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.     

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.     

Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea

The aim of this study is to analyze hydrothermal gold–silver mineral deposits potential in the Taebaeksan mineralized district, Korea, using an artificial neural network (ANN) and a geographic information system (GIS) environment. A spatial database considering 46 Au and Ag deposits, geophysical, geological, and geochemical data was constructed for the study area using the GIS. The geospatial factors were used with the ANN to analyze mineral potential. The Au and Ag mineral deposits were randomly divided into a training set (70%) to analyze mineral potential using ANN and a test set (30%) to validate predicted potential map. Four different training datasets determined from likelihood ratio and weight of evidence models were applied to analyze and validate the effect of training. Then, the mineral potential index (MPI) was calculated using the trained back-propagation weights, and mineral potential maps (MPMs) were constructed from GIS data for the four training cases. The MPMs were then validated by comparison with the test mineral occurrences. The validation results gave respective accuracies of 73.06, 73.52, 70.11, and 73.10% for the training cases. The comparison results of some training cases showed less sensitive to training data from likelihood ratio than weight of evidence. Overall, the training cases selected from 10% area with low and high index value of MPM_L and MPM_W gave higher accuracy (73.52 and 73.10%) for MPMs than those (73.06 and 70.11%, respectively) from known deposits and 10% area with low index value of MPI_L and MPI_W.     

Design of a low-cost attitude determination GPS/INS integrated navigation system

Due to their complementary features of GPS and INS, the GPS/INS integrated navigation system is increasingly being used for a variety of commercial and military applications. An attitude determination GPS (ADGPS) receiver, with multiple antennas, can be more effectively integrated with a low-cost IMU since the receiver gives not only position and velocity data but also attitude data. This paper proposes a low-cost attitude determination GPS/INS integrated navigation system. The proposed navigation system comprises an ADGPS receiver, a navigation computer unit (NCU), and a low-cost commercial MEMS IMU. The navigation software includes a fault detection and isolation (FDI) algorithm for integrity. In order to evaluate the performance of the proposed navigation system, two flight tests have been performed using a small aircraft. The first flight test confirmed the fundamental operation of the proposed navigation system and the effectiveness of the FDI algorithm. The second flight test evaluated the performance of the proposed navigation system and demonstrated the benefit of GPS attitude information in a high dynamic environment. The flight test results show that the proposed ADGPS/INS integrated navigation unit gives reliable navigation performance even when anomalous GPS data is provided and gives better navigation performance than a conventional GPS/INS unit.     

Maritime trade, biological invasions, and the properties of alternate inspection regimes

We analyze the problem of preventing biological invasions caused by ships transporting internationally traded goods between countries and continents. Specifically, we ask the following question: should a port manager have a small number of inspectors inspect arriving ships less stringently or should this manager have a large number of inspectors inspect the same ships more stringently? We use a simple queuing-theoretic framework and show that if decreasing the economic cost of regulation is very important then it makes more sense for the port manager to choose the less stringent inspection regime. In contrast, if reducing the damage from biological invasions is more salient then the port manager ought to pick the more stringent inspection regime.     

Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS

This study constructs a hazard map for ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using a probability (frequency ratio) model, a statistical (logistic regression) model, and a Geographic Information System (GIS). To evaluate the factors related to ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, Global Positioning System (GPS) data, land use map, lineaments, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed from field investigations and reports on the existing ground subsidence areas at the study site. Nine major factors causing ground subsidence were extracted from the probability analysis of the existing ground subsidence area: (1) depth of drift; (2) DEM and slope gradient; (3) groundwater level, permeability, and rock mass rating (RMR); (4) lineaments and geology; and (5) land use. The frequency ratio and logistic regression models were applied to determine each factor’s rating, and the ratings were overlain for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with existing subsidence areas. The verification results showed that the logistic regression model (accuracy of 95.01%) is better in prediction than the frequency ratio model (accuracy of 93.29%). The verification results showed sufficient agreement between the hazard map and the existing data on ground subsidence area. Analysis of ground subsidence with the frequency ratio and logistic regression models suggests that quantitative analysis of ground subsidence near AUCMs is possible.