Pullout testing and Particle Image Velocimetry (PIV) analysis of geogrid reinforcement embedded in granular drainage layers

The paper investigates the feasibility of using fine-grained soil as backfill material of geosynthetic-reinforced walls and slopes, through a laboratory study on pullout behavior of geogrids in granular layers. A series of pullout tests was carried out on an HDPE uniaxial geogrid in thin sand and gravel layers that were embedded in clay specimens.Aside from different soil arrangements, the influences of moisture content and overburden pressure on the geogrid pullout behavior is assessed and discussed. The tests were carried out at four different gravimetric water contents (GWC) on the dry and wet sides of the clay optimum moisture content (OMC), and overburden pressure values within the range σ_v = 25–100 kPa. Particle Image Velocimetry (PIV) was used to capture digital images during the tests, which were processed to help with the interpretation and improved understanding of the soil-geogrid interactions at different GWC values. Results show that embedding geogrid reinforcement in layers of sand or gravel can significantly increase the pullout resistance in an otherwise moist clay backfill, and this improved pullout efficiency is greater at higher overburden pressures. The improvement in pullout capacity was observed in clay specimens compacted at both the dry and wet sides of the OMC.     

混凝土结构的压电声波检测分析

本文介绍了混凝土结构的压电体波和表面波检测的主要进展,对两种压电声波检测的优缺点进行了总结。体波检测设备一般埋入混凝土内部,需要选择合理的检测部位,检测结果较为精确;声表面波检测无需选择特定的部位,但是检测深度有限。在实际检测工作过程中,可以联合两种方法相互验证。     

配分工艺对中碳Ti-Mo高强Q&P钢组织和性能的影响

摘要：采用盐浴实验、扫描电镜、透射电镜、拉伸实验和磨损实验等手段，研究了配分工艺对中碳Ti Mo钢组织和性能的影响，分析了不同配分工艺处理下的组织演变和性能变化。结果表明，显微组织主要由回火马氏体、渗碳体、(Ti，Mo)C粒子组成。随着配分时间的延长和配分温度的升高，板条马氏体数量减少，马氏体板条厚度增加，边界钝化。此外，随着配分温度从310℃提高至400℃，抗拉强度、硬度和低温冲击韧性同时下降，分别降低约250MPa、56HV和15J。最后，Ms以下温度配分（310℃）试样的耐磨损性能明显优于Ms以上温度配分（400℃）试样。Ms以下温度配分试样磨损表面形貌以塑性变形为主，Ms以上温度配分试样磨损表面以犁沟为主。     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

基于BIM超高层密集布筋区埋入式型钢混凝土柱脚施工技术

以漳州市某超高层项目为例,针对超高层密集布筋区埋入式型钢柱脚与钢筋的深化设计、地脚螺栓的定位预埋、型钢柱脚支撑体系设计、节点钢筋穿插连接施工、埋入式型钢柱脚安装等方面,提出切实可行的施工方法,解决了埋入式型钢混凝土柱脚的地脚螺栓精准预埋、与底板密集钢筋穿插连接施工、悬空支撑稳定等一系列施工技术难题,既满足了设计方案的要求,又可为类似工程提供借鉴。     

“方变圆”变径筒状结构滑模施工技术

为解决塔式光热的核心构筑物吸热塔"方变圆"变径超高筒体结构施工难题,选用一套可变形的滑模平台系统,通过操作系统上可调千斤顶支座、门架横向液压千斤顶装置、多处可调节模板角度和收缩模板的丝杆等,使得滑模根据施工需求可在一定的范围内调节模板倾角、间距,并逐步完成收缩变形,其理论滑模允许偏角20°,最终高效完成异形结构施工。该滑模系统刚度大、承载及堆料能力强、空间大、机械化程度高,施工操作简单,安全性高,适应沙漠极端环境。全过程仅需使用一套滑模体系,就能完成方形段、方变圆的变形段及圆形段施工,大大提高了施工效率,为类似环境下相似结构的施工提供了一定的参考。     

Synthesis of thermoresponsive copolymer/silica-coated magnetite nanoparticle composite and its application for heavy metal ion recovery

To enhance chemical stability and suppress of aggregation of magnetite nanoparticles (MNPs), which are used as a support for thermoresponsive copolymer immobilization, silica coating of the MNPs is applied via the electrooxidation method. Although the resulting silica coated-MNPs also formed aggregates, the size distribution of the aggregate shifted to smaller size range. Because of that, the surface area available for copolymer immobilization increased approximately 6.7 times at maximum as compared with that of the uncoated MNPs. It contributed to the increase of the amount of the immobilized copolymer on the silica-coated MNPs, which is approximately four times larger than that on the uncoated MNPs. Fe₃O₄ dissolution test confirmed enhancement of chemical stability of MNPs. The thermoresponsive copolymer immobilized on the silica-coated MNPs shows the ability to recycle Cu(II) ion from Cu(II) containing solution by changing temperature with significantly shorter time than those in other thermoresponsive adsorbents in gel form.     

Centrifugally spun carbon fibers prepared from aqueous poly(vinylpyrrolidone) solutions as binder-free anodes in lithium-ion batteries

Aqueous solutions of poly(vinylpyrrolidone) (PVP) of various concentrations (20, 25, and 28 wt%) were successfully spun into fibers by centrifugal spinning. The pristine PVP fibers were annealed and carbonized to produce flexible carbon fibers for use as binder-free anodes in lithium-ion batteries. These flexible carbon fibers were prepared by developing a novel three-step heat treatment to reduce the residual stresses in the pristine PVP precursor fibers, and to prevent fiber degradation during carbonization. The thermogravimetric analysis data showed that the annealed fibers yielded a residual mass percentage of 36.0% while the pristine PVP fibers suffered a higher mass loss and only retained 26.5% of original mass above 450 °C (under nitrogen). The electrochemical performance of the carbon-fiber anodes was evaluated by conducting galvanostatic charge/discharge, rate performance, and cycle voltammetry experiments. The 20, 25, and 28 wt% derived binder-free anodes delivered specific charge capacities of 205, 189, and 275 mAh g⁻¹, respectively, after the first cycle at a current density of 100 mA g⁻¹. The results obtained in this work indicate that a feasible pathway towards a large-scale production of carbon-fiber anodes from a 100% aqueous solution can be achieved via centrifugal spinning and subsequent heat treatment.