Structural performance of approach slab and its effect on vehicle induced bridge dynamic response

Differential settlement often occurs between the bridge abutment and the embankment soil. It causes the approach slab to lose its contacts and supports from the soil and the slab will bend in a concave manner. Meanwhile, loads on the slab will also redistribute to the slab ends, which may result in faulting (or bump ) at the slab ends. Once a bump forms, repeating traffic vehicles can deteriorate the expansion joint in turn. In this case, the vehicle receives an initial disturbance before it reaches the bridge. This excitation introduces an extra impact load on the bridge and affects its dynamic responses. The present research targets at the structural performance of the approach slab as well as its effect on the vehicle induced bridge vibration. Firstly, the structural performance of the approach slab is investigated. Based on a parametric study, a correlation among the slab parameters, deflections, internal moments, and the differential settlements has been established. The predicted moments make it much easier to design the approach slab considering different levels of embankment settlements. While flat approach slab may be used for some short span applications, large span length would require a very thick slab. In such case, ribbed approach slabs are proposed, providing advantages over flat slabs. Based on finite element analysis, internal forces and deformations of ribbed slabs have been predicted and their designs are conducted. Secondly, a fully computerized vehicle-bridge coupled model has been developed to analyze the effect of approach slab deformation on bridges’ dynamic response induced by moving vehicles. With this model, the dynamic performance of vehicles and bridges under different road conditions (including approach slab deformation) can be obtained for different numbers and types of vehicles, and different types of bridges. A parametric study reveals that the deformation at the approach span causes significant dynamic responses in short span bridges. AASHTO specifications may underestimate the impact factors for short bridges with uneven joints at the bridge ends. Finally, this study investigated the possibility of using tuned mass damper (TMD) to suppress the vehicle-induced bridge vibration under the condition of uneven bridge expansion joints

Recursive Utility Maximization for Terminal Wealth under Partial Information

This paper concerns the recursive utility maximization problem for terminal wealth under partial information. We first transform our problem under partial information into the one under full information. When the generator of the recursive utility is concave, we adopt the variational formulation of the recursive utility which leads to a stochastic game problem and characterization of the saddle point of the game is obtained. Then, we study the -ignorance case and explicit saddle points of several examples are obtained. At last, when the generator of the recursive utility is smooth, we employ the terminal perturbation method to characterize the optimal terminal wealth

A model-driven deep reinforcement learning heuristic algorithm for resource allocation in ultra-dense cellular networks

Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) and fairness. The existing methods can not effectively solve this NP-hard nonconvex problem, especially in the presence of limited channel state information (CSI). In this paper, we investigate a novel model-driven deep reinforcement learning assisted resource allocation method. We first design a novel deep neural network (DNN)-based optimization framework consisting of a series of Alternating Direction Method of Multipliers (ADMM) iterative procedures, which makes the CSI as the learned weights. Then a novel channel information absent Q-learning resource allocation (CIAQ) algorithm is proposed to train the DNN-based optimization framework without massive labeling data, where the SE, the EE, and the fairness can be jointly optimized by adjusting discount factor. Our simulation results show that, the proposed CIAQ with rapid convergence speed not only well characterizes the extent of optimization objective with partial CSI, but also significantly outperforms the current random initialization method of neural network and the other existing resource allocation algorithms in term of the tradeoff among the SE, EE and fairness