Weathering assessment approach for building sandstone using hyperspectral imaging technique

Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presen...
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Autor*in:	Yang, Haiqing [verfasserIn] Ni, Jianghua Chen, Chiwei Chen, Ying

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Building sandstone Weathering assessment model Hyperspectral imaging Microscopic observation Machine learning

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Heritage Science - London : Chemistry Central, 2013, 11(2023), 1 vom: 07. Apr.
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:1 ; day:07 ; month:04

Links:	Volltext

DOI / URN:	10.1186/s40494-023-00914-7

Katalog-ID:	SPR049977040

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520			\|a Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone.
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allfields	10.1186/s40494-023-00914-7 doi (DE-627)SPR049977040 (SPR)s40494-023-00914-7-e DE-627 ger DE-627 rakwb eng Yang, Haiqing verfasserin (orcid)0000-0003-4497-044X aut Weathering assessment approach for building sandstone using hyperspectral imaging technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone. Building sandstone (dpeaa)DE-He213 Weathering assessment model (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Microscopic observation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ni, Jianghua (orcid)0000-0002-2809-0170 aut Chen, Chiwei (orcid)0000-0003-0427-1898 aut Chen, Ying aut Enthalten in Heritage Science London : Chemistry Central, 2013 11(2023), 1 vom: 07. Apr. (DE-627)74117118X (DE-600)2710672-X 2050-7445 nnns volume:11 year:2023 number:1 day:07 month:04 https://dx.doi.org/10.1186/s40494-023-00914-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2023 1 07 04
spelling	10.1186/s40494-023-00914-7 doi (DE-627)SPR049977040 (SPR)s40494-023-00914-7-e DE-627 ger DE-627 rakwb eng Yang, Haiqing verfasserin (orcid)0000-0003-4497-044X aut Weathering assessment approach for building sandstone using hyperspectral imaging technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone. Building sandstone (dpeaa)DE-He213 Weathering assessment model (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Microscopic observation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ni, Jianghua (orcid)0000-0002-2809-0170 aut Chen, Chiwei (orcid)0000-0003-0427-1898 aut Chen, Ying aut Enthalten in Heritage Science London : Chemistry Central, 2013 11(2023), 1 vom: 07. Apr. (DE-627)74117118X (DE-600)2710672-X 2050-7445 nnns volume:11 year:2023 number:1 day:07 month:04 https://dx.doi.org/10.1186/s40494-023-00914-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2023 1 07 04
allfields_unstemmed	10.1186/s40494-023-00914-7 doi (DE-627)SPR049977040 (SPR)s40494-023-00914-7-e DE-627 ger DE-627 rakwb eng Yang, Haiqing verfasserin (orcid)0000-0003-4497-044X aut Weathering assessment approach for building sandstone using hyperspectral imaging technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone. Building sandstone (dpeaa)DE-He213 Weathering assessment model (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Microscopic observation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ni, Jianghua (orcid)0000-0002-2809-0170 aut Chen, Chiwei (orcid)0000-0003-0427-1898 aut Chen, Ying aut Enthalten in Heritage Science London : Chemistry Central, 2013 11(2023), 1 vom: 07. Apr. (DE-627)74117118X (DE-600)2710672-X 2050-7445 nnns volume:11 year:2023 number:1 day:07 month:04 https://dx.doi.org/10.1186/s40494-023-00914-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2023 1 07 04
allfieldsGer	10.1186/s40494-023-00914-7 doi (DE-627)SPR049977040 (SPR)s40494-023-00914-7-e DE-627 ger DE-627 rakwb eng Yang, Haiqing verfasserin (orcid)0000-0003-4497-044X aut Weathering assessment approach for building sandstone using hyperspectral imaging technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone. Building sandstone (dpeaa)DE-He213 Weathering assessment model (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Microscopic observation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ni, Jianghua (orcid)0000-0002-2809-0170 aut Chen, Chiwei (orcid)0000-0003-0427-1898 aut Chen, Ying aut Enthalten in Heritage Science London : Chemistry Central, 2013 11(2023), 1 vom: 07. Apr. (DE-627)74117118X (DE-600)2710672-X 2050-7445 nnns volume:11 year:2023 number:1 day:07 month:04 https://dx.doi.org/10.1186/s40494-023-00914-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2023 1 07 04
allfieldsSound	10.1186/s40494-023-00914-7 doi (DE-627)SPR049977040 (SPR)s40494-023-00914-7-e DE-627 ger DE-627 rakwb eng Yang, Haiqing verfasserin (orcid)0000-0003-4497-044X aut Weathering assessment approach for building sandstone using hyperspectral imaging technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone. Building sandstone (dpeaa)DE-He213 Weathering assessment model (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Microscopic observation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ni, Jianghua (orcid)0000-0002-2809-0170 aut Chen, Chiwei (orcid)0000-0003-0427-1898 aut Chen, Ying aut Enthalten in Heritage Science London : Chemistry Central, 2013 11(2023), 1 vom: 07. Apr. (DE-627)74117118X (DE-600)2710672-X 2050-7445 nnns volume:11 year:2023 number:1 day:07 month:04 https://dx.doi.org/10.1186/s40494-023-00914-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2023 1 07 04
language	English
source	Enthalten in Heritage Science 11(2023), 1 vom: 07. Apr. volume:11 year:2023 number:1 day:07 month:04
sourceStr	Enthalten in Heritage Science 11(2023), 1 vom: 07. Apr. volume:11 year:2023 number:1 day:07 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Building sandstone Weathering assessment model Hyperspectral imaging Microscopic observation Machine learning
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container_title	Heritage Science
authorswithroles_txt_mv	Yang, Haiqing @@aut@@ Ni, Jianghua @@aut@@ Chen, Chiwei @@aut@@ Chen, Ying @@aut@@
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author	Yang, Haiqing
spellingShingle	Yang, Haiqing misc Building sandstone misc Weathering assessment model misc Hyperspectral imaging misc Microscopic observation misc Machine learning Weathering assessment approach for building sandstone using hyperspectral imaging technique
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topic_title	Weathering assessment approach for building sandstone using hyperspectral imaging technique Building sandstone (dpeaa)DE-He213 Weathering assessment model (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Microscopic observation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213
topic	misc Building sandstone misc Weathering assessment model misc Hyperspectral imaging misc Microscopic observation misc Machine learning
topic_unstemmed	misc Building sandstone misc Weathering assessment model misc Hyperspectral imaging misc Microscopic observation misc Machine learning
topic_browse	misc Building sandstone misc Weathering assessment model misc Hyperspectral imaging misc Microscopic observation misc Machine learning
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title	Weathering assessment approach for building sandstone using hyperspectral imaging technique
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title_full	Weathering assessment approach for building sandstone using hyperspectral imaging technique
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author_browse	Yang, Haiqing Ni, Jianghua Chen, Chiwei Chen, Ying
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title_sort	weathering assessment approach for building sandstone using hyperspectral imaging technique
title_auth	Weathering assessment approach for building sandstone using hyperspectral imaging technique
abstract	Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone. © The Author(s) 2023
abstractGer	Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone. © The Author(s) 2023
abstract_unstemmed	Abstract Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone. © The Author(s) 2023
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700
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title_short	Weathering assessment approach for building sandstone using hyperspectral imaging technique
url	https://dx.doi.org/10.1186/s40494-023-00914-7
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author2	Ni, Jianghua Chen, Chiwei Chen, Ying
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up_date	2024-07-04T02:59:21.205Z
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Weathering assessment approach for building sandstone using hyperspectral imaging technique

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