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The geotechnical properties of soil deposit and the variability associated with their probable distributions have a profound impact on the seismic response of a site. In the present work, t...

Site response is a function of the soil profile, and the probable distribution of the soil profile has a significant effect on the seismic site response. In the present study, the influence of random variations in soil characterizations on the site response is investigated using different probabilistic distributions. The important characteristics of the local soil, corresponding to the layering, the shear wave velocity ( V s ), the decrease in the nonlinear modulus, and the damping (MRD) curves, are considered when carrying out these random variations. Stochastic processes are generated by using different distribution models and keeping in mind the effect of the coefficients of the variations. In this research, a proposed procedure is developed and coded to perform the variations in soil characterizations. The coding of this new procedure is based on the original SHAKE91 framework. However, instead of using the fixed soil properties and profile, the uncertainties of the MRD curves, the layer thickness, and V s are generated as the input data. This analysis shows that the use of median V s , obtained from all the possible inputs under the different stochastic processes, yields good agreements with the baseline profile. Modelling the variabilities in the layering and the V s profile is seen to have a slight effect on the performance of the site response. Additionally, the results of these analyses indicate that the variabilities in nonlinear soil properties have a significant impact on the median surface response spectrum and the amplification spectrum of the surface motions.

Utility systems such as power and communication systems regularly experience significant damage and loss of service during hurricanes. A primary damage mode for these systems is failure of wooden utility poles that support conductors and communication lines. In this article, we present an approach for combining structural reliability models for utility poles with observed data on pole performance during past hurricanes. This approach, based on Bayesian updating, starts from an imperfect but informative prior and updates this prior with observed performance data. We consider flexural and foundation failure mechanisms in the prior, acknowledging that these are an incomplete, but still informative, subset of the possible failure mechanisms for utility poles during hurricanes. We show how a model-based prior can be updated with observed failure data, using pole failure data from Hurricane Katrina as a case study. The results of this integration of model-based estimates and observed performance data then offer a more informative starting point for power system performance estimation for hurricane conditions.

The structural deformed shape is important information to structural analysis. If the sufficient measuring points are secured at the structural monitoring system, reasonable and accurate structural deformation shapes can be obtained and structural analysis is possible using this deformation. However, the accurate estimation of the global structural shapes might be difficult if sufficient measuring points are not secure under cost limitations. In this study, SFSM-LS algorithm, the economic and effective estimation method for the structural deformation shapes with limited displacement measuring points is developed and suggested. In the suggested method, the global structural deformation shape is determined by the superposition of the pre-investigated structural deformed shapes obtained by preliminary FE analyses, with their optimum weight factors which lead minimization of the estimate errors. 2-span continuous bridge model is used to verify developed algorithm and parametric studies are performed. By the parametric studies, the characteristics of the estimation results obtained by the suggested method were investigated considering essential parameters such as pre-investigated structural shapes, locations and numbers of displacement measuring points. By quantitative comparison of estimation results with the conventional methods such as polynomial, Lagrange and spline interpolation, the applicability and accuracy of the suggested method was validated.

In recent years, fusion camera systems that consist of color cameras and Time-of-Flight (TOF) depth sensors have been popularly used due to its depth sensing capability at real-time frame rates. However, captured depth maps are limited in low resolution compared to the corresponding color images due to physical limitation of the TOF depth sensor. Most approaches to enhancing the resolution of captured depth maps depend on the implicit assumption that when neighboring pixels in the color image have similar values, they are also similar in depth. Although many algorithms have been proposed, they still yield erroneous results, especially when region boundaries in the depth map and the color image are not aligned. We therefore propose a novel kernel regression framework to generate the high quality depth map. Our proposed filter is based on the vector pointing similar pixels that represents the unit vector toward similar neighbors in the local region. The vectors are used to detect misaligned regions between color edges and depth edges. Unlike conventional kernel regression methods, our method properly handles misaligned regions by introducing the numerical analysis of the local structure into the kernel regression framework. Experimental comparisons with other data fusion techniques prove the superiority of the proposed algorithm.

This paper investigates the moment modification factors of an I-girder with trapezoidal web corrugations under moment gradient and various end restraint conditions. The moment modification factors are obtained by using the finite element method with beam elements and new general formulas of cross-section properties as well as a new warping constant of the I-girder with trapezoidal web corrugations. The theoretical results of moment modification factors are successfully verified by comparing them with the results from the commercial finite element software using shell elements. A series of finite element analyses with different corrugation profiles and lengths are investigated. From the results, it is found that the present design of moment modification factor formulas cannot satisfactorily predict the buckling capacities of the I-girder with trapezoidal web corrugations under unequal end moments with various end restraint conditions. For the purpose of design, the closed-form expressions for the moment modification factors are proposed and successfully verified.

Moment modification factors of the I-girder with trapezoidal web corrugations subjected to concentrated load, applied at different heights on the cross section and various end restraint conditions, are investigated. The moment modification factors are obtained by finite element buckling analyses. The new FEM program is developed by using beam elements and new general formulas of cross-section properties as well as a new warping constant of the I-girder with trapezoidal web corrugations. The theoretical results of moment modification factors are compared with commercial finite element software (ABAQUS) using shell elements. A series of finite element analyses with different corrugation profiles and lengths is performed. Through comparative numerical studies, the theoretical results are successfully verified. The moment modification factors from the SSRC Guide are compared with present FEM results. The new values of the variable C b , the moment modification factor, in the SSRC Guide are proposed as the variable C b,c to improve the accuracy of lateral–torsional buckling strength.

Presently, composite method for steel and concrete is often used the stud. Steel properties of composite column could be changed by increasing of welding. The changed properties is possibly to cause local-buckling. Composite column had a large effect by slip instead of pull-out force in comparison composite girder. Improvement of adhesive force had effect by contact area rather than height of stud in composite column. This paper proposed new type of stud and analyzed performance through experimental study. This method would be effect steel structure with curvature.

Hurricanes frequently cause damage to electric power systems in the United States, leading to widespread and prolonged loss of electric service. Restoring service quickly requires the use of repair crews and materials that must be requested, at considerable cost, prior to the storm. U.S. utilities have struggled to strike a good balance between over- and underpreparation largely because of a lack of methods for rigorously estimating the impacts of an approaching hurricane on their systems. Previous work developed methods for estimating the risk of power outages and customer loss of power, with an outage defined as nontransitory activation of a protective device. In this article, we move beyond these previous approaches to directly estimate damage to the electric power system. Our approach is based on damage data from past storms together with regression and data mining techniques to estimate the number of utility poles that will need to be replaced. Because restoration times and resource needs are more closely tied to the number of poles and transformers that need to be replaced than to the number of outages, this pole-based assessment provides a much stronger basis for prestorm planning by utilities. Our results show that damage to poles during hurricanes can be assessed accurately, provided that adequate past damage data are available. However, the availability of data can, and currently often is, the limiting factor in developing these types of models in practice. Opportunities for further enhancing the damage data recorded during hurricanes are also discussed.

While I-girders with trapezoidal web corrugations have been used in various structural applications and bridges, lateral torsion–flexure of I-girders with trapezoidal web corrugations still needs to be investigated, especially the general formulation for cross-section properties. This paper presents the results of theoretical and finite element analyses of the lateral-torsional buckling of an I-girder with trapezoidal web corrugations under uniform moment. By using the numerical equations and method, the location of shear center and warping constant are proposed. The elastic lateral-torsional buckling strength is then calculated via proposed cross-section properties. The results are compared with previous studies in the literature. A series of finite element analyses with different corrugation profiles and lengths are investigated and the results are compared with proposed formulas. Through comparative numerical studies, the proposed formulas are successfully verified. The effects of corrugation profiles on elastic lateral-torsional buckling strength are also investigated and discussed.

Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.

Hurricanes have caused severe damage to the electric power system throughout the Gulf coast region of the US, and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing power outage risk and preparing for post-storm recovery efforts requires accurate methods for estimating the number and location of power outages. This paper builds on past work on statistical power outage estimation models to develop, test, and demonstrate a statistical power outage risk estimation model for the Gulf Coast region of the US. Previous work used binary hurricane-indicator variables representing particular hurricanes in order to achieve a good fit to the past data. To use these models for predicting power outages during future hurricanes, one must implicitly assume that an approaching hurricane is similar to the average of the past hurricanes. The model developed in this paper replaces these indicator variables with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The models were developed using data about power outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast region.

Time-varying mesh, which is attracting a lot of attention as a new multimedia representation method, is a sequence of 3-D models that are composed of vertices, edges, and some attribute components such as color. Among these components, vertices require large storage space. In conventional 2-D video compression algorithms, motion compensation (MC) using a block matching algorithm is frequently employed to reduce temporal redundancy between consecutive frames. However, there has been no such technology for 3-D time-varying mesh so far. Therefore, in this paper, we have developed an extended block matching algorithm (EBMA) to reduce the temporal redundancy of the geometry information in the time-varying mesh by extending the idea of the 2-D block matching algorithm to 3-D space. In our EBMA, a cubic block is used as a matching unit. MC in the 3-D space is achieved efficiently by matching the mean normal vectors calculated from partial surfaces in cubic blocks, which our experiments showed to be a suboptimal matching criterion. After MC, residuals are transformed by the discrete cosine transform, uniformly quantized, and then encoded. The extracted motion vectors are also entropy coded after differential pulse code modulation. As a result of our experiments, 10%-18% compression has been achieved.

In recent years, fusion camera systems that consist of color cameras and Time-of-Flight (TOF) depth sensors have been popularly used due to its depth sensing capability at real-time frame rates. However, captured depth maps are limited in low resolution compared to the corresponding color images due to physical limitation of the TOF depth sensor. Although many algorithms have been proposed, they still yield erroneous results, especially when boundaries of the depth map and the color image are not aligned. We therefore propose a novel kernel regression framework to generate the high quality depth map. Our proposed filter is based on the vector pointing homogeneous pixels that represents the unit vector toward similar neighbors in the local region. The vectors are used to detect misaligned regions between color edges and depth edges. Experimental comparisons with other data fusion techniques prove the superiority of the proposed algorithm.

Stereoscopic images are captured by two cameras at different positions. In general, the two images often have geometric distortions including vertical misalignment, rotation and keystone as well as photometric distortions such as luminance or color differences. Even in case of a carefully designed parallel stereo camera configuration, the captured images pairs may have the distortions that cause uncomfortable 3D experiences to users. In this paper, we develop an algorithm to correct the captured image pairs to give a comfortable stereoscopic experience to users. The algorithm provides a practical method for compensating the photometric and geometrical distortions.

Conventional machining process of freeform surfaces usually employs three-stage machining process of roughing, semi-finishing and finishing. This multi-stage machining process has some weaknesses such as the repetition of process planning activities or heavy cutting load changes. But they can be overcome by gradually machining a stock along morphing surfaces. The generation of the morphing surfaces transforming smoothly inside the to-be-cut volume is essential for the new machining process and it can be achieved by the volume interior parameterization. This paper introduces five volume interior parameterization methods: TFI method, Laplace method, Poisson method using exponential control functions, Poisson method using arc-length based control map and weighted parametric redistribution method. Algorithms for these methods are presented, and their respective computer simulations packages are generated. Numerical examples show that the algorithms can adequately generate intermediate machining surfaces inside any arbitrary shape volume.

Hurricanes frequently cause damage to electric power systems in the United States, leading to widespread and prolonged loss of electric service. Restoring service quickly requires the use of repair crews and materials that must be requested, at considerable cost, prior to the storm. U.S. utilities have struggled to strike a good balance between over- and underpreparation largely because of a lack of methods for rigorously estimating the impacts of an approaching hurricane on their systems. Previous work developed methods for estimating the risk of power outages and customer loss of power, with an outage defined as nontransitory activation of a protective device. In this article, we move beyond these previous approaches to directly estimate damage to the electric power system. Our approach is based on damage data from past storms together with regression and data mining techniques to estimate the number of utility poles that will need to be replaced. Because restoration times and resource needs are more closely tied to the number of poles and transformers that need to be replaced than to the number of outages, this pole-based assessment provides a much stronger basis for prestorm planning by utilities. Our results show that damage to poles during hurricanes can be assessed accurately, provided that adequate past damage data are available. However, the availability of data can, and currently often is, the limiting factor in developing these types of models in practice. Opportunities for further enhancing the damage data recorded during hurricanes are also discussed.

We present a novel method that automatically generates a preview video of a mesh sequence. To make the preview appealing to users, the important features of the mesh model should be captured in the preview video, while preserving the constraint that the transitions of the camera are as smooth as possible. Our approach models the important features by defining a surface saliency and by measuring the appearance of the mesh sequence. The task of generating the preview video is then formulated as a shortest-path problem and we find an optimal camera path by using Dijkstra's algorithm.

Three-dimensional (3-D) video provides an immersing experience for users. In recent years, many attempts have been made to capture the complex surface shape and highly detailed texture of real-world moving objects, which results in a huge amount of data. In this paper, we discuss compression issues for 3-D video. We introduce 3-D video, which is classified into two categories. Then we survey compression methods that have been investigated for each category. We present our compression methods for temporally varying mesh sequences. In addition, we show comparison results for our algorithm with respect to previous work.

Time Varying Meshes (TVMs) are sequences of three-dimensional models created from multi-camera images. Like other kinds of mesh sequences, it has a great need for compression, in order to become largely usable. Alongside with other types of signals, mesh coding has received considerable attention towards scalable coding, which is a very important feature in many kinds of coded signals. Scalable mesh coding can be divided basically into three types of scalability: temporal, spatial and SNR (signal-to-noise ratio). In this work we focus on the use of block-based and run-length encoding aiming at achieving a combination of spatial and SNR scalability for the geometrical information of TVM. Our results have shown that it is possible to achieve scalability with only a minor increase in rate over a previous coding method.

Hurricanes have caused severe damage to the electric power distribution system in the U.S., leading to widespread, prolonged power outages. Electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas, and power restoration depends on effective pre-storm planning and resource allocation. Power outage estimates form the basis of pre-storm decisions that utility companies must make about the number of crews to request through mutual aid agreements and the locations where crews and materials should be staged in preparation for a recovery effort. Managing power outage risk and properly preparing for post-storm recovery efforts requires rigorous methods for estimating the number and location of power outages before a storm makes landfall. These estimates must be geographically detailed and accurate while also accounting for the complicated relationships between a number of possible explanatory variables and power outages. Previous research in estimating power outages during hurricanes has relied on negative binomial generalized linear models (GLMs) that use the standard linear relationship between the log of the expectation of the response variable Y (i.e., the number of power outages in a geographic area) and a set of explanatory variables given in the matrix X . However, these models have been shown to substantially overestimate the number of power outages in urban areas in some cases (Han et al. 2007a), and they do not provide insight into the possibility of non-linear relationships between the elements of X and the response variable. This paper shows how generalized additive models (GAMs), a class of semi-parametric regression models, can be used to more accurately estimate power outages during hurricanes. This is done through an analysis of a data set consisting of power outages in 6,681 3.66 km (12,000 foot) by 2.44 km (8,000 foot) grid cells during five hurricanes in the service area of a large, investor-owned utility company serving a large portion of a Gulf Coast state that has been impacted by a number of hurricanes in the past. The results show that GAMs can provide more accurate predictions of the number of power outages in each geographic area of a utility company's service area and a better understanding of the response of the system than GLMs do. The explanatory variables used in the regression model include information about (1) the winds experienced in each grid cell during each hurricane, (2) the long-term precipitation and the soil moisture levels in each grid cell at the time of the hurricane, (3) the power system components in each grid cell, and (4) land use and land cover in each grid cell. The underlying goal in developing an outage count prediction model is to provide a basis for choosing how many crews and materials to request from other utility companies as a hurricane is approaching and where to position these crews and materials in order to restore electric power as quickly as possible without incurring unnecessary expense.

Time-varying meshes (TVM) is a new 3-D scene representation which are generated from multiple cameras. It captures highly detailed shape and texture as well as movement of real-world moving objects. Compression is a key technology for supporting TVM applications such as education, interactive broadcasting, and intangible heritage archiving. Previous works focused on compression of 3-D animation that has the same topology throughout the entire sequences. Unfortunately, the topology of TVMs change with time which makes it difficult to compress TVMs. In this paper, we propose a geometry encoder for TVMs. The encoder finds spatial and temporal redundancy by coarse and fine level quantization. Thereafter, vertex information is converted into binary sequences. And then, the binary sequences are encoded using run-length encoding (RLE). Experimental results show that vertices of TVMs which require 96 bits per vertex (bpv) are compressed to 1.9-15.4 bpv while maintaining a small geometric distortion ranging from 0.7 times 10-4 to 1.3 times 10-3 % of the maximum error.

Three dimensional (3D) video is attracting a lot of attention as a new multimedia representation method. 3D video is a sequence of 3D models (frames) that consist of varying vertices and connectivity. In conventional 2D video compression algorithms, motion compensation (MC) using block matching algorithm is frequently employed to reduce redundancy between consecutive frames. However, there is no such technology for 3D video so far. Therefore, in this paper, we have developed an extended block matching algorithm (EMBA) to reduce temporal redundancy of geometry information of 3D video by extending the idea of 2D block matching to 3D space. In our EBMA, a cubic block is used as a matching unit and, MC is achieved efficiently by matching the mean normal vectors of the sub-blocks, which turned out to be sub-optimal by our experiments. The residual information is further transformed by discrete cosine transform (DCT) and then encoded. The extracted motion vectors are also entropy encoded. As a result of our experiments, compression ratio ranging from 10% to 18% of the original 3D video data has been achieved.