桥梁转体施工自闭合式合龙钢壳系统及施工技术研究

为进一步减小转体施工桥梁合龙施工时对既有运营线路的干扰,同时解决传统钢壳法中加劲肋板对合龙段施工的影响,以首座采用墩中转体的大树村龙川江三线大桥为依托,在传统钢壳法的基础上进行优化研究,自行研制、设计了一套自闭合式合龙钢壳系统并应用于实践,最终达到了安全、优质、快速、有序合龙的目的。实践结果表明,自闭合式合龙钢壳系统能够满足施工要求,且使用效果良好,达到了优化施工的目的,可为类似桥梁合龙施工提供参考。     

浅谈高速公路混凝土冬季施工

中国北方受冬季气温影响全年可施工时间短,加之各个项目的施工工期紧,致使近年来冬季施工现象较为普遍。从机械设备、原材料、混凝土拌和以及混凝土养护等方面总结经济可行的施工措施,为其它类似工程提供借鉴。     

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。     

盾构法施工单机核算管理模式的应用

近年来盾构机越来越多地应用于各种隧道工程中,其中地铁应用的数量最多,约占80%。现代盾构已演变成为一种高度智能化的大型工程机械装备,对其控制的好坏将直接影响到整个工程的施工成本和企业的经营效益,而通过单机核算降低机械成本是降低施工成本、提高企业效益的有效手段,因此推行盾构法施工单机核算显得尤为重要。文章结合地铁2222标施工项目,重点介绍了盾构法单机核算管理模式及其在施工中的应用。     

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.     

采空区治理中“混凝土墩柱式支撑”施工工艺探讨

通过在某高速公路特殊路段采空区治理中首次使用“混凝土墩柱式支撑”方法的施工，该方法对于埋深较浅、顶板较好、存在有明显空腔且充水的采空区治理有明显的效果。     

铁路货物追踪系统的构建

铁路货物运输组织是铁路工作的一个核心内容,铁路货物追踪系统的设计与实现能够为货主提供更好、更快的服务,同时加快和保障铁路货运工作的组织。通过对铁路整车货物运输的流程分析,制定货物追踪系统的目标,分析系统功能。通过数据的采集和信息共享平台的建立,提出货物追踪系统的实现途径。     

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.