High-Modulus Columns for Liquefaction Mitigation

This paper presents the performance of a shopping complex in Turkey where the soils were improved with jet-grout columns and preload fills and subjected to the 1999 Kocaeli Earthquake (M = 7.4). Under construction at the time of the earthquake, the Carrefour Shopping Center covers an area of 55,000?m2 and is founded on shallow footings, mats, and slabs-on-grade that rest on soft, saturated alluvial sediments consisting of clays, silts, and sands. High-modulus columns constructed by jet grouting were installed at close-to-moderate spacings to reduce anticipated static settlements in the clays and mitigate liquefaction in the sands. The site was subjected to a peak acceleration of approximately 0.2g during the earthquake. Grouting had been completed for about two-thirds of the site when the earthquake struck. Following the event, a field reconnaissance found stark contrast between the performance of the improved and unimproved sections. The jet-grout-treated areas suffered no apparent damage, whereas the unimproved sections of the complex, along with nearby untreated building sites, commonly suffered liquefaction-related settlements of up to 10 cm. This is the only case history known to the authors that documents the field performance of high-modulus columns used in this manner for liquefaction mitigation and direct instrumented measurement of liquefaction-induced settlements.     

The variation of the dielectric constant and loss index with temperature and draw ratio in α‐PVDF

In this study, the effect of measurement temperature and uniaxial drawing on the real (dielectric constant, ε′) and imaginary (loss index, ε″) parts of the complex dielectric constant of α‐crystalline phase poly(vinylidene fluoride) (PVDF) was investigated. The samples having different draw ratios (λ) were obtained by drawing the PVDF film at constant speed and temperature. The dielectric measurements were performed in the frequency range of 100 Hz–1 MHz and in the temperature range of 80–400 K. Although ε′ and ε″ were not affected by the orientation process during the β‐relaxation transition, it was observed that there were systematical variations for the α‐relaxation transition. ε′ and ε″ showed different behaviors depending on the draw ratio at different temperatures. Especially, ε″ was more affected by the orientation process at 380 K. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Adaptive neuro-fuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas

A new method based on adaptive neuro-fuzzy inference system (ANFIS) for calculating the resonant frequency of rectangular microstrip antennas (MSAs) with thin and thick substrates is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The resonant frequency results obtained by using ANFIS are in excellent agreement with the experimental results reported elsewhere.     

Millimeter-wave Ground-based Synthetic Aperture Radar Imaging for Foreign Object Debris Detection: Experimental Studies at Short Ranges

In this paper, millimeter-wave imaging of foreign object debris (FOD)-type objects on the ground is studied with the help of ground-based synthetic aperture radar (GB-SAR) technique. To test the feasibility of detecting runway FODs with this technique, some preliminary experiments are conducted within short antenna-to-target ranges of small imaging patches. An automated stripmap GB-SAR system with stepped-frequency transmission is constructed together with a quasi-monostatic data collection operation. The imaging experiments for various braces and screws are then carried out by using 32–36?GHz and 90–95?GHz frequency bands of the millimeter-wave. Images reconstructed by a matched-filter based algorithm are analyzed to determine the proper system parameters for an efficient imaging and to comprehend the factors against a successful detection. Results demonstrate the capability of GB-SAR imaging in accurately locating these FOD-like targets under near-range operating conditions.     

Input Resistance Calculation for Circular Microstrip Antennas Using Adaptive Neuro-Fuzzy Inference System

This paper presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the input resistance of circular microstrip patch antennas. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of FIS with learning power of neural networks. A hybrid learning algorithm based on the least square approach and the backpropagation algorithm is used to optimize the parameters of ANFIS. The input resistance results predicted by ANFIS are in excellent agreement with the experimental results reported elsewhere.     

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR COMPUTING THE PHYSICAL DIMENSIONS OF ELECTRICALLY THIN AND THICK RECTANGULAR MICROSTRIP ANTENNAS

A new method for calculating the patch length and width of rectangular microstrip antennas (MSAs) with thin and thick substrates, based on adaptive neuro-fuzzy inference system (ANFIS), is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). It combines the powerful features of FISs with those of artificial neural networks (ANNs) to achieve a desired performance. The results of ANFIS are in excellent agreement with the experimental results available in the literature.