Seismic features of vibration induced by train

Chen, Qi‐Fu; Li, Li; Li, Gang; Chen, Ling; Peng, Wentao; Tang, Yi; Chen, Yong; Wang, Fuyun

doi:10.1007/s11589-004-0011-7

Cited by 33 publications

(22 citation statements)

References 12 publications

(12 reference statements)

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“…2(a). In the frequency range above 1 Hz traffic-induced seismic waves are excited by road traffic (Hao & Ang 1998;Coward et al 2003) and trains (Chen et al 2004;Fiala et al 2007) or can be generated by traffic induced bridge oscillations (Chen et al 2007). These contribute significantly to USN in a broad frequency range from ∼1 Hz to more than 45 Hz with maximum amplitudes between 1 and 10 Hz.…”

Section: Microtremor (>1 Hz)mentioning

confidence: 99%

Time domain classification and quantification of seismic noise in an urban environment

Groos¹,

Ritter²

2009

Geophysical Journal International

137

108

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S U M M A R YBroad-band urban seismic noise (USN) must be considered as a temporally and spatially non-stationary random process. Due to the high variability of USN a single measure like the standard deviation of a seismic noise time-series or the power spectral density at a given frequency is not enough to characterize a sample (time-series) of USN comprehensively. Therefore, we use long-term spectrograms and propose an automated statistical classification in the time domain to quantify and characterize USN. Long-term spectrograms of up to 28 d duration are calculated from a broad-band seismic data set recorded in the metropolitan area of Bucharest, Romania, to identify the frequency-dependent behaviour of the timevariable processes contributing to USN. Based on the spectral analysis eight frequency ranges between 8 mHz and 45 Hz are selected for our proposed time domain classification. The classification scheme identifies deviations from the Gaussian distribution of 4-hr-long timeseries of USN. Our classification is capable to identify Gaussian distributed seismic noise timeseries as well as time-series dominated by transient or periodic signals using six noise classes. Four additional noise classes are introduced to identify corrupt time-series. The performance of the method is tested with a synthetic data set. We also apply the statistical classification for the data set from Bucharest in three time windows (0-4, 8-12 and 13-17 EET) at 11 d in the eight frequency ranges. Only 40 per cent of the analysed time-series are observed to be Gaussian distributed. Most common deviations from the Gaussian distribution (∼47 per cent) are due to the influence of large-amplitude transient signals. In all frequency ranges between 0.04 and 45 Hz significant variations of the statistical properties of USN are observed with daytime, indicating the broad-band human influence on USN. We observe the human activity as a dominant influence on the USN above and below the frequency band of ocean-generated microseism between 0.04 and 0.6 Hz. Our time domain classification and quantification is furthermore capable to resolve the influence of wind on seismic noise and a known site effect variation in the metropolitan area of Bucharest. The information about noise amplitudes and statistical properties derived automatically from broad-band seismic data can be used to select time windows containing adequate data for seismic noise utilization like H/V-studies or ambient noise tomography.

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Section: Microtremor (>1 Hz)mentioning

confidence: 99%

Time domain classification and quantification of seismic noise in an urban environment

Groos¹,

Ritter²

2009

Geophysical Journal International

137

108

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“…This result is contrary to most research results, [2,3,6] but agrees with the results of ref. [1,4,5,21] The conductivity of PPY/Fe 3 O 4 composite depends on the doping level of PPY, compactness of the samples, PPY content, etc. The conductivity increases with the increase in the doping level of PPY, compactness of the samples, and PPY content.…”

Section: Electrical Propertiesmentioning

confidence: 99%

“…Deng et al [3] prepared the polypyrrole-Fe 3 O 4 composite with high saturation magnetization with the aid of sodium dodecylbenzene sulfonate, but its conductivity was very low (10 À3 S Á cm À1 ). Chen et al [4,5] first modified the surface of Fe 3 O 4 nanoparticles with ethanol or Fe 3þ , and polymerized pyrrole in the presence of Fe 3 O 4 and surfactant sodium dodecyl sulfate by the emulsion polymerization method, and then prepared polypyrrole-Fe 3 O 4 composite with electrical and magnetic properties. Suri et al [6] prepared polypyrrole and iron oxide nanocomposite by simultaneous gelation and polymerization.…”

Section: Introductionmentioning

confidence: 99%

Polypyrrole‐Fe₃O₄ Magnetic Nanocomposite Prepared by Ultrasonic Irradiation

Qiu

Wang

Nie

2005

Macro Materials & Eng

121

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Summary: Ultrasonic irradiation was employed to prepare polypyrrole (PPY)/Fe3O4 magnetic nanocomposite by chemical oxidative polymerization of pyrrole in the presence of Fe3O4 nanoparticles. This approach can solve the problem in the dispersion and stabilization of inorganic nanoparticles in polymer. The structure and properties of PPY/Fe3O4 nanocomposite were characterized by TEM, XPS, FT‐IR, TG, and XRD. PPY deposits on the surface of Fe3O4 nanoparticles while Fe3O4 nanoparticles are dispersed at the nanoscale by ultrasonic irradiation, which leads to the formation of polypyrrole‐encapsulated Fe3O4 composite particles. The doping level of PPY in PPY/Fe3O4 nanocomposite is higher than that of neat PPY. The composites possess good electrical and magnetic properties. With the increase in the Fe3O4 content, the magnetization increases and the conductivity first increases and then decreases. When the Fe3O4 content is 40 wt.‐%, the conductivity reaches a maximum value of 11.26 S · cm−1, about nine times higher than that of neat PPY, and the saturation magnetization is 23 emu · g−1. Also, the introduction of Fe3O4 nanoparticles enhances the thermal stability of PPY/Fe3O4 composite.Conductivity of PPY/Fe3O4 composite at different Fe3O4 content.magnified imageConductivity of PPY/Fe3O4 composite at different Fe3O4 content.

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“…Magnetic saturation of the composite has been reported to be low due to the presence of silica. Chen et al 26 used emulsion polymerization through polymerization of pyrrole in the presence of Fe 3 O 4 particles and sodium dodecyl sulfate as surfactant. Aphesteguy and Jacobo 27 prepared PANI-Fe 3 O 4 nanocomposite through polymerization of aniline in the absence of external oxidant.…”

Section: Introductionmentioning

confidence: 99%

Novel electrically conductive and ferromagnetic composites of poly(aniline‐co‐aminonaphthalenesulfonic acid) with iron oxide nanoparticles: Synthesis and characterization

Reddy

Lee

Gopalan

2007

J of Applied Polymer Sci

162

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Nanocomposites of iron oxide (Fe 3 O 4 ) with a sulfonated polyaniline, poly(aniline-co-aminonaphthalenesulfonic acid) [SPAN(ANSA)], were synthesized through chemical oxidative copolymerization of aniline and 5-amino-2-naphthalenesulfonic acid/1-amino-5-naphthalenesulfonic acid in the presence of Fe 3 O 4 nanoparticles. The nanocomposites [Fe 3 O 4 /SPAN(ANSA)-NCs] were characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, elemental analysis, UV-visible spectroscopy, thermogravimetric analysis (TGA), superconductor quantum interference device (SQUID), and electrical conductivity measurements. The TEM images reveal that nanocrystalline Fe 3 O 4 particles were homogeneously incorporated within the polymer matrix with the sizes in the range of 10-15 nm. XRD pattern reveals that pure Fe 3 O 4 particles are having spinel structure, and nanocomposites are more crystalline in comparison to pristine polymers. Differen-tial thermogravimetric (DTG) curves obtained through TGA informs that polymer chains in the composites have better thermal stability than that of the pristine copolymers. FTIR spectra provide information on the structure of the composites. The conductivity of the nanocomposites ($ 0.5 S cm 21 ) is higher than that of pristine PANI ($ 10 23 S cm 21 ). The charge transport behavior of the composites is explained through temperature difference of conductivity. The temperature dependence of conductivity fits with the quasi-1D variable range hopping (quasi-1D VRH) model. SQUID analysis reveals that the composites show ferromagnetic behavior at room temperature. The maximum saturation magnetization of the composite is 9.7 emu g 21 .

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Seismic features of vibration induced by train

Cited by 33 publications

References 12 publications

Time domain classification and quantification of seismic noise in an urban environment

Time domain classification and quantification of seismic noise in an urban environment

Polypyrrole‐Fe₃O₄ Magnetic Nanocomposite Prepared by Ultrasonic Irradiation

Novel electrically conductive and ferromagnetic composites of poly(aniline‐co‐aminonaphthalenesulfonic acid) with iron oxide nanoparticles: Synthesis and characterization

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Seismic features of vibration induced by train

Cited by 33 publications

References 12 publications

Time domain classification and quantification of seismic noise in an urban environment

Time domain classification and quantification of seismic noise in an urban environment

Polypyrrole‐Fe3O4 Magnetic Nanocomposite Prepared by Ultrasonic Irradiation

Novel electrically conductive and ferromagnetic composites of poly(aniline‐co‐aminonaphthalenesulfonic acid) with iron oxide nanoparticles: Synthesis and characterization

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Product

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Polypyrrole‐Fe₃O₄ Magnetic Nanocomposite Prepared by Ultrasonic Irradiation