Quantitative classification of near-fault ground motions selected by energy indicators

Structures ◽  
2022 ◽  
Vol 35 ◽  
pp. 780-791
Author(s):  
Dahai Zhao ◽  
Huiwei Wang ◽  
Ding Wang ◽  
Ruiguang Zhu ◽  
Jinghui Zhang
Keyword(s):  
Ground Motions ◽  
Quantitative Classification ◽  
Near Fault ◽  
Energy Indicators

Effects of Fling Step and Forward Directivity on Seismic Response of Buildings

2006 ◽  
Vol 22 (2) ◽  
pp. 367-390 ◽  
Author(s):  
Erol Kalkan ◽  
Sashi K. Kunnath
Keyword(s):  
Seismic Response ◽  
Ground Motions ◽  
High Amplitude ◽  
Moment Frames ◽  
Steel Moment Frames ◽  
Damage Potential ◽  
Near Fault ◽  
Forward Directivity ◽  
Fling Step ◽  
Far Fault

This paper investigates the consequences of well-known characteristics of near-fault ground motions on the seismic response of steel moment frames. Additionally, idealized pulses are utilized in a separate study to gain further insight into the effects of high-amplitude pulses on structural demands. Simple input pulses were also synthesized to simulate artificial fling-step effects in ground motions originally having forward directivity. Findings from the study reveal that median maximum demands and the dispersion in the peak values were higher for near-fault records than far-fault motions. The arrival of the velocity pulse in a near-fault record causes the structure to dissipate considerable input energy in relatively few plastic cycles, whereas cumulative effects from increased cyclic demands are more pronounced in far-fault records. For pulse-type input, the maximum demand is a function of the ratio of the pulse period to the fundamental period of the structure. Records with fling effects were found to excite systems primarily in their fundamental mode while waveforms with forward directivity in the absence of fling caused higher modes to be activated. It is concluded that the acceleration and velocity spectra, when examined collectively, can be utilized to reasonably assess the damage potential of near-fault records.

scholarly journals Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gabriel A. Colozza-Gama ◽  
Fabiano Callegari ◽  
Nikola Bešič ◽  
Ana C. de J. Paviza ◽  
Janete M. Cerutti
Keyword(s):  
Machine Learning ◽  
Sanger Sequencing ◽  
Learning Algorithm ◽  
Absolute Quantification ◽  
Braf V600e Mutation ◽  
Braf V600e ◽  
Driver Genes ◽  
Quantitative Classification ◽  
Cancer Driver

AbstractSomatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

A simplified iterative approach for testing the pulse derailment of light rail vehicles across a viaduct to near-fault earthquake scenarios

2021 ◽  
pp. 095440972098741
Author(s):  
Ling-Kun Chen ◽  
Peng Liu ◽  
Li-Ming Zhu ◽  
Jing-Bo Ding ◽  
Yu-Lin Feng ◽  
...  
Keyword(s):  
Ground Motions ◽  
Simulation Software ◽  
Light Rail ◽  
Rail Transit ◽  
Light Rail Transit ◽  
Seismic Responses ◽  
Near Fault ◽  
Rail Vehicle ◽  
Pulse Type ◽  
The Impact

Near-fault (NF) earthquakes cause severe bridge damage, particularly urban bridges subjected to light rail transit (LRT), which could affect the safety of the light rail transit vehicle (“light rail vehicle” or “LRV” for short). Now when a variety of studies on the fault fracture effect on the working protection of LRVs are available for the study of cars subjected to far-reaching soil motion (FFGMs), further examination is appropriate. For the first time, this paper introduced the LRV derailment mechanism caused by pulse-type near-fault ground motions (NFGMs), suggesting the concept of pulse derailment. The effects of near-fault ground motions (NFGMs) are included in an available numerical process developed for the LRV analysis of the VBI system. A simplified iterative algorithm is proposed to assess the stability and nonlinear seismic response of an LRV-reinforced concrete (RC) viaduct (LRVBRCV) system to a long-period NFGMs using the dynamic substructure method (DSM). Furthermore, a computer simulation software was developed to compute the nonlinear seismic responses of the VBI system to pulse-type NFGMs, non-pulse-type NFGMs, and FFGMs named Dynamic Interaction Analysis for Light-Rail-Vehicle Bridge System (DIALRVBS). The nonlinear bridge seismic reaction determines the impact of pulses on lateral peak earth acceleration (Ap) and lateral peak land (Vp) ratios. The analysis results quantify the effects of pulse-type NFGMs seismic responses on the LRV operations' safety. In contrast with the pulse-type non-pulse NFGMs and FFGMs, this article's research shows that pulse-type NFGM derail trains primarily via the transverse velocity pulse effect. Hence, this study's results and the proposed method can improve the LRT bridges' seismic designs.

Ductility Demand Spectra of the Self-Centering Structure Subjected to Near-Fault Pulse-like Ground Motions

2021 ◽  
pp. 1-19
Author(s):  
Huihui Dong ◽  
Qiang Han ◽  
Kaiming Bi ◽  
Chao Zhang ◽  
Xiuli Du
Keyword(s):  
Ground Motions ◽  
The Self ◽  
Ductility Demand ◽  
Near Fault

Robustness evaluation of fiv3 using near-fault pulse-like ground motions

2021 ◽  
Vol 230 ◽  
pp. 111694
Author(s):  
Héctor Dávalos ◽  
Eduardo Miranda
Keyword(s):  
Ground Motions ◽  
Near Fault
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