Recovering the functional form of the nonlinear roll damping of ships from a free-roll decay experiment: An inverse formulism

The purpose of this paper is to identify the functional form of the nonlinear roll damping for a particular ship based on an experiment. The problem of damping identification is formulated as an integral equation of the first kind. However, the solution of the problem lacks stability properties, due to the ill-posedness of the first-kind integral equation. To resolve this problem, a stabilization technique (known as a regularization method) is applied to the present problem of the identification of nonlinear damping. The identified results for nonlinear roll dampings are compared with those from a conventional roll identification method. The findings of the present study are validated by the direct comparison of experimental data on free-roll decay motion with the numerically simulated results.     

Pre- and post-earthquake regional loss assessment using deep learning

As urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community-level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre- and post-earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction of individual structures during the pre-earthquake loss assessment, a widely used nonlinear static procedure is replaced by the recently developed probabilistic deep neural network model. The variabilities of the nonlinear responses of a structural system given the seismic intensity can be quantified during the loss assessment process. Second, to facilitate near-real-time post-earthquake loss assessments, an adaptive algorithm, which identifies the optimal number and locations of sensors in a given urban area, is proposed. Using a deep neural network that estimates area-wide structural damage given the spatial distribution of the seismic intensity levels as a surrogate model, the algorithm adaptively places additional sensors at property lots at which errors from surrogate estimations of the structural damage are the greatest. Note that the surrogate model is constructed before earthquake events using simulated datasets. To test and demonstrate the proposed frameworks, the paper introduces thorough numerical investigations of two hypothetical urban communities. The proposed frameworks using the deep learning methods are expected to make critical advances in pre- and post-earthquake regional loss assessments.     

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.     

The effects of drag reduction by ribbons attached to cylindrical pipes

This study proposes a drag reduction device that uses three ribbons attached 120 degree apart to vertical pipes. Experiments were conducted in a circulating water channel to investigate the effects of the ribbon length and the direction of the flow on various current velocities. Drag on a vertical cylinder was measured by a resistance dynamometer. Flow visualizations were conducted using laser sheet beams. Laser Doppler Velocimetry (LDV) was used to measure the velocity field in the wake. This experiment demonstrates that attached ribbons can be used to reduce the drag force on vertical pipes for various directions of incoming flows. The ribbon-type device is very simple and easy to fabricate for field applications. The results are promising for the application to offshore structures.     

Nonlinear wave profiles of wave–wave interaction in a finite water depth by fixed point approach

In this paper, a superposition of two periodic wave profiles in a finite water depth was investigated. This paper is focused on the improvement of a wave profile on the linear superposition of two waves. This improvement was realized by introducing an iterative method, which was based on a fixed point approach. Application of the fixed point approach to the wave superposition made it possible to obtain a wave profile of wave–wave interaction. The improved result of the wave profile was in good agreement with that of the nonlinear perturbation solution of the second order. It was interesting that the improved result revealed the higher-order nonlinear frequencies for two interacting Stokes waves while Dalzell's solution by a perturbation method could not predict them.     

Kernel adaptive subspace detector for hyperspectral imagery

In this letter, we present a kernel-based nonlinear version of the adaptive subspace detector (ASD) that implicitly detects signals of interest in a high-dimensional (possibly infinite) feature space associated with a particular nonlinear mapping. In order to address the high dimensionality of the feature space, ASD is first implicitly formulated in the feature space, which is then converted into an expression in terms of kernel functions via the kernel trick property of the Mercer kernels. Experimental results based on simulated data and real hyperspectral imagery show that the proposed kernel-based ASD outperforms the conventional ASD and a nonlinear anomaly detector so called the kernel RX-algorithm.     

Prediction of Typhoon Tracks Using Dynamic Linear Models

This paper presents a study on the statistical forecasts of typhoon tracks. Numerical models have their own systematic errors, like a bias. In order to improve the accuracy of track forecasting, a statistical model called DLM (dynamic linear model) is applied to remove the systematic error. In the analysis of typhoons occurring over the western North Pacific in 1997 and 2000, DLM is useful as an adaptive model for the prediction of typhoon tracks.     

Geostatistical integration of gravity and magnetotelluric data to enhance resolution of geologic structure

To enhance the spatial resolution, two types of complementary integration methods were developed using gravity and magnetotelluric (MT) data. The first method involves the enhancement processing of gravity vertical resolution using MT data. This is called a layer density correction (LDC) process which makes the density distribution more sensitive to geologic structure. The second method involves the spatial expansion processing of MT data using the enhanced gravity data. In this process, non-linear indicator transformation (NLIT) and simple kriging with varying local means (SKlm) methods were employed. The assumptions are that while the results are analyzed by different physical properties, each method senses the same underlying geologic structure and thus there is a relationship among the physical properties. The proposed geostatistical integration methods were tested using synthetic models and field data. The experiment illustrates that the integration method proposed in this study can provide improved structures. The method integrates constructively the gravity information having a wider spatial distribution and the MT information having higher vertical resolution.     

Numerical models of RC elements and their impacts on seismic performance assessment

This paper aims to provide a guideline for numerical modeling of reinforced concrete (RC) frame elements for the seismic performance assessment of a structure. Several types of numerical models of RC frame elements are available in nonlinear structural analysis packages. Because the numerical models are formulated based on different assumptions and theories, the models' accuracy, computing time, and applicability vary, which poses a great difficulty to practicing engineers and limits their confidence in the analysis results. In this study, the applicability of five representative numerical models of RC frame elements is evaluated through comparison with 320 experimental results available from the Pacific Earthquake Engineering Research column database. The accuracy of a numerical model is evaluated according to its initial stiffness, peak strength, and energy dissipation capacity of the global responses. In addition, a parametric study of a cantilever RC column subjected to earthquake excitation is carried out to systematically evaluate the consequence of the adopted numerical models on the maximum inelastic structural responses. It is found from this study that the accuracy of the numerical models is sensitive to shear force demand–capacity ratio. If a structural period is short and the structure is shear critical, the use of numerical models that can explicitly capture the shear deformation and failure is suggested. If the structural period is long, the selection of a numerical model does not greatly influence the global response of the structure. The paper also presents statistical parameters of each numerical model, which can be used for probabilistic seismic performance assessment. Copyright © 2014 John Wiley & Sons, Ltd.     

Effective periods and seismic performance of steel moment resisting frames designed for risk categories I and IV according to IBC2009

In current seismic design, structures that are essential for post‐disaster recovery, and hazardous facilities are classified as risk category IV and are designed with higher importance factors and stringent drift limits. These structures are expected to perform better in an earthquake event because a larger base shear and more stringent drift limit are used. Although this provision has been in the seismic design code over the last three decades, few studies have investigated the performance of essential structures. The aim of this study is to quantify the impact of higher importance factors and stringent drift limits on the seismic performance of steel moment resisting frames. A total of 16 steel structures are designed for Los Angeles and Seattle. Different risk categories are used for the design. The effects of the risk categories on the structural periods, and thus on the seismic force demand, are investigated. A suite of inelastic time history analyses are carried out to understand the probability of exceeding a specified limit state when the structures are subjected to different levels of earthquake events. The results show that the periods of the structures in risk category IV decrease by a factor of 0.5 to 0.8, and the strengths increase by a factor of 1.5 to 3.2. Seismic fragility analysis shows that the structures in risk category IV generally satisfy the probabilistic performance objectives. Copyright © 2014 John Wiley & Sons, Ltd.