A fractal model of reinforcement of elastoplastic nanocomposites

Within the framework of fractal analysis and percolation theory, an alternative model of reinforcement of filled polymers is offered. Practically, this model can be used only to describe the reinforcement of nanocomposites, because, according to the treatment considered, a pronounced reinforcement can be reached only at ratios of filler particle diameter to the statistical segment length of about 10 and less. A theoretical calculation showed a good qualitative and quantitative agreement with experiments. The type of reinforcement mechanism of composites is determined by the type of the space (fractal or Euclidean) in which the structure of the polymeric matrix is formed. __________ Translated from Mekhanika Kompozitnykh Materialov, Vol. 42, No. 6, pp. 797–802, November–Decem ber, 2006.     

Action learning versus strategy learning

This article seeks to ascertain whether the strategy‐learning model of Hanaki, Sethi, Erev, and Peterhansl (2003) better accounts for observed behavior than do the various action‐learning models. It does so by measuring the goodness‐of‐fit of the models' predictions against published experimental results for such games as Coordination, Prisoner's Dilemma, and Chicken. The fit is measured via the mean squared deviation (MSD) between the observed behavior and the one predicted by the model. The results show that, for Chicken, the strategy‐learning model fits the observed data much better than do the action‐learning models. The best action‐learning model, on the other hand, fits the observed data well in Coordination. Overall, the strength of the strategy‐learning model is best shown in games where alternations between the two stage‐game Nash equilibria are often observed in the laboratory experiments. © 2004 Wiley Periodicals, Inc. Complexity 9: 41–50, 2004     

Application of a Near-Optimal Reinforcement Learning Controller to a Robotics Problem in Manufacturing: A Hybrid Approach

Optimization theory provides a framework for determining the best decisions or actions with respect to some mathematical model of a process. This paper focuses on learning to act in a near-optimal manner through reinforcement learning for problems that either have no model or the model is too complex. One approach to solving this class of problems is via approximate dynamic programming. The application of these methods are established primarily for the case of discrete state and action spaces. In this paper we develop efficient methods of learning which act in complex systems with continuous state and action spaces. Monte-Carlo approaches are employed to estimate function values in an iterative, incremental procedure. Derivative-free line search methods are used to obtain a near-optimal action in the continuous action space for a discrete subset of the state space. This near-optimal control policy is then extended to the entire continuous state space via a fuzzy additive model. To compensate for approximation errors, a modified procedure for perturbing the generated control policy is developed. Convergence results under moderate assumptions and stopping criteria are established.     

Hydrogel‐like elastic membrane consisting of semi‐interpenetrating polymer networks based on a phosphorylcholine polymer and a segmented polyurethane

To obtain a hydrogel‐like elastic membrane, we prepared semi‐interpenetrating polymer networks (IPNs) by the radical polymerization of methacrylates such as 2‐methacryloyloxyethyl phosphorylcholine (MPC), 2‐hydroxyethylmethacrylate, and triethyleneglycol dimethacrylate diffused into segmented polyurethane (SPU) membranes swollen with a monomer mixture. The values of Young's modulus for the hydrated semi‐IPN membranes were less than that for an SPU membrane because of higher hydration, but they were much higher than that for a hydrated MPC polymer gel (non‐SPU). According to a thermal analysis, the MPC polymer influenced the segment association of SPU. The diffusion coefficient of 8‐anilino‐1‐naphthalenesulfonic acid sodium salt from the semi‐IPN membrane could be controlled with different MPC unit concentrations in the membrane, and it was about 7 × 10² times higher than that of the SPU membrane. Fibroblast cell adhesion on the semi‐IPN membrane was effectively reduced by the MPC units. We concluded that semi‐IPNs composed of the MPC polymer and SPU may be novel polymer materials possessing attractive mechanical, diffusive‐release, and nonbiofouling properties. © 2002 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 41: 68–75, 2003     

热致液晶共聚酯酰胺／PET共混物的流变性能和原位增强特性

采用Ｉｎｓｔｒｏｎ３２１１型毛细管流变仪研究了ＰＥＡ／ＰＥＴ共混物的流变性能，据此探讨共混物熔体的可纺性，讨论了共混物配比与复合纤维力学性能的关系，并且偏光显微镜，扫描电镜研究纤维的结构。结果表明，共混物的表观粘度低于ＰＥＴ的粘度，当ＰＥＡ为１０％，共混物的表观粘度达到最小值；且ＰＥＡ含量低于１０％时，共混物的可纺性即，表现出良好的原位增强效果。     

Mechanical Properties and Structure of Zinc-Containing Latex-Silicate Composites

The sol-gel technology is highly efficient for improving mechanical and thermal properties of polydiene elastomers by creating in them additional inorganic networks composed mainly of silica or silicates. The incorporation of modifying Zn ions into polydiene latex-silica systems increases considerably their mechanical characteristics due to an increase of the interaction between the organic and inorganic networks. Interpenetrating polydiene-silica networks were prepared by cohydrolysis of tetraethoxysilane and zinc acetate in natural Qualitex or synthetic butadiene-styrene latexes and then by aging and drying the liquid compositions at room temperature and vulcanizing at 90°C. Their structure and properties were studied by the IR spectroscopy, TMA and by tensile testing of the prepared films. The studied system was shown to be extremely suitable for obtaining latex films with improved elastic and strength characteristics. For example, the elastic modulus values of the Qualitex films containing 2–5% SiO₂ and 0.1–0.2% ZnO, are 4–9 and 6–11 times higher than those of the films prepared without zinc and of the pure latex films respectively. The possible structure roles of zinc ions at various stages of film preparation and vulcanization are discussed on the base of the results of IR and TMA studies.     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

高强钢筋混凝土单向板在非接触爆炸下的抗爆性能研究

钢筋混凝土(RC)单向板是桥梁建设的重要组成部分. 对6块高强RC单向板进行非接触爆炸试验, 研究HRB400、HRB500和HTRB600高强RC单向板的抗爆性能, 分析爆炸后试件的破坏形态和参数. 结果表明 非接触爆炸下, 3种类型高强钢筋混凝土单向板的破坏形态相似, 单向板整体响应后出现弯曲破坏, 侧表面出现多条弯曲破坏裂缝; 在2.5kgTNT作用下的HTRB600高强RC单向板的裂缝数量和裂缝类型与2.1kgTNT作用下的HRB500高强RC单向板和1.7 kgTNT作用下的HRB400高强RC单向板差别不大, 相差范围在1条裂缝以内; HTRB600高强RC单向板在承受更大载荷时, 其平均加速度峰值比低载荷下HRB500、HRB400高强RC单向板分别增加45.1%和88.6%, 其抗弯承载力更好, 刚度更大.     

钢筋混凝土圆形截面非均匀配筋的图算法

圆形截面是工程中常使用的截面形式之一,但圆形截面的配筋计算较为困难,存在双重非线性(材料和截面宽度变化的非线性),《混凝土结构设计规范》给出了计算均匀配筋的超越方程组,且须迭代求解,无法手算.另外,一些圆形截面构件(如桥墩、桩基的预制桩和灌注桩等)的截面尺寸大,构件长,钢筋用量大,若采用均匀布置的配筋方式,中性轴附近的...     

钢筋混凝土旧工业厂房结构加固方案优选

当前人们选择旧工业厂房结构加固方案效率低下,且所考虑的因素较为片面,为了全面、高效地解决这一问题,通过综合考虑安全性、经济性、时间性、可行性和适用性等因素,以加固效果显著的工程案例为标准,以全面的18个参数为评价指标,构建了一种基于蛛网面积相似和形状相似的结构加固方案优选模型。该模型中首先运用熵权法确定18个指标权重,并将参数值归一化处理,随后运用Python计算蛛网结构相似度,检索出与目标范例最为相似的源范例。最后以结构加固效果显著的工程为例,验证了该模型能有效减少主观误差,具有较强的实用性,并以此模型优选出了某轧钢厂房结构加固方案。