某高速公路下边坡稳定性分析及支护方案研究

某高速公路K87+200~K87+320段下边坡高约52 m,边坡坡度约42°~54°,地质条件较复杂。基于理正软件采用瑞典条分法和基于Midas-GTS有限元仿真计算对该边坡的稳定性进行了分析,两种方法的计算得到的边坡滑动面位置和形状基本一致,瑞典条分法计算得到的安全系数为1.241,小于仿真计算的1.309,说明瑞典条分法相对保守。针对该边坡特征,提出完善排水系统+挂网锚喷的支护方案,并利用理正软件对支护后的边坡稳定进行计算,得到其安全系数为1.359>1.35,符合规范要求,说明边坡处于稳定状态。     

隆林新洲养护站边坡稳定性分析

文章利用SLOPE／W模块,基于确定值方法和可靠度理论的极限平衡法．对隆林新洲养护站边坡在不采取支挡措施和采用锚索抗滑桩支挡措施情况下,如何保持边坡稳定性进行了对比分析。     

桥梁下部结构加固主要工艺

结合京福高速K30+924通道桥维修加固工程,叙述了高压喷射灌浆法、粘贴碳纤维贴片法及钢板补强法的施工工艺。     

公路滑坡综合整治处理设计

介绍了广东省公路K41+800—K41+860右侧边坡的整治处治。分析了边坡滑塌的起因,在分析的基础上提出了以预应力锚索和抗滑挡墙为主、护坡、拱架植草防护为辅的综合整治措施。实践证明,对于顺层且倾向于路基一侧的边坡以及由地质构造如断层控制的边坡,都有失稳的可能性,在设计时应除了对全坡面进行必要的防护外,还应根据计算分析结果对边坡进行有效可行的加固。本边坡滑塌整治至今效果良好,说明本文提出的"削坡减载、强身固脚"的综合措施是合理可行的。     

考虑电动自行车骑行的非机动车道最大纵坡探讨

现行规范要求非机动车道的最大纵坡不超过3.5%,但要在山地城市、跨江桥梁两岸慢行系统衔接等场景下满足该要求存在工程占地大、造价高、可实施性差等问题,考虑到电动自行车在非机动车出行方式中占比越来越高的实际情况,以《电动自行车安全技术规范》（GB 17761—2018）规定的国标电动自行车为研究对象对骑行过程进行受力分析,提出了可满足电动自行车骑行要求的非机动车道最大纵坡参考值,可供电动车骑行占比高的类似项目参考。     

含软弱土层边坡稳定性分析的全局滑面自动搜索技术

提出了一种用于边坡稳定性分析的全局滑面自动搜索技术,首先采用三角网格进行了边坡土层的几何离散,然后,结合蒙特卡洛法等角/等边逐段扫描与模拟退火法,构建了全套的边坡全局临界滑面搜索扫描算法。以Fortran和Matlab为计算手段进行联合编程,采用简明Morgenstern-Price法进行了边坡稳定性分析计算。通过选用3个算例进行了算法的可靠性验证,计算结果表明:截面三角网格划分方法具有良好的适应性,能较好地对不规则层位几何形状进行离散;多段折线滑面具有较圆弧滑面更为优秀的极限滑面定位功能,其安全系数随着滑面段数的增加会逐步达到收敛;本搜索方法可不依赖于工程师经验进行多段折线初始滑面的大致范围选择,其能快速地、全自动地进行由弱到强不同复杂土质情况的边坡稳定性计算分析,并能找到全局临界滑面与安全系数。     

地下连续墙的受力状态及计算图式分析

在地下连续墙工程现场观测的基础上,对其受力状态进行了分析探讨。指出单元墙段是呈双向弯曲受力状态。因而按现行设计(单向受弯)的配筋计算应做适当的调整。在用筋总量不增加的基础上,使墙体结构设计更加合理、完善、经济、可靠。     

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.     

基于反分析法的均质路堑滑坡治理技术研究

反分析方法与计算机技术的结合使其功能越来越强,在实际工程中得到越来越多的应用与研究。采用反分析法确定边坡岩土体抗剪强度参数从而得出对滑坡治理的设计参数。基于简化Bishop法建立反分析极限平衡方程,并采用计算剖面多组单参数反演与统计比较分析相结合的方法,优化反算岩土抗剪强度参数值。该方法应用于某均值路堑滑坡治理工程并得以论证,表明了反分析法应用于滑坡治理的可行性与正确性。     

石方边坡控制爆破在公路路网工程中的运用

文章通过光面爆破与普通石方爆破的对比,总结石灰岩地区光面爆破各技术参数的选取和施工工艺的控制方法。