线性回归法进行Kelvin电位测试

采用线性回归方法确定Kelvin电位,以克服交流信号在零振幅点附近误差的影响.实验结果表明用该法设计的Kelvin探针装置可进行薄液层下金属电极电位测量和在有、无涂层下金属表面电位分布的测试.     

SQUARE WAVE COHERENT METHOD FOR EXTRACTING IP EFFECT

ＳＱＵＡＲＥＷＡＶＥＣＯＨＥＲＥＮＴＭＥＴＨＯＤＦＯＲＥＸＴＲＡＣＴＩＮＧＩＰＥＦＦＥＣＴ￥Ｈｅ，Ｊｉｓｈａｎ；Ｗａｎｇ，Ｓｈａｏｗｕ；ＴａｎｇＪｉｎｇｔｉａｎ（ＣｅｎｔｒａｌＳｏｕｔｈＵｎｉｖｅｒｃｉｔｙｏｆＴｅｃｈｎｏｌｏｇｙ，Ｃｈａｎｇｓｈａ，...     

方柱体镦粗的两个新力学模型

在对平板间镦粗圆柱体的研究［１，２，３］基础上，进一步对普通平板间的方柱体镦粗，进行了工程力学的分析，提出了二个新力学模型，并进行了实验验证。这为形成新的拔长理论［４，６］奠定了力学基础。     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(...     

烃的含氧衍生物自燃点的QSPR预测研究

应用定量构效关系（QSPR）方法对烃的含氧衍生物的自燃点（AIT）及其与分子结构间的内在定量关系进行了研究。选取国际电工委员会（IEC）数据库中的76种烃的含氧衍生物作为样本集,选择65 种作为训练集用于建立预测模型,11 种作为测试集。采用遗传算法（GA）对变量进行筛选,结合线性和非线性方法分别建立多元线性回归( MLR) 模型和支持向量机( SVM) 模型,理论预测得到了11种烃的含氧衍生物的自燃点,最后对所构建模型的性能及应用域进行了评价。结果表明,经GA筛选得出MATS2e、nCOH、Dv、BEHv2、nCHR、GATS1v、IDE、Du等8种特征分子描述符,GA-MLR和GA-SVM模型的理论预测值与实验值均较为相符且后者更优,两个预测模型均比较稳定,且具备较强的预测能力和泛化推广性能。     

Influence of accelerated curing on the compressive strength of polymer-modified concrete

In recent building practice, rapid construction is one of the principal requisites. Furthermore, in designing concrete structures, compressive strength is the most significant of all parameters. While 3-d and 7-d compressive strength reflects the strengths at early phases, the ultimate strength is paramount. An effort has been made in this study to develop mathematical models for predicting compressive strength of concrete incorporating ethylene vinyl acetate (EVA) at the later phases. Kolmogorov-Smirnov (KS) goodness-of-fit test was used to examine distribution of the data. The compressive strength of EVA-modified concrete was studied by incorporating various concentrations of EVA as an admixture and by testing at ages of 28, 56, 90, 120, 210, and 365 d. An accelerated compressive strength at 3.5 hours was considered as a reference strength on the basis of which all the specified strengths were predicted by means of linear regression fit. Based on the results of KS goodness-of-fit test, it was concluded that KS test statistics value (D) in each case was lower than the critical value 0.521 for a significance level of 0.05, which demonstrated that the data was normally distributed. Based on the results of compressive strength test, it was concluded that the strength of EVA-modified specimens increased at all ages and the optimum dosage of EVA was achieved at 16% concentration. Furthermore, it was concluded that predicted compressive strength values lies within a 6% difference from the actual strength values for all the mixes, which indicates the practicability of the regression equations. This research work may help in understanding the role of EVA as a viable material in polymer-based cement composites.     

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer (GFRP) elastic gridshell structures. Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacement of GFRP elastic gridshell structures. Several ML algorithms, including linear regression (LR), ridge regression (RR), support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine (LightGBM), are implemented in this study. Output features of structural performance considered in this study are the maximum stress as f₁(x) and the maximum displacement to self-weight ratio as f₂(x). A comparative study is conducted and the Catboost model presents the highest prediction accuracy. Finally, interpretable ML approaches, including shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions. SHAP is employed to describe the importance of each variable to structural performance both locally and globally. The results of sensitivity analysis (SA), feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for f₁(x) and f₂(x).     

地籍测量中界址点点位中误差的自动计算

界址点点位中误差是反映地籍调查成果质量的重要依据,如何在质量检查中实现界址点点位中误差计算统计的自动化具有现实意义。本文提出了一种快速可行的自动匹配计算方法,实现了其自动计算及统计报表输出,极大提高了检查效率。     

方钢管混凝土柱施工工艺的应用

针对工程的特殊性、复杂性,通过现场试验,确定了方钢管混凝土柱内浇筑混凝土的类型,并介绍了方钢管混凝土柱钢导管法施工工艺,结果表明：方钢管混凝土柱内采用自密实混凝土,确保了钢管内以及隔板下方35°～45°角处混凝土的密实度;先浇筑混凝土楼板,再浇筑方钢管混凝土柱,采用钢导管法分段作业,降低了浇筑高度,确保了施工的安全性。     

Predicting a water infrastructure leakage index via machine learning

In this study, the infrastructure leakage index (ILI) indicator that is preferred frequently by the water utilities with sufficient data to determine the performances of water distribution systems is modeled for the first time through the three different methodologies using different input data. In addition to the variables in the literature used for the classical ILI calculations, the age parameter is also included in the models. In the first step, the ILI values have been estimated via multiple linear regression (MLR) using water supply quantity, water accrual quantity, network length, service connection length, number of service connections, and pressure variables. Secondly, the Artificial Neural Network (ANN) approach has been applied with raw data to improve the ILI prediction performance. Finally, the data set has been standardized with the Z-Score method for increasing the learning power of the ANN models, and then the ANN predictions have been made by converting the data through the principal component analysis (PCA) method to minimize complexity by reducing the data set size. The model predictions have been evaluated via mean square error, G-value, mean absolute error, mean bias error, and adjusted-R² model performance scale. When the model outputs obtained at the end of the study are evaluated together with the classical ILI calculations, it is seen that the successful ILI predictions with three and four variables, including the age parameter, rather than six variables, have been made through the PC-ANN method. Water utilities with insufficient physical and operational data for ILI indicator calculation can make network performance evaluations by predicting the ILI through the models suggested in this study with high accuracy in a reliable way.