Experimental and numerical investigation of the seismic behavior of rectangular tunnels in soft soils

Underground structures constitute crucial components of the transportation networks. Considering their significance for modern societies, their proper seismic design is of great importance. However, this design may become very tricky, accounting of the lack of knowledge regarding their seismic behavior. Several issues that are significantly affecting this behavior (i.e. earth pressures on the structure, seismic shear stresses around the structure, complex deformation modes for rectangular structures during shaking etc.) are still open. The problem is wider for the non-circular (i.e. rectangular) structures, were the soilstructure interaction effects are expected to be maximized. The paper presents representative experimental results from a test case of a series of dynamic centrifuge tests that were performed on rectangular tunnels embedded in dry sand. The tests were carried out at the centrifuge facility of the University of Cambridge, within the Transnational Task of the SERIES EU research program. The presented test case is also numerically simulated and studied. Preliminary full dynamic time history analyses of the coupled soil-tunnel system are performed, using ABAQUS. Soil non-linearity and soil-structure interaction are modeled, following relevant specifications for underground structures and tunnels. Numerical predictions are compared to experimental results and discussed. Based on this comprehensive experimental and numerical study, the seismic behavior of rectangular embedded structures is better understood and modeled, consisting an important step in the development of appropriate specifications for the seismic design of rectangular shallow tunnels.

Seismic behaviour of barrel vault systems

In this paper, the experimental study on the rocking behaviour of a full scale barrel vaulted structure undergo cyclic horizontal loading is discussed. The study is the first part of an ongoing experimental and theoretical research program, developed by the University of Brescia, concerning the seismic behaviour of masonry buildings. The scope of the paper is to provide some evidence of the rocking mechanism experienced by barrel vaulted structures undergo horizontal loading. Understanding of the behaviour of such structural systems is fundamental for their seismic vulnerability assessment, as well as for the correct design of possible strengthening techniques. The structural behaviour is also investigated by means of non linear finite element analyses. Numerical results are validated through comparison with experimental results. After validation, the FE model can be applied to different case studies.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

Wavelet denoising and feature extraction of seismic signal for footstep detection

Seismic sensors are widely used to detect moving target in ground sensor networks. Footstep detection is very important for security surveillance and other applications. Because of non-stationary characteristic of seismic signal and complex environment conditions, footstep detection is a very challenging problem. A novel wavelet denoising method based on singular value decomposition is used to solve these problems. The signal-to-noise ratio (SNR) of raw footstep signal is greatly improved using this strategy. The feature extraction method is also discussed after denosing procedure. Comparing, with kurtosis statistic feature, the wavelet energy feature is more promising for seismic footstep detection, especially in a long distance surveillance.