The resource discovery is a critical component of the P2P file-sharing network. However, because of the huge overhead of locating operation or management, neither the traditional methods, such as Flooding, EPS and Random Walks, provide high performance for this process, nor do the recent ones such as Chord, CAN, and so on. To conquer this problem, locality of the underlying network should be taken into account when constructing the P2P networks. It can shorten the length of routes in network layer and reduce the bandwidth consumed when locating the resource. Meanwhile, semantic overlay is another powerful way to organize the P2P nodes. In the semantic overlay, the nodes with semantically similar content are "clustered" together, which can facilitate the resource discovery. Based on these two characters, we propose a new architecture of resource discovery which incorporates the underlying locality into the semantic overlays using decentralized group concept. The query is processed in the group one by one and the major management operations are in the group. In this way, the globe status maintenance can be avoided. The mathematical analysis and simulation results also show that the performance of new mechanism has been enhanced largely, including average diameter, average management overhead, average searching overhead, and so on.

In order to study the causes of arch foot cracking, a multiscale numerical simulation method was used to establish the finite element model of Xizha Bridge during the construction stage of the Xiaoqing River restoration project in Jinan by using Midas Civil, and the internal forces under adverse conditions were extracted. On this basis, Abaqus was used to establish the local model of arch foot, and the plastic damage model parameters were introduced to conduct stress analysis. The results show that the anchorage stress of prestressed steel bundle is too high. On the one hand, the stress component produced by the bending of the prestressed steel bundle can squeeze the concrete inside the bending angle, and on the other hand, it will stretch the concrete outside the bending angle, resulting in concrete cracking. There is a tendency of relative displacement between arch rib and arch foot, and the interface surface of arch foot and arch rib is pulled by the displacement of arch rib, resulting in cracking. Arch foot inner bend produces a certain tensile stress, and if this place is not paid enough attention to, insufficient reinforcement will produce large cracks. Finally, it is suggested that concrete cracking can be avoided by arranging enough reinforcement bars under anchor and sealing reinforcement bars, encrypting steel mesh, arranging shear studs, and extending insertion depth.

In order to realize the simple application of stochastic subspace identification in the modal analysis of bridge structures under ambient excitation, the data preprocessing theory is used to process the original vibration signal, and restore the most real data under the actual situation. At the same time, the data-driven stochastic subspace method (Data-SSI) is used to identify the preprocessed data, and the singular value result graph with obvious jump points is obtained, which makes it easy to determine the order of the system clearly. The results show that, after pre-processing the original data, the order of the system can be determined conveniently by using the Data-SSI method, and the results obtained are reliable.

The design of health monitoring scheme for long-span continuous rigid frame bridge with high piers includes the determination of monitoring indexes, the types of sensors and the installation positions, etc. In this paper, the Baijianhe Bridge is taken as the engineering background, and the MIDAS software is used to carry out the finite element modeling analysis, the static characteristics and dynamic response of the bridge under live load, dead load and temperature load are analyzed. It is found that the maximum values of stress and deflection occur at the mid-span position and the joint surface of pier and beam, which should be monitored in real time.

In order to calculate the vehicle load model of Baijianhe Bridge, based on the vehicle load data of the health monitoring system, the vehicle type, vehicle weight, wheelbase, and other information were counted and the data were processed and diagraphed to obtain the probability density distribution. At the same time, the automobile load model parameters relative to the national current code and finite element method are calculated. The results show that 2-axle and 6-axle vehicles are the main vehicle types, accounting for about 48%. The number of upstream and downstream vehicles is the same, but the number of vehicles in the lane is much higher than in the overtaking lane. The carriageway is dominated by 6-axle vehicles, while the overtaking lane is dominated by 2-axle vehicles. The probability density distribution of vehicle weight in overtaking lane obeys mixed Gaussian distribution and that in the carriageway obeys Weibull distribution. According to the measured vehicle load data, the vehicle load suitable for Baijianhe Bridge is 1.1 times the highway-I vehicle load of the current Chinese standard “General Code for Design of Highway Bridges and Culvers” (JTG D60-2015).