强夯法在下卧深层软基处治中的应用

结合工程实例，分析强夯法处理软弱路基机理，总结强夯法形成硬壳层在处理下卧深层软基的新思路，对类似公路的建设具有借鉴作用。     

Asynchronous n-step Q-learning adaptive traffic signal control

Ensuring transportation systems are efficient is a priority for modern society. Intersection traffic signal control can be modeled as a sequential decision-making problem. To learn how to make the best decisions, we apply reinforcement learning techniques with function approximation to train an adaptive traffic signal controller. We use the asynchronous n-step Q-learning algorithm with a two hidden layer artificial neural network as our reinforcement learning agent. A dynamic, stochastic rush hour simulation is developed to test the agent’s performance. Compared against traditional loop detector actuated and linear Q-learning traffic signal control methods, our reinforcement learning model develops a superior control policy, reducing mean total delay by up 40% without compromising throughput. However, we find our proposed model slightly increases delay for left turning vehicles compared to the actuated controller, as a consequence of the reward function, highlighting the need for an appropriate reward function which truly develops the desired policy.     

论基坑支护结构的变形控制与周边建筑物的保护

该文就建筑深基坑支护结构在工程建设过程中的变形控制及对周边建(构)筑物的保护等若干问题进行探讨,可供同行参考。     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.     

基于深度学习的破碎带盾构施工沉降预测分析

为进一步提高复杂地层条件下盾构沉降预测的准确性，以广州地铁7号线1期工程谢村站-钟村站区间盾构工程为依托，针对破碎带盾构隧道沉降控制难题，提出基于深度学习的人工智能预测模型。通过分析开挖面破碎带分布规律，确定将破碎带面积比作为地层特性参数。采用相关系数矩阵分析不同施工参数与破碎带面积比的相关性，确定采用刀盘转矩代表破碎带面积比实时描述地层分布特性。以刀盘转矩、盾尾间隙与注浆量作为输入值，地面沉降作为输出值训练深度学习模型，并利用训练后的深度学习模型进行沉降预测分析。通过分析预测结果与沉降实测值的对比验证预测模型的有效性。     

深水中承台悬吊钢套箱施工

南丫大桥横跨东莞水道南丫涌，6#-10#墩处在深水中，如何高效、安全地进行承台施工，是本工程的难点之一。介绍一种既简便又经济的施工技术，在深水中悬吊钢套箱，成功解决了该技术难题。     

深水高基床防波堤工程爆夯施工工艺

文章叙述了某深水高基床防波堤的基床爆夯施工中,通过典型施工确定爆夯参数的可行性和起爆网络设计,并介绍了爆夯施工工艺、质量控制和实测效果及爆夯安全管理。     

深基坑支护结构变形预测研究与应用

利用现场监测的深基坑支护结构变形信息资料 ,结合参数优化反分析土体m值 ,根据现场地质资料和优化后的参数 ,通过有限元计算对深基坑支护系统进行变形预测 ,及时调整开挖方案和支护参数 ,此方法可以有效的指导基坑施工 ,确保施工安全