent. The compressive properties were investigated under quasi-static condition. The structure of the foamed material was analyzed during compression test to reveal the relationship between morphology and compressive behavior. The results show that the stress-strain behavior is typical of closed-cell metal foams and mostly of brittle type. Governing deformation mechanism at plateau stage is identified to be brittle crushing. A substantial increase in compressive strength of Zn-22 Al foams was obtained. The agreement between compressive properties and Gibson-Ashby model was also detected.

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently,many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties.Although statistical analysis is a common method for developing regression models,but still selection of suitable transformation of the independent variables in a regression model is diffcult.In this paper,a genetic algorithm(GA)has been employed as a heuristic search method for selection of best transformation of the independent variables(some index properties of rocks)in regression models for prediction of uniaxial compressive strength(UCS)and modulus of elasticity(E).Firstly,multiple linear regression(MLR)analysis was performed on a data set to establish predictive models.Then,two GA models were developed in which root mean squared error(RMSE)was defned as ftness function.Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.

Impact of Freeze-Thaw Cycles on Compressive Characteristics of Asphalt Mixture in Cold Regions

Low average temperature, large temperature difference and continual freeze-thaw （F-T） cycles have significant impacts on mechanical property of asphalt pavement. F-T cycles test was applied to illustrate the mixtures＇ compressive characteristics. Exponential model was applied to analyze the variation of compressive characteristics with F-T cycles; Loss ratio model and Logistic model were used to present the deterioration trend with the increase of F-T cycles. ANOVA was applied to show the significant impact of F-T cycles and asphalt- aggregate ratio. The experiment results show that the compressive strength and resilient modulus decline with increasing F-T cycles; the degradation is sharp during the initial F-T cycles, after 8 F-T cycles it turns to gentle. ANOVA results show that F-T cycles, and asphalt-aggregate ratio have significant influence on the compressive characteristics. Exponential model, Loss ratio model and Logistic model are significantly fitting the test data from statistics view. These models well reflect the compressive characteristics of asphalt mixture degradation trend with increasing F-T cycles.

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

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.

资源化利用粉煤灰的混凝土强度预测模型

这是一篇陶瓷及复合材料领域的论文。为粉煤灰的可资源化利用以及准确评估粉煤灰混凝土的抗压强度,基于机器学习建模技术,构建了三个混凝土抗压强度预测模型(传统线性回归模型、决策树模型和支持向量机模型),对其抗压性能进行建模预测和对比分析。首先建立了相应的实验数据库,输入参数为水泥、粉煤灰、减水剂、粗骨料、细骨料、水和养护龄期等七个参数,抗压强度为输出参数。基于10折交叉验证,通过均方根误差(RMSE)、平均绝对误差和相关系数评估了上述三个模型在训练集上的性能,并对比各个模型在测试集上的性能。结果表明:养护龄期与抗压强度存在较高的相关性(0.60),粉煤灰对抗压强度的相关性高于水泥。传统线性回归模型在训练集和测试集的RMSE分别为7.27和5.91,决策树模型分别为2.72和9.23,支持向量机模型分别为5.34和4.09。综合来看,支持向量机模型在预测粉煤灰混凝土抗压强度方面具有较好的准确性和稳健性能。研究可为采用粉煤灰的混凝土提供强度设计指导以及推进粉煤灰的可资源化利用。

基于拌和生产数据的BP神经网络混凝土抗压强度预测

为解决混凝土生产中抗压强度试验周期长及工程管理存在滞后性的问题,提出了一种基于混凝土拌和生产实时监控数据的BP神经网络混凝土抗压强度预测模型。以混凝土拌和生产中的8项物料生产称重数据和5项生产配比数据作为预测输入变量,建立200组混凝土拌和站生产监控数据和对应的抗压强度试验数据样本集,按照6∶2∶2比例划分为训练集、验证集和测试集;分别以C40配比混凝土拌和生产的8项物料称重数据和全部13项数据作为输入变量,进行混凝土28 d抗压强度预测,将预测结果与实际试验结果进行比较,验证所提出BP神经网络模型的预测效果。结果表明:所提出的BP神经网络混凝土强度预测模型能较好地实时预测混凝土28 d抗压强度,且相对误差优于利用7 d抗压强度试验数据估算值;8项物料称重数据作为输入变量的BP神经网络预测模型预测精度更好,平均绝对百分比误差为0.82%,均方根误差为0.52 MPa;利用不同拌和站C20配比、C30配比混凝土拌和生产监控数据对8项输入变量BP神经网络混凝土抗压强度预测模型进行适应性验证可知,其预测平均绝对误差均在0.5 MPa之内,平均绝对百分比误差均小于2%,与C40配比预测误差一致;该预测模型充分挖掘了混凝土拌和站生产实时监控数据的价值,实现了传统混凝土抗压试验结果提前化,对提高工程建设质量水平具有重要意义。

骨料含量和试件尺寸对混凝土单轴受压细观性能影响

为了研究粗骨料含量对混凝土单轴受压性能及尺寸效应的影响,通过编写Python脚本在Abaqus中建立二维混凝土随机骨料模型.该模型涵盖了骨料、砂浆和界面过渡区三相混凝土细观结构,使得模型更加接近真实混凝土结构;通过设置骨料含量和模型尺寸这两个变量,研究了其对模型力学性能的影响.研究结果表明,在同一试件尺寸下,随着骨料含量的增加,混凝土损伤破坏渐趋严重,弹性模量略有增大,抗压强度呈下降趋势;相同骨料含量下混凝土的尺寸效应明显,随着试件尺寸的增加,细观非均质性增强,导致混凝土抗压强度降低,损伤破坏现象更趋明显.分析尺寸效应对模型的影响规律,为实际情况下缩尺模型的性能预测提供参考.

剑麻纤维加筋水泥土力学性能试验

目的 研究纤维掺量、纤维长度及养护龄期对剑麻纤维加筋水泥土无侧限抗压强度及巴西劈裂抗拉强度的影响规律。方法 设置纤维掺量为0~0.8%,加筋长度为7~19 mm,养护龄期为7 d、28 d及90 d,对不同影响因素下剑麻纤维加筋水泥土进行无侧限抗压强度试验及巴西劈裂试验。结果 剑麻纤维的掺入提升了水泥土的无侧限抗压强度及巴西劈裂抗拉强度,随着纤维掺量及加筋长度的增加呈现出先增大后减小的规律;当加筋长度为11 mm、纤维掺量为0.4%时,加筋效果最显著,无侧限抗压强度提高幅度达30.2%;剑麻纤维加筋水泥土的抗拉强度、拉压比与纤维掺量呈线性关系。结论 剑麻纤维的掺入改善了水泥土的脆性,提高了水泥土的破坏韧性;采用幂函数对试验数据进行拟合,得到了养护龄期与纤维掺量共同作用下剑麻纤维加筋水泥土无侧限抗压强度的预测模型,可以为实际工程提供参考。

纳米TiO_(2)混凝土抗碳化性能的试验研究

纳米TiO_(2)以同等质量代替水泥(0、1%、3%、5%)掺入混凝土中,对纳米TiO_(2)混凝土进行抗压试验和碳化试验,研究了纳米TiO_(2)的掺量对混凝土抗压强度和碳化深度的影响,建立了考虑纳米TiO_(2)掺量和碳化龄期的混凝土碳化深度模型。结果显示:掺入适量的纳米TiO_(2)对混凝土的抗压强度和碳化性能的改善有很好的促进作用,且随着纳米TiO_(2)掺入量的增加对两者的促进作用呈现先升高后降低的趋势,纳米TiO_(2)掺量为1%时对混凝土性能促进作用最好。通过引入纳米TiO_(2)对混凝土碳化深度的影响系数,改进碳化深度模型。模型预测数据和试验数据吻合度较高,可应用于纳米TiO_(2)混凝土碳化深度的预测。