高速铁路路基边坡纤维团粒喷播生态防护技术

针对高速铁路路基绿化工程高要求、少维护等特点,提出纤维团粒喷播技术,并详细阐述了其机理及特点.通过在京雄城际铁路路基边坡工程中的应用,归纳总结了该技术施工工艺流程:平整坡面→坡面覆土→铺设铁丝网→喷播基质→养护管理.喷播基质是利用喷播机械将性能优越的基质材料喷附至铁路路基坡面上的.工程应用表明该技术施工快,质量好,养护维修量小,经济性高.     

锚杆支护及其配套技术

详细介绍了锚杆支护及其配套技术，主要有光面爆破、喷射混凝土等。     

基于深度强化学习的智能船舶航迹跟踪控制

[目的]智能船舶的航迹跟踪控制问题往往面临着控制环境复杂、控制器稳定性不高以及大量的算法计算等问题。为实现对航迹跟踪的精准控制,提出一种引入深度强化学习技术的航向控制器。[方法]首先,结合视线(LOS)算法制导,以船舶的操纵特性和控制要求为基础,将航迹跟踪问题建模成马尔可夫决策过程,设计其状态空间、动作空间、奖励函数;然后,使用深度确定性策略梯度(DDPG)算法作为控制器的实现,采用离线学习方法对控制器进行训练;最后,将训练完成的控制器与BP-PID控制器进行对比研究,分析控制效果。[结果]仿真结果表明,设计的深度强化学习控制器可以从训练学习过程中快速收敛达到控制要求,训练后的网络与BP-PID控制器相比跟踪迅速,具有偏航误差小、舵角变化频率小等优点。[结论]研究成果可为智能船舶航迹跟踪控制提供参考。     

铁路预应力路堤加固技术数值研究

研究目的:铁路预应力路堤在国内外尚属一种新型路基加固法,其受力变形特性暂未得到系统化研究,相关加固设计理论仍处于探索性阶段。因此,有必要通过数值手段了解预应力路堤的工作状态,以掌握其加固性能。鉴于此,借助ABAQUS软件平台构建预应力路堤仿真系统,分析差异化预应力加固参数对路堤变形和承载能力的影响以及预应力加固构件的受力特征。研究结论:(1)路堤本体段坡面较优加固位置为距本体顶面以下0.3倍本体高度处;(2)坡率1∶1的预应力路堤在第1、2排侧压板分别施加50 k Pa、100 k Pa预压荷载时,其变形与承载力均可达传统路堤(坡率1∶1.5)水平,并可通过提升加固标准进一步强化路堤承载性能;(3)当对第1、2、3排侧压板分别施加50 k Pa、100 k Pa、100 k Pa预压荷载时,路堤内部附加围压S11>13.5 k Pa区域大致呈"x"形分布并形成横贯路堤的"预压加固区";(4)侧压板锚固区受力集中且复杂,应注意保障锚固区板体强度;(5)力筋在路堤加载前后的应力变化量与坡面侧向变形特征相关;(6)本研究成果可为铁路预应力路堤的加固设计提供技术指导。     

向量法在不规则隧道边仰坡面积计算中的应用

根据隧道边仰坡三维坐标测量数据,通过计算边仰坡斜面和投影水平面的法向量,进而计算两面之间夹角的方法,反算边仰坡斜面面积,在类似不规则隧道边仰坡等斜面面积计算中具有广泛的应用价值。     

公路地基真空排水预压法探讨

真空排水预压法与堆载预压法相比具有加载快、无需堆载材料、不会失稳等优势,适用于加固软土特别是含水量较高的超软土地基。真空-堆载联合预压法的应用也越来越多。经过国内外十几年的探索,真空排水预压法日趋成熟,已经成为大面积应用的加固软土地基的行之有效的方法之一。     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

桥梁单板受力的原因及解决措施

随着交通量的迅猛增长,桥梁单板受力情况越来越频繁,通过对单板受力现象产生原因进行分析,提出解决措施,以供同行参考和借鉴。     

浅埋顶管沉井周边土体的三维破坏形状及沉降研究

为研究顶管沉井及周边土体在顶进力作用下的形变范围与大小，以加固旋喷桩作为沉井周围加固体建立ABAQUS软件计算模型，并将模拟结果与工程实例、PLAXIS 3D软件所得结果进行对比分析。同时，采用广州地区花岗岩残积土进行室内沉井模型试验，并通过试验结果对数值模拟结论进行验证，得出结论如下： 1）在开挖基坑前达到设计强度的旋喷桩能有效减小基坑周边土体塌陷变形、坑底隆起。2）在施加工作顶推力下，前侧主动区首先出现贯通裂缝，竖向位移斜率增大出现明显拐点；继续增大顶推力，土体位移会急剧增大导致地表沉降严重。3）对于后背土体，在顶推力作用下，地表破裂线的切线角度从0°逐渐增大至45°+φ/2，导致破裂范围也不断扩大；达到45°+φ/2处后，继续施加顶推力会导致短轴方向破坏范围扩大的速度较长轴方向的速度更快；三维空间中被动区破坏土体在地表处产生形状为椭圆的破坏面，椭圆长轴方向为顶推轴线方向，被动破坏体呈现为牛角状椭圆楔体。     

Key technologies of ship remote control system in inland waterways under ship-shore cooperation conditions北大核心CSCD

［Objective ］ To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions. ［Method］On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing, navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out. ［Results］The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance, with a consistent deviation of less than 20 meters. ［Conclusions］The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems. © 2022 Journal of Clinical Hepatology. All rights reserved.