Nonlinear dynamic finite element simulation of Alibey earth dam

In this study, dynamic behavior and earthquake resistance of Alibey earth dam was investigated. The dam was modeled with four node plane-strain finite elements (FE) and displacement-pore pressure coupled FE analyses were performed. Nonlinear material models such as pressure dependent and independent multi yield materials were implemented during the analyses. Transient dynamic FE analyses were performed with Newmark method. The Newton-Raphson solution scheme was adopted to solve the equations. Liquefaction and/or cyclic mobility effects were considered during the analysis. For the FE analyses, OpenSees (Open System for Earthquake Engineering Simulation) framework was adopted.     

Investigating Correlations and Variations of Air Pollutant Concentrations under Conditions of Rapid Industrialization – Kocaeli (1987–2009)

In this study, it was aimed to characterize temporal variations of air pollutants for determining contribution to pollution episodes and to obtain correlations between these pollutants. With this aim we used data analysis for measured sulfur dioxide (SO₂), particulate matter (PM, black fume and PM10), nitrogen oxides (NO_x), ozone (O₃), carbon monoxide (CO), methane (CH₄), and non‐methane hydrocarbons (NMHC) recorded in Kocaeli, one of the most industrilizated cities of Turkey. Pollutant concentrations were the results of continuous and semi‐automatic measurements. Semi‐automatic measurements of SO₂ and PM (black fume) were enclosing period from 1987 to 2008 whereas continuous monitoring of all pollutants included years of 2007–2009. In the first stage of the study daily, monthly, annual, and seasonal variations of pollution were researched. Annual average concentrations were compared with limits set by Air Quality Protection Regulation (AQPR), Air Quality Evaluation and Management Regulation (AQEMR), World Health Organization (WHO), European Union (EU), and National Ambient Air Quality Standards (USEPA). In the following stage relationships between pollutants such as NO₂–O₃, NO_x–CO, NO_x–NMHC, and NO_x–SO₂ were investigated and correlation coefficients were determined as 0.87, 0.56, 0.51, and 0.69, respectively. R² values of regression models developed from these correlations were 0.78, 0.56, 0.34, and 0.72, respectively. Vehicle density of the traffic was evaluated with NO_x–O₃ emissions and decrease was seen in NO_x emissions due to decreasing vehicle density at weekends whereas O₃ concentrations increased. These correlations enable prediction of the parameters that cannot be measured which is important for providing improvement in early warning systems.     

Influence of Two Tropical Storms on the Residual Flow in a Subtropical Tidal Inlet

The mechanisms responsible for the modulation of laterally sheared non-tidal (residual) exchange flow in a subtropical inlet, with special emphasis on tropical storm influence, are studied using a combination of current velocity profiles and hydrographic and meteorological data. The mouth of the inlet, St. Augustine Inlet in northeast Florida, is characterized by a 15-m-deep channel flanked by shoals (<6 m deep). Residual flows across the inlet mouth were laterally sheared with inflow in the channel and outflow over the shoals. This pattern persisted during four separate semi-diurnal tidal cycle surveys effected over 3 years. During spring tides, residual exchange flows intensified relative to neap tides. Residual inflow in the channel only reversed immediately after tropical storms because of their extreme winds and major temporal changes in water level. After the residual flow reversed in the channel, along-channel baroclinicity drove gravitational circulation that persisted for 4.5 days and was enhanced by offshore winds. A depth-averaged along-basin momentum budget highlighted the importance of bottom friction to help balance the barotropic pressure gradient. The rest of the momentum budget was likely provided by advective terms. During and after tropical storms, accelerations from wind stress and baroclinic pressure gradients also became influential in the along-basin momentum budget.     

Modeling the Effects of Meteorological Factors on SO2 and PM10 Concentrations with Statistical Approaches

Air quality has been deteriorated seriously in urban areas as a result of increasing anthropogenic activities. Meteorological conditions affect air pollution levels in the urban atmosphere significantly due to their important role in transport and dilution of the pollutants. This paper aims to investigate usability of some promising statistical methods for examining the impacts of metrological factors on SO₂ and PM10 levels. Data were collected from city centre of Kocaeli in winter periods from 2007 to 2010 as pollutant concentrations increase in winters due to expanding combustion facilities. Results of bivariate correlation analysis showed that humidity and rainfall have remarkable negative correlations with the pollutants. Multiple linear regression models and artificial neural network (ANN) models were used to predict next day's PM10 and SO₂ levels. In regression models calculated R² values were 0.89 and 0.75 for PM10 and SO₂, respectively. Among the various architectures, single layer networks provided better performance in ANN applications. Highest R² values were obtained as 0.89 and 0.69 for PM10 and SO₂, respectively, by using appropriate networks.     

Direct use of PGV for estimating peak nonlinear oscillator displacements

A predictive model is presented for estimating the peak inelastic oscillator displacements (S_d,ie) from peak ground velocity (PGV). The proposed model accounts for the variation of S_d,ie for bilinear hysteretic behavior under constant ductility (µ) and normalized lateral strength ratio (R) associated with postyield stiffness ratios of α=0 and 5%. The regression coefficients are based on a ground‐motion database that contains dense‐to‐stiff soil site recordings at distances of up to 30 km from the causative fault. The moment magnitude ( M ) range of the database is 5.2? M ?7.6 and the ground motions do not exhibit pulse‐dominant signals. Confined to the limitations imposed by the ground‐motion database, the model can estimate S_d,ie by employing the PGV predictions obtained from the attenuation relationships (ground‐motion prediction equations). In this way, the influence of important seismological parameters can be incorporated to the variation of S_d,ie in a fairly rationale manner. This feature of the predictive model advocates its implementation in the probabilistic seismic hazard analysis that employs scalar ground‐motion intensity indices. Various case studies are presented to show the consistent estimations of S_d,ie by the proposed model. The error propagation in the S_d,ie estimations is also discussed when the proposed model is associated with attenuation relationships. Copyright © 2008 John Wiley & Sons, Ltd.     

A comparative study on linear and nonlinear site response analysis

Site response analysis is usually the first step of any seismic soil-structure study. Geotechnical earthquake engineers and engineering geologist have been trying to find both practical and most appropriate solution techniques for ground response analysis under earthquake loadings. The paper attempts to give a critical overview of the field of site response analysis. In this paper, the influences of nonlinearity on the site response analysis summarized and were evaluated with a numerical example. Site response of a two layered soil deposit with the assumption of linear and rigid base bedrock (or viscoelastic half-space) was analyzed by using linear and nonlinear approaches. The amplification spectrum of the soil column is computed between the top and the bottom of this soil deposit. Nonlinear analysis was compared with the linear method of analysis. Steps involved in ground response analyses to develop site-specific response spectra at a soil site are briefly summarized. Some of the well-known site response analysis methods are summarized and similarities and differences between linear and nonlinear methods are compared by a numerical example.     

An adaptive Markov chain algorithm applied over map-matching of vehicle trip GPS data

Markov chains have frequently been applied to match the probable routes with a set of GPS trip data that a pilot vehicle is emitting over a specific graph road network. This class of map- matching (MM) algorithms presently demonstrates and involve statistical and ad-hoc measures to drive the Markov chain transitional probabilities in picking the best route combinations constrained over the graph road network. In this study, we have devised an adaptive scheme to modify the Markov Chain (MC) kernel window as we move along the GPS samples to reduce the mistakes that can happen by the use of narrower MC widths. The measure for temporarily increasing the MC window width is chosen to be the ratio between the geodesic distance of current route to the actual geodesic distance between each pair of GPS samples. This adaptive use of MC has shown to have hardened the results significantly with tolerable computational cost increase. The details of the overall algorithm are depicted by the example routes extracted from various vehicle trips and the results are shown to validate the usefulness of the algorithm in practice.