Comparative study of EICP treatment methods on the mechanical properties of sandy soil

This study analyzed the effect of different treatment methods in enzyme-induced carbonate precipitation (EICP) on the mechanical properties of soil. Soybean crude urease was used to catalyze the precipitation of calcium carbonate (CaCO₃). A multiple-phase method was proposed and further compared with commonly practiced EICP treatment methods (including the one-phase method, two-phase method, and premix-and-compact method) from the aspects of chemical conversion efficiency, CaCO₃ precipitation distribution, permeability, and unconfined compressive strength. Based on the findings, the characteristics of each method were further discussed and summarized. Although the enzymatic CaCO₃ precipitation generated from all the treatment methods could potentiate the soil strength to a great or less degree, using the proposed multiple-phase method could bring about a high chemical conversion efficiency, uniform distribution of CaCO₃ as well as preferable permeability retention. In addition, the multiple-phase method could significantly improve the efficiency of urease usage.     

Consolidation considering increasing soil column radius for dredged slurries improved by vacuum preloading method

Soil column is often investigated in the improvement of dredged slurries. Different from the smear zone, the soil column forms gradually and has extremely low permeability. This study presents an analytical solution for soil consolidation considering the increasing radius of the soil column and time-dependent discharge capacity. Based on the solution, the influence of the radius' increase on the consolidation behavior is found significant when the soil column has low permeability and large final radius, and the increase of formation time can lead to the increase of consolidation speed and final consolidation degree.     

Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model

Geogrids embedded in fill materials are checked against pullout failure through standard pullout testing methodology. The test determines the pullout interaction coefficient which is critical in fixing the embedment length of geogrids in mechanically stabilized earth walls. This paper proposes prediction of pullout interaction coefficient using data driven machine learning regression algorithms. The study primarily focusses on using extreme gradient boosting (XGBoost) method for prediction. A data set containing 220 test results from the literature has been used for training and testing. Predicted results of XGBoost have been compared with the results of random forest (RF) ensemble learning based algorithm. The predictions of XGBoost model indicates 85% accuracy and that of RF model shows 77% accuracy, indicating significantly superior and robust prediction through XGBoost above RF model. The importance analysis indicates that normal stress is the most significant factor that influences the pullout interaction coefficients. Subsequently pullout tests have been performed on geogrid embedded in four different fill materials at three normal stresses. The proposed XGBoost model gives 90% accuracy in prediction of pullout interaction coefficient compared to laboratory test results. Finally, an open-source graphical user interface based on the XGBoost model has been created for preliminary estimation of the pullout interaction coefficient of geogrid at different test conditions.     

Learn decision trees with deep visual primitives

In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features extracted from deep neural networks (DNNs) and trees’ transparent decision-making processes. Specifically, we utilize a codebook, which embeds the continuous deep features into a finite discrete space (deep visual primitives) to distill the most common semantic information. The decision tree adopts the spatial location information and the mapped primitives to present the decision-making process of the deep features in a tree hierarchy. Moreover, the trained interpretable PrimitiveTree can inversely explain the constituents of the deep features, highlighting the most critical and semantic-rich image patches attributing to the final predictions of the given DNN. Extensive experiments and visualization results validate the effectiveness and interpretability of our method.     

建筑物掏土纠倾法单孔与多孔塑性区特性研究及工程应用

在建筑物水平掏土纠倾工程中，掏土孔间距是影响纠倾工程安全与工期的重要因素。为了快速准确地确定纠倾工程中的水平掏土孔间距，研究了单个掏土孔和多个掏土孔情况下孔周边土体塑性区发展特性。利用土体塑性力学分析计算得到了单孔下的孔周土塑性区半径，而后通过有限元模拟得到孔周土体塑性区半径的数值解，将孔周塑性区半径解析解与数值解进行了对比。并通过有限元数值模型研究了多个掏土孔相互影响情况下的塑性区发展规律，以孔间土体塑性区贯通时的距离作为掏土孔间距。考虑土体参数随机特性的影响，研究不同上部荷载作用下掏土孔间距的取值变化规律，上部面荷载与地基承载力特征值比值用p表示，孔间距与掏土孔直径比值用n表示。研究发现：多孔塑性区半径（孔间塑性区贯通时）是单孔塑性区半径的1.3倍左右;标准化荷载p与孔间距比值n二者呈线性关系;通过不同土体参数及上部荷载的不同情况下的p-n曲线，给出了掏土孔间距建议值。同时，将研究结果与三个实际工程进行对比，发现p-n曲线法与实际结果更为接近。     

Adaptive investigation of the optical properties of polymer fibers from mixing noisy phase shifting microinterferograms using deep learning algorithms

In this article, an adaptive denoising method is suggested to accurate investigate the optical and structural features of polymeric fibers from noisy phase shifting microinterferograms. The mixed class of noise that may produce in the phase-shifting interferometric techniques is established. To our knowledge, this is an early study considered the mixing noises that may occur in microinterferograms. The suggested method utilized the convolution neural networks to detect the noise class and then denoising, it according to its class. Four convolution neural networks (Googlenet, VGG-19, Alexnet, and Alexnet–SVM) are refined to perform the automatic classification process for the noise class in the established data set. The network with the highest validation and testing accuracy of these networks is considered to apply the suggested method on realistic noisy microinterferograms for polymeric fibers, polypropylene and antimicrobial polyethylene terephthalate)/titanium dioxide, recoded using interference microscope. Also, the suggested method is applied on noisy microinterferograms include crazing and nanocomposite material. The demodulated phase maps and the three-dimensional birefringence profiles are calculated for tested fibers according to the suggested method. The obtained results are compared with the published data for these fibers and found to be in good agreements.     

风险矩阵法在A水泥工厂风险分级的应用初探

本文介绍“风险矩阵法”进行风险分级工作的基本思路，结合水泥厂的生产特点通过危险有害因素辨识，获得危险源分布情况，采用风险矩阵法对风险进行评估，按风险值将风险等级划分为重大风险、较大风险、一般风险和低风险，为水泥生产企业的安全风险分级工作提供参考。     

火花放电原子发射光谱类型标准化校正方式的探讨

在国内,火花放电原子发射光谱分析广泛使用类型标准化进行方法校正。在国外,标准和文献中鲜见使用此方法的相关论述。类型标准化主要采用平移校正和转动校正两种方式,哪种方式更加合理也鲜见报道。国外设备类型标准化的默认设置优先采用转动校正方式,相关国内标准对最优校正方式的规定尚不明确,此默认设置的合理性有待探讨。实验选用低合金钢20CrNi2Mo、R407标准样品和不锈钢317L、0Cr18Ni9标准样品,引用国内相关标准,以正确度临界差为评判依据,模拟类型标准化样品和待测样品“十分接近”和“接近”两种情况下平移校正和转动校正的数据正确度。经数据统计分析,平移校正分析结果均满足要求,转动校正结果在“接近”情况下部分元素不满足要求。结合相关国家标准中元素含量范围和精密度数据进行分析,通过计算允许最大偏倚量并制作曲线图方式展开分析,得出如下结论:在满足文中类型标准化控制要点前提下,分析设置更适合于采用平移校正方式。     

Behavior of phase transformation of Baotou mixed rare earth concentrate during oxidation roasting

Based on the new process named “Combination Method” for metallurgy and separation of Baotou mixed rare earth concentrate (BMREC), the aim of this paper is to clearly elucidate the phase change behavior of BMREC without additives during oxidative roasting at 450–800 °C. The results indicate that the bastnaesite in BMREC is decomposed at 450–550 °C, the weight loss is about 10.3 wt%, and the activation energy (E) is 144 kJ/mol. The bastnaesite in BMREC is decomposed into rare earth fluoride, rare earth oxides (La₂O₃, Ce₇O₁₂, Pr₆O₁₁ and Nd₂O₃), and CO₂, particularly, with the increase of roasting temperature, bastnaesite in BMREC is more completely decomposed into LaF₃, which causes a decrease in leaching rate of La during the HCl leaching process. Additionally, the maximum cerium oxidation efficiency reaches about 60 wt% when the roasting temperature is equal to or above 500 °C, and the oxidation reaction rate of cerium increases with the increasing roasting temperature.