Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms

Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand. The primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams (SB). A database encompassing 1118 experimental findings from the relevant literature was compiled, containing eight distinct factors. Gradient Boosting (GB) technique was developed and evaluated in combination with three different optimization algorithms, namely Particle Swarm Optimization (PSO), Random Annealing Optimization (RA), and Simulated Annealing Optimization (SA). The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output variables. Shap values and two-dimensional PDP analysis were then carried out. Engineers may use the findings in this work to define beam's geometrical components and material used to achieve the desired shear strength of SB without reinforcement.     

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

The compressive strength of self-compacting concrete (SCC) needs to be determined during the construction design process. This paper shows that the compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid and hybrid models. Nine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DTR), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network using two training algorithms LBFGS and SGD (denoted as ANN_LBFGS and ANN_SGD), are also compared with the XGB model. Moreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model. The highest number of SCC samples available in the literature is collected for building the ML techniques. Compared with previously published works’ performance, the proposed XGB method, both hybrid and non-hybrid models, is the most reliable and robust of the examined techniques, and is more accurate than existing ML methods (R² = 0.9644, RMSE = 4.7801, and MAE = 3.4832). Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.     

THE EFFECT OF AEROBIC MESOPHILIC MICROFLORAL LEVELS ON THE ISOLATION OF INOCULATED LISTERIA MONOCYTOGENES STRAIN LM82 FROM SELECTED FOODS

The effect of aerobic mesophilic microfloral concentration on the isolation of Listeria monocytogenes LM82 was studied in 31 (18 cheeses and 7 noncheese) retail foods having standard plate counts of 10¹ to 10⁸ colony forming units (CFU)/g. Foods were spiked with L. monocytogenes and enriched at 30°C for 24 h in a selective enrichment broth used in a U.S. Food and Drug Administration method. Inoculum levels for isolation on modified McBride agar ranged from 0.1 to > 5 × 10³ with a geometric mean value of 5 inoculated CFU/g or 1.4 CFU/g. Pure Enterococcus (Streptococcus) faecalis ( 0 to 6 × 10⁶ inoculated CFU/mL ) in the absence of food matrix had no effect on the enrichment of L. monocytogenes. Ease of isolation of LM82 was independent of the food microflora concentration both generally and in the specific food type of 9 Brie cheeses. Competition, when it occurs, therefore, may be due to specific bacterial competitors rather than bacterial numbers .     

A Rapid Method for the Evaluation of Emulsion Stability of Non-Dairy Creamers

A rapid method based on the Lowry protein assay was developed for the determination of the emulsion stability of nondairy creamers in a model system. The method used the detergent and crystallization properties of the anionic surfactant sodium dodecyl sulfate to solubilize the casein present in the creamers. Increasing concentrations of the divalent cations Ca⁺⁺ and Mg⁺⁺ resulted in an increasing degree of emulsion breakdown in a commercial nondairy creamer. Significant differences in the emulsion stability of five commercial nondairy creamers were also demonstrated with this method.     

Stackelberg strategies in discrete linear quadratic problems

Linear closed-loop Stackelberg strategies in sequential decision-making problems for linear time-invariant discrete systems and trace criteria which may be interpreted as the expected value of quadratic criteria are considered. Necessary conditions for the solution are developed and an algorithm of the multi-level Stackelberg problem is presented. An example for a two-level problem is presented to illustrate the proposed numerical algorithm     

越南木薯生产概况

[目的]越南地处热带,光照充足,雨量充沛.通过对越南气候条件、农业生态区域、越南全国、各生态区、部份省份近几年种植木薯面积、单产及总产概况分析,了解越南木薯发展现状和趋势,为我国木薯产业发展提供参考.[方法]基于越南农业统计数据库、越南统计总局网站、越南农业农村部栽培局、越南统计年鉴和越南国情报告年度数据,分析越南全国、各生态区、部份省份近几年木薯发展、种植、生产及贸易概况.[结果]越南纬度跨越大,气候条件差异大,分6个农业生态区.越南木薯种植主要在丘陵山区,在热带地区可全年种植,越南南部是热带地区,北部是亚热带地区,越南种植木薯主要集中在南部,近年来全国种植木薯面积已超过50万公顷,木薯总产已超1000万吨,其中2016年达到最高面积为56.90万公顷,产量达1090.99万吨,2019年种植面积为51.93万公顷,产量984.69万吨;2019年南部种植面积是36.07万公顷,北部只有15.88万公顷,2012年越南木薯及木薯产品出口价值达最高13.514亿美元,2019年为9.669亿美元.[结论]越南地处热带亚热带,北部是亚热带,四季分明,南部是热带,分旱季和雨季.木薯是越南重要的经济作物,越南是全球木薯出口大国,越南木薯及木薯产品出口价值10亿美元左右,全球排名第二.我国广西等西南地区与越南相邻,有比较相似的气候和环境条件,合作的空间和潜力有广阔的前景.     

MSDL-IEW:面向文本分类的密集度感知主动学习算法

为了解决文本分类任务中未标注数据无法即时标注及成本过高的问题,提出一种面向文本分类的不确定性主动学习方法。提出MSDL（Measure sample density by LDA）算法对未标注样本密集度进行计算,引入新的度量样本聚集情况的密集度计算方式,在密集度高的样本区域选取初始训练集样本,从而使初始训练集更具代表性;从未标注样本中选取更具不确定性的样本加入到训练集中,并基于信息熵对样本进行加权训练,迭代更新分类器模型,直至达到预期终止条件。实验结果表明,在文本分类任务中,该方法相较于其他传统主动学习算法性能更优。     

3D打印混凝土技术在澳大利亚的最近研究进展

3D打印,又被称为增材制造,是通过逐层累积叠加制造三维物体的一种技术手段。过去几年中,这种技术吸引了越来越多来自建筑业的关注。与传统混凝土浇筑技术相比,3D打印混凝土的应用可以实现更加高效的自由建筑制造,同时减小对人工劳动力的依赖。在全球范围内,由于相关高校和科研单位的积极参与,关于3D打印混凝土的研究工作取得了巨大的进展,其中包括澳大利亚的相关研究。本文主要对澳大利亚在3D打印混凝土研究方面有代表性的两所高校的最新进展进行介绍,两所学校分别是皇家墨尔本理工大学(RMIT大学)和斯威本科技大学。本文首先介绍了两所高校的打印设备及其特点,随后讨论了具体研究领域和近期发表的相关文章。RMIT大学主要侧重于研究纤维增强对3D打印混凝土力学性能方面的影响,而斯威本科技大学则着重研究3D打印地聚物混凝土的性能。本文旨在促进相关领域研究人员对澳大利亚3D打印混凝土研究团队和研究进展的理解。     

A NUMERICAL STUDY OF THE FLOW OF GRANULAR MATERIALS BETWEEN TWO VERTICAL FLAT PLATES WHICH ARE AT DIFFERENT TEMPERATURES

ABSTRACT

The present study considers an assembly of spherical particles, densely packed, between two vertical flat plates which are at different temperatures. A continuum model is used and the flow due to such a temperature difference is investigated. For a fully developed flow of these materials, the governing equations reduce to a system of coupled, non-linear ordinary differential equations. The equations are integrated numerically. In so doing, the equations are discretized using the conventional central finite difference approximation technique and the unknown conditions are assumed to reduce the problem to an initial value problem. After the trial solutions the known boundary conditions at the edge of the integration interval are compared with the correspondent values provided by the trial solutions. If the solutions at this point do not agree with the known boundary conditions the Newton-Raphson method is used to correct the initial guesses and the iteration is repeated. This procedure continues until the solutions converge to the given values. The technique is found to be powerful for this type of application.     

滤波器对新型换流变压器运行参数的影响分析

新型换流变压器及其滤波器具有特殊的绕组接线方案,所以对其运行参数如视在功率、功率因数、网侧电流、负载电压、网侧电流的畸变率等的研究尤为重要。首先建立反映网侧电流和负载电流、负载电压和网侧电压、网侧谐波电流和负载谐波电流关系的数学模型。理论分析表明,未投滤波器时,新型换流变压器跟传统换流变压器的运行参数特性一致;而投滤波器时,新型换流变压器可以有效地降低网侧视在功率,提高网侧功率因数,减少网侧电流、增加负载电压及改善网侧电流的畸变率。最后通过对比理论计算值、仿真结果和实验结果,验证了所推导的新型换流变压器的数学模型的正确性。