BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework

The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration p...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yaqin Yang [verfasserIn] Peng Xu [verfasserIn] Guotao Yang [verfasserIn] Long Chen [verfasserIn] Junbo Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming

Übergeordnetes Werk:	In: Frontiers in Materials - Frontiers Media S.A., 2014, 8(2021)
Übergeordnetes Werk:	volume:8 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fmats.2021.620484

Katalog-ID:	DOAJ058660380

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ058660380
003	DE-627
005	20230308225622.0
007	cr uuu---uuuuu
008	230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3389/fmats.2021.620484 \|2 doi
035			\|a (DE-627)DOAJ058660380
035			\|a (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Yaqin Yang \|e verfasserin \|4 aut
245	1	0	\|a BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections.
650		4	\|a maintenance activities identification
650		4	\|a Bayesian information criterion
650		4	\|a track irregularity
650		4	\|a adaptive thresholding
650		4	\|a dynamic programming
653		0	\|a Technology
653		0	\|a T
700	0		\|a Peng Xu \|e verfasserin \|4 aut
700	0		\|a Guotao Yang \|e verfasserin \|4 aut
700	0		\|a Long Chen \|e verfasserin \|4 aut
700	0		\|a Junbo Li \|e verfasserin \|4 aut
773	0	8	\|i In \|t Frontiers in Materials \|d Frontiers Media S.A., 2014 \|g 8(2021) \|w (DE-627)779920716 \|w (DE-600)2759394-0 \|x 22968016 \|7 nnns
773	1	8	\|g volume:8 \|g year:2021
856	4	0	\|u https://doi.org/10.3389/fmats.2021.620484 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f \|z kostenfrei
856	4	0	\|u https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2296-8016 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 8 \|j 2021

Indexfelder

author_variant	y y yy p x px g y gy l c lc j l jl
matchkey_str	article:22968016:2021----::ibsdaarvnalrcdtroaindpieicw
hierarchy_sort_str	2021
publishDate	2021
allfields	10.3389/fmats.2021.620484 doi (DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f DE-627 ger DE-627 rakwb eng Yaqin Yang verfasserin aut BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T Peng Xu verfasserin aut Guotao Yang verfasserin aut Long Chen verfasserin aut Junbo Li verfasserin aut In Frontiers in Materials Frontiers Media S.A., 2014 8(2021) (DE-627)779920716 (DE-600)2759394-0 22968016 nnns volume:8 year:2021 https://doi.org/10.3389/fmats.2021.620484 kostenfrei https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f kostenfrei https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full kostenfrei https://doaj.org/toc/2296-8016 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021
spelling	10.3389/fmats.2021.620484 doi (DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f DE-627 ger DE-627 rakwb eng Yaqin Yang verfasserin aut BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T Peng Xu verfasserin aut Guotao Yang verfasserin aut Long Chen verfasserin aut Junbo Li verfasserin aut In Frontiers in Materials Frontiers Media S.A., 2014 8(2021) (DE-627)779920716 (DE-600)2759394-0 22968016 nnns volume:8 year:2021 https://doi.org/10.3389/fmats.2021.620484 kostenfrei https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f kostenfrei https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full kostenfrei https://doaj.org/toc/2296-8016 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021
allfields_unstemmed	10.3389/fmats.2021.620484 doi (DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f DE-627 ger DE-627 rakwb eng Yaqin Yang verfasserin aut BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T Peng Xu verfasserin aut Guotao Yang verfasserin aut Long Chen verfasserin aut Junbo Li verfasserin aut In Frontiers in Materials Frontiers Media S.A., 2014 8(2021) (DE-627)779920716 (DE-600)2759394-0 22968016 nnns volume:8 year:2021 https://doi.org/10.3389/fmats.2021.620484 kostenfrei https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f kostenfrei https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full kostenfrei https://doaj.org/toc/2296-8016 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021
allfieldsGer	10.3389/fmats.2021.620484 doi (DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f DE-627 ger DE-627 rakwb eng Yaqin Yang verfasserin aut BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T Peng Xu verfasserin aut Guotao Yang verfasserin aut Long Chen verfasserin aut Junbo Li verfasserin aut In Frontiers in Materials Frontiers Media S.A., 2014 8(2021) (DE-627)779920716 (DE-600)2759394-0 22968016 nnns volume:8 year:2021 https://doi.org/10.3389/fmats.2021.620484 kostenfrei https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f kostenfrei https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full kostenfrei https://doaj.org/toc/2296-8016 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021
allfieldsSound	10.3389/fmats.2021.620484 doi (DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f DE-627 ger DE-627 rakwb eng Yaqin Yang verfasserin aut BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T Peng Xu verfasserin aut Guotao Yang verfasserin aut Long Chen verfasserin aut Junbo Li verfasserin aut In Frontiers in Materials Frontiers Media S.A., 2014 8(2021) (DE-627)779920716 (DE-600)2759394-0 22968016 nnns volume:8 year:2021 https://doi.org/10.3389/fmats.2021.620484 kostenfrei https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f kostenfrei https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full kostenfrei https://doaj.org/toc/2296-8016 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021
language	English
source	In Frontiers in Materials 8(2021) volume:8 year:2021
sourceStr	In Frontiers in Materials 8(2021) volume:8 year:2021
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T
isfreeaccess_bool	true
container_title	Frontiers in Materials
authorswithroles_txt_mv	Yaqin Yang @@aut@@ Peng Xu @@aut@@ Guotao Yang @@aut@@ Long Chen @@aut@@ Junbo Li @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	779920716
id	DOAJ058660380
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ058660380</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308225622.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fmats.2021.620484</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ058660380</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yaqin Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">maintenance activities identification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian information criterion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">track irregularity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adaptive thresholding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">dynamic programming</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Peng Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Guotao Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Long Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Junbo Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Materials</subfield><subfield code="d">Frontiers Media S.A., 2014</subfield><subfield code="g">8(2021)</subfield><subfield code="w">(DE-627)779920716</subfield><subfield code="w">(DE-600)2759394-0</subfield><subfield code="x">22968016</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2021</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fmats.2021.620484</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2296-8016</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2021</subfield></datafield></record></collection>
author	Yaqin Yang
spellingShingle	Yaqin Yang misc maintenance activities identification misc Bayesian information criterion misc track irregularity misc adaptive thresholding misc dynamic programming misc Technology misc T BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework
authorStr	Yaqin Yang
ppnlink_with_tag_str_mv	@@773@@(DE-627)779920716
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	22968016
topic_title	BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming
topic	misc maintenance activities identification misc Bayesian information criterion misc track irregularity misc adaptive thresholding misc dynamic programming misc Technology misc T
topic_unstemmed	misc maintenance activities identification misc Bayesian information criterion misc track irregularity misc adaptive thresholding misc dynamic programming misc Technology misc T
topic_browse	misc maintenance activities identification misc Bayesian information criterion misc track irregularity misc adaptive thresholding misc dynamic programming misc Technology misc T
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Frontiers in Materials
hierarchy_parent_id	779920716
hierarchy_top_title	Frontiers in Materials
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)779920716 (DE-600)2759394-0
title	BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework
ctrlnum	(DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f
title_full	BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework
author_sort	Yaqin Yang
journal	Frontiers in Materials
journalStr	Frontiers in Materials
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
author_browse	Yaqin Yang Peng Xu Guotao Yang Long Chen Junbo Li
container_volume	8
format_se	Elektronische Aufsätze
author-letter	Yaqin Yang
doi_str_mv	10.3389/fmats.2021.620484
author2-role	verfasserin
title_sort	bic-based data-driven rail track deterioration adaptive piecewise modeling framework
title_auth	BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework
abstract	The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections.
abstractGer	The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections.
abstract_unstemmed	The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework
url	https://doi.org/10.3389/fmats.2021.620484 https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full https://doaj.org/toc/2296-8016
remote_bool	true
author2	Peng Xu Guotao Yang Long Chen Junbo Li
author2Str	Peng Xu Guotao Yang Long Chen Junbo Li
ppnlink	779920716
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3389/fmats.2021.620484
up_date	2024-07-03T19:20:47.378Z
_version_	1803586840696979456
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ058660380</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308225622.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fmats.2021.620484</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ058660380</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yaqin Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">maintenance activities identification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian information criterion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">track irregularity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adaptive thresholding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">dynamic programming</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Peng Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Guotao Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Long Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Junbo Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Materials</subfield><subfield code="d">Frontiers Media S.A., 2014</subfield><subfield code="g">8(2021)</subfield><subfield code="w">(DE-627)779920716</subfield><subfield code="w">(DE-600)2759394-0</subfield><subfield code="x">22968016</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2021</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fmats.2021.620484</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2296-8016</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2021</subfield></datafield></record></collection>
score	7.400199

Nicht das Richtige dabei?

Schreiben Sie uns!

BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?