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 methods. 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 relations obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression results. 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.     

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 difficult. 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 defined as fitness 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.     

既有建筑砌体抗压强度的贝叶斯推断

基于贝叶斯理论推断既有建筑砌体抗压强度,将现场原位轴压法测试的砌体抗压强度作为先验信息,同时利用块体和砂浆回弹法检测强度的推定值,按照《砌体结构设计规范》(GB 50003-2011)中的砌体抗压强度计算公式构造似然函数,联合先验信息和似然函数,推导既有建筑砌体抗压强度的后验分布,研究结果表明:通过后验分布可得到综合各...     

Modeling of shear wave velocity in limestone by soft computing methods

The main purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters.Because rocks have complicated structure, direct determination of this parameter takes time, spends expenditure and requires accuracy. On the other hand, there are no precise equations for indirect determination of it; most of them are empirical. By using data sets of several dams of Iran and neuro-genetic,adaptive neuro-fuzzy inference system(ANFIS), and gene expression programming(GEP) methods, models are rendered for prediction of shear wave velocity in limestone. Totally, 516 sets of data has been used for modeling. From these data sets, 413 ones have been utilized for building the intelligent model, and 103 have been used for their performance evaluation. Compressional wave velocity(V_ p), density(γ)and porosity(n), were considered as input parameters. Respectively, the amount of R for neuro-genetic and ANFIS networks was 0.959 and 0.963. In addition, by using GEP, three equations are obtained; the best of them has 0.958 R. ANFIS shows the best prediction results, whereas GEP indicates proper equations. Because these equations have accuracy, they could be used for prediction of shear wave velocity for limestone in the future.     

广东高教新村桩基岩石抗压强度测试成果分析

对一高层建筑桩基岩石抗压强度的 3种测试成果进行了回归分析 ,得出相关公式 利用公式 ,通过两较为简易的现场原位测试成果推算出较难获得的近似室内试验成果 ,从而对桩基岩石抗压强度作了客观的工程地质评价     

简析顾桥矿东风井土工基本参数与冻结粘土单轴抗压强度之间的关系

冻结法施工技术因其适合地质条件复杂地段的施工,在国内外广泛用于城市建设和煤矿建设中。本文以顾桥煤矿为例,通过实验简析冻结粘土的单轴抗压强度与土工基本参数之间的关系,发现其中的规律。顾桥煤矿东风井穿越超过400m的深厚表土层需要采用冻结法施工,实验表明：含水率在23．4％时,冻结粘土的单轴抗压强度达到最大值;塑性指数大于23时,冻结粘土单轴抗压强度变得越来越小,为施工中的关键土层,对其采用低温冻结才是提高其强度的关键途径;当塑性指数小于23时,冻结粘土的单轴抗压强度满足工程要求。实验结果为两淮地区类似地层施工具有指导意义。     

鲍罗米公式在预测轻骨料混凝土28d强度方面的应用

研究结果表明,用鲍罗米公式推测轻骨料混凝土28 d抗压强度的误差较大.实测出不同配合比的轻骨料混凝土28 d抗压强度和相应的28 d胶砂试块抗压强度,运用多元线性回归分析的方法完善了鲍罗米公式,所得公式能较准确地预测轻骨料混凝土28 d抗压强度,具有现实指导意义.