Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks

Understanding rock material characterizations and solving relevant problems are quite difficult tasks because of their complex behavior, which sometimes cannot be identified without intelligent, numerical, and analytical approaches. Because of that, some prediction techniques, like artificial neural networks (ANN) and nonlinear regression techniques, can be utilized to solve those problems. The purpose of this study is to examine the effects of the cycling integer of slake durability index test on intact rock behavior and estimate some rock properties, such as uniaxial compressive strength (UCS) and modulus of elasticity (E) from known rock index parameters using ANN and various regression techniques. Further, new performance index (PI) and degree of consistency (Cd) are introduced to examine the accuracy of generated models. For these purposes, intact rock dataset is established by performing rock tests including uniaxial compressive strength, modulus of elasticity, Schmidt hammer, effective porosity, dry unit weight, p‐wave velocity, and slake durability index tests on selected carbonate rocks. Afterward, the models are developed using ANN and nonlinear regression techniques. The concluding remark given is that four‐cycle slake durability index (I_d4) provides more accurate results to evaluate material characterization of carbonate rocks, and it is one of the reliable input variables to estimate UCS and E of carbonate rocks; introduced performance indices, both PI and Cd, may be accepted as good indicators to assess the accuracy of the complex models, and further, the ANN models have more prediction capability than the regression techniques to estimate relevant rock properties. Copyright © 2011 John Wiley & Sons, Ltd.     

A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations

Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least-square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)-PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL-PCE-IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples.     

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method

A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.     

双误差回归模型在ICP-MS测定饮用水中锶的不确定度评定的简化应用

在计算电感耦合等离子体质谱法测定饮用水中锶的浓度(x)时,由于标准系列配制和仪器检测过程中信号(y)漂移产生的不确定度会传递给最终的计算结果。普通的一次或多次线性拟合结果不能真实地反映对于x、y值都含误差的数据拟合情况。文章对标准曲线进行了双误差回归计算,从双误差回归线性方程推导出校准曲线拟合过程产生不确定度的计算公式,建立了方法检出限与曲线拟合参数(x、y、曲线截距和斜率)及其相关不确定度之间的关系式,依据误差连续传递公式及不确定度分量计算公式简化了合成不确定度的表达式。从合成不确定度计算公式中可得出,水样中锶含量的浓度越低,其对应的不确定度越大;标准系列配制过程不细致、仪器灵敏度低,方法检出限越差,与实际测试情况符合。     

基于GIS的兰州滑坡与泥石流灾害危险性分析

频繁发生的灾害愈来愈对人类社会造成巨大影响, 遥感、地理信息系统、全球定位系统和网络技术日益在减灾行动中发挥重要的作用. 兰州区域地质岩性、构造断裂、地震活动带、地表侵蚀强烈和地形起伏破碎等因素造成了兰州滑坡与泥石流灾害的发育, 夏季暴雨、人类工程活动等诱发下灾害频繁, 风险加剧. 针对兰州地区滑坡与泥石流等山地灾害对区域社会经济的影响, 通过建立区域滑坡与泥石流灾害的空间数据库, 在GIS技术辅助下实现了专家经验模型和Logistic模型对滑坡与泥石流灾害危险性的预测, 其中滑坡Logistic模型准确性达到85.3%, 专家经验模型准确性达到74.2%; 泥石流Logistic危险性预模型准确性达到80.5%, 专家经验模型准确性达到90.5%. 随着研究的深入, 综合遥感、地理信息系统、全球定位系统和网络技术的灾害研究与应用将在灾害防治中发挥重要的作用.     

单桩沉降的半经验计算法

由单桩的沉降机理导出一种计算软土地基单桩沉降的半理论半经验方法,并根据实测资料的回归、拟合确定经验系数。通过与实测单桩沉降数据的比较,表明该方法的可行性。     

不同变质程度煤的发热量与水分和灰分的多元线性回归分析

发热量是煤质评价和热工计算的重要指标。通过收集内蒙、山西等矿区煤的水分、灰分、挥发分及发热量数据,根据挥发分高低对煤变质程度进行分级,并在此基础上,建立煤的发热量与水分、灰分的多元线性回归分析模型。研究结果表明,研究区煤的发热量与水分、灰分呈反比关系,且随着煤变质程度的降低,水分对发热量的影响逐渐增大,而灰分的影响逐渐降低。运用多元线性回归模型对其他矿区煤的发热量进行预测,并将预测值与实测值进行对比来验证模型的正确性,结果显示,该模型在不同矿区煤的发热量的预测中具有适用性。     

建立磁化率快速预测白马钒钛磁铁矿含量模型

根据白马钒钛磁铁矿区试验钻孔井壁岩矿石磁化率与钒钛磁铁矿含量的关系,矿区岩矿石磁化率与钒钛磁铁矿含量的相关性,建立了磁化率与钒钛磁铁矿含量的回归方程。通过此方程的应用可以快速判断井壁岩矿体的钒钛磁铁矿含量,指导采样布设。     

矿体储量及其计算参数的误差公式和金属储量误差的回归模型

据个旧锡矿开发勘探资料,在计算机上实现不同网度稀空试验的结果、讨论了误差估计问题,提出了矿石品位、矿体厚度、矿体投影面积、矿体体积和金属储量等误差估值公式。以实例建立了不同穿透样锡品位变化系数值的金属储量误差与工程数量的回归模型,并将其应用于金属储量误差估计和工程效果的预测。