To address the problem of sensor faults and measurement noise being misinterpreted as structural damage in structural health monitoring (SHM), this paper proposes a new framework for distinguishing sensor faults and structural damage based on stacked gated recurrent neural networks (S-GRU NN) that considers measurement noise. In this framework, the sensor signal reconstruction model was constructed by learning and training the S-GRU NN. The sensor fault threshold was determined based on a statistical analysis of the response reconstruction error between the true and reconstruction values. The sensor fault and structural damage are then distinguished by the fact that the sensor fault is independent and the structural damage is global. The framework is compared with other isolation frameworks based on traditional deep learning models through numerical simulations of a three-span continuous beam and laboratory steel frame experiments. The results show that the S-GRU NN has better reconstruction effect and isolation performance of sensor faults and structural damage in noisy environment.

This paper presents experimental and numerical investigations of the seismic failure of the reinforced and unreinforced monoliths of the Huangdeng concrete gravity dam. To verify the scale factors, we use suitable materials (emulation concrete material and fine alloy wire) to simulate the dam concrete and the steel reinforcement (SR) in a scaled experiment model that includes the water-retaining monolith and overflow monolith of the dam. We design shaking table model tests based on the similarity laws and perform nonlinear numerical simulations of damage to the dam. By comparing the numerical simulation with the experimental results, the intervals for peak acceleration, in which microcracks appear and macrocracks rapidly expand, are obtained. The modal and damage distribution results verify the proposed design method for the scaled experimental model with SR. By analyzing the results, we reveal the crack resistance mechanism of SR. This research provides a rational foundation for further study of the similarity laws for reinforced dams.

A novel blind watermarking method for the 3D mesh model is proposed. This method is based on the geometry image which is created by the parameterization and sampling of the model. The geometry image except the boundary is partitioned into several sub-images of equal size. Every sub-image is normalized and decomposed with two-dimensional Haar wavelet. The watermark is embedded into the low components of the wavelet coefficients with the LSB method. Other conventional 2D image watermarking methods could also be applied. The watermarked model is reconstructed from the embedded geometry image. For the extraction of the watermark, the original model is not required. Experimental results show the proposed algorithm is robust against the attacks such as translation, scaling, rotation, local deformation and certain amplitude of noise disturbance.

In order to overcome the defect that the general shaking table test can’t clarify the scale relationship between the prototype structure and the scale model in the damaged area based on the fully similar laws, we derived a method that can analyze the relationship between the prototype structure and the scale model in the damaged area based on non-fully similar lows and verified it. We selected the appropriate material (the emulation concrete material, ECM), which has a good scale relationship with the prototype material in the elastic stage, the crack appearance stage, and crack propagation stage (damage stage). We proposed some similar ratios to reveal the similar relationship between the prototype structure and the scale model in the elastic stage, the crack appearance stage, and the crack propagation stage. Especially, we proposed two similar ratios: c_r and h_r (deduced by the fracture toughness similarity requirements and the fracture energy similarity requirements, respectively) which can well reflect the similar relationship between the prototype structure and the scale model in the damaged area. In order to verify these ratios, we compared the results of the prototype structure numerical simulation with those of the shaking table test of the scale model. The results show that h_r has higher accuracy for predicting the relationship between the prototype structure and the scale model in the damaged area.

三仓出版社主要从事中文国际开源期刊的出版与发行，期刊领域涵盖工程技术、数学与物理、人文社会、信息科学、经济与管理、医药卫生、农业、地球环境等学科，借助互联网思维及现代信息技术，立足国内高校及研究机构的科研需求，力图推动中国学术界知识的交流与传播，可期刊发表、发表论文、快速发表，见刊快。三仓出版社欢迎广大科研学者加入我们，参与审稿，合作办刊，投稿荐稿，与中文期刊一同成长壮大。如果您对期刊有任何的意见或者建议，请联系service@sciscanpub.com.

This paper researches the problem that different thicknesses of silt of earthquake response and the differently distributed layered sediment with certain thicknesses for the seismic response of concrete dam. In this paper, there are layered silt layers of different thicknesses, and it is divided into three different forms of layered silt distributed based on the pressure wave equation of saturated porous fluid-solid two-phase medium. Under the conditions of both the same and the different physical parameter distribution of each distribution form of each layer, the magnitude of influence of silt on the response of concrete dam is determined. The analysis shows that the impact from the same physical properties with different forms of distrubition on seismic response is not significant, but the different distrubution forms have a larger impact on seismic response of the dam.