新建地铁隧道下穿既有地铁施工技术

北京地铁10号线国贸-双井站区间暗挖隧道施工下穿既有地铁1号线,既有线地铁结构的安全度已达临界状态,施工不能中断行车运营。为有效控制新线施工开挖引起的地层变化对既有结构位移和变形的影响,对既有线采用袖阀管注浆、WSS工法加固的措施,详细介绍新建10号线初支顶部与既有1号线初支仰拱零距离密贴、刚性支护紧贴1号线底板进行下部隧道施工的作业要点和技术措施,以确保施工安全和既有地铁的正常运营。实践证明,该工程首次采用密贴既有结构底板的形式穿越既有地铁隧道,对城市地下工程施工具有一定的指导意义。     

宜宾小南门大桥的抢修加固与恢复工程

介绍在宜宾市小南门大桥发生坍塌事故后，抢险加固中所采取的配重、减载、加固等措施，以及修复工程中车道板的拆除和预制安装，横梁的预制安装，吊杆的拆换，裂缝的修补等关键技术。     

挤密桩在台背回填后土体中加固的应用

介绍了采用10%水泥碎石稳定集料挤密桩加固已施工结束的台背回填土体的挤密原理、设计方案、施工要求及质量控制.     

公路路基侧滑失稳的防治技术

公路路基侧滑失稳对公路建设和运营安全构成严重威胁，而且具有突发性，危险极大。由于情况复杂，此类工程处治技术难度较大，本文结合多项具体的工程实例对此进行详细介绍，具有较高的参考价值。     

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。     

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。