Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka

Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Randima Nirmal Gunatilaka [verfasserIn] Fathima Nishara Abdeen [verfasserIn] Samad M. E. Sepasgozar [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	smart buildings commercial buildings automation IoT sensors analytical hierarchy process

Übergeordnetes Werk:	In: Buildings - MDPI AG, 2012, 11(2021), 12, p 644
Übergeordnetes Werk:	volume:11 ; year:2021 ; number:12, p 644

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/buildings11120644

Katalog-ID:	DOAJ018820638

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ018820638
003	DE-627
005	20240412095403.0
007	cr uuu---uuuuu
008	230226s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/buildings11120644 \|2 doi
035			\|a (DE-627)DOAJ018820638
035			\|a (DE-599)DOAJ21b32c45a6514d0780393ed77018311b
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TH1-9745
100	0		\|a Randima Nirmal Gunatilaka \|e verfasserin \|4 aut
245	1	0	\|a Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka
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 Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers.
650		4	\|a smart buildings
650		4	\|a commercial buildings
650		4	\|a automation
650		4	\|a IoT sensors
650		4	\|a analytical hierarchy process
653		0	\|a Building construction
700	0		\|a Fathima Nishara Abdeen \|e verfasserin \|4 aut
700	0		\|a Samad M. E. Sepasgozar \|e verfasserin \|4 aut
773	0	8	\|i In \|t Buildings \|d MDPI AG, 2012 \|g 11(2021), 12, p 644 \|w (DE-627)718622251 \|w (DE-600)2661539-3 \|x 20755309 \|7 nnns
773	1	8	\|g volume:11 \|g year:2021 \|g number:12, p 644
856	4	0	\|u https://doi.org/10.3390/buildings11120644 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/21b32c45a6514d0780393ed77018311b \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/2075-5309/11/12/644 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2075-5309 \|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_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_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_2014
912			\|a GBV_ILN_2055
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_4392
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 11 \|j 2021 \|e 12, p 644

Indexfelder

author_variant	r n g rng f n a fna s m e s smes
matchkey_str	article:20755309:2021----::eeoigsoigytmovlaehlvlfmrnsicmecab
hierarchy_sort_str	2021
callnumber-subject-code	TH
publishDate	2021
allfields	10.3390/buildings11120644 doi (DE-627)DOAJ018820638 (DE-599)DOAJ21b32c45a6514d0780393ed77018311b DE-627 ger DE-627 rakwb eng TH1-9745 Randima Nirmal Gunatilaka verfasserin aut Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers. smart buildings commercial buildings automation IoT sensors analytical hierarchy process Building construction Fathima Nishara Abdeen verfasserin aut Samad M. E. Sepasgozar verfasserin aut In Buildings MDPI AG, 2012 11(2021), 12, p 644 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:11 year:2021 number:12, p 644 https://doi.org/10.3390/buildings11120644 kostenfrei https://doaj.org/article/21b32c45a6514d0780393ed77018311b kostenfrei https://www.mdpi.com/2075-5309/11/12/644 kostenfrei https://doaj.org/toc/2075-5309 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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_4392 GBV_ILN_4700 AR 11 2021 12, p 644
spelling	10.3390/buildings11120644 doi (DE-627)DOAJ018820638 (DE-599)DOAJ21b32c45a6514d0780393ed77018311b DE-627 ger DE-627 rakwb eng TH1-9745 Randima Nirmal Gunatilaka verfasserin aut Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers. smart buildings commercial buildings automation IoT sensors analytical hierarchy process Building construction Fathima Nishara Abdeen verfasserin aut Samad M. E. Sepasgozar verfasserin aut In Buildings MDPI AG, 2012 11(2021), 12, p 644 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:11 year:2021 number:12, p 644 https://doi.org/10.3390/buildings11120644 kostenfrei https://doaj.org/article/21b32c45a6514d0780393ed77018311b kostenfrei https://www.mdpi.com/2075-5309/11/12/644 kostenfrei https://doaj.org/toc/2075-5309 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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_4392 GBV_ILN_4700 AR 11 2021 12, p 644
allfields_unstemmed	10.3390/buildings11120644 doi (DE-627)DOAJ018820638 (DE-599)DOAJ21b32c45a6514d0780393ed77018311b DE-627 ger DE-627 rakwb eng TH1-9745 Randima Nirmal Gunatilaka verfasserin aut Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers. smart buildings commercial buildings automation IoT sensors analytical hierarchy process Building construction Fathima Nishara Abdeen verfasserin aut Samad M. E. Sepasgozar verfasserin aut In Buildings MDPI AG, 2012 11(2021), 12, p 644 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:11 year:2021 number:12, p 644 https://doi.org/10.3390/buildings11120644 kostenfrei https://doaj.org/article/21b32c45a6514d0780393ed77018311b kostenfrei https://www.mdpi.com/2075-5309/11/12/644 kostenfrei https://doaj.org/toc/2075-5309 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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_4392 GBV_ILN_4700 AR 11 2021 12, p 644
allfieldsGer	10.3390/buildings11120644 doi (DE-627)DOAJ018820638 (DE-599)DOAJ21b32c45a6514d0780393ed77018311b DE-627 ger DE-627 rakwb eng TH1-9745 Randima Nirmal Gunatilaka verfasserin aut Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers. smart buildings commercial buildings automation IoT sensors analytical hierarchy process Building construction Fathima Nishara Abdeen verfasserin aut Samad M. E. Sepasgozar verfasserin aut In Buildings MDPI AG, 2012 11(2021), 12, p 644 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:11 year:2021 number:12, p 644 https://doi.org/10.3390/buildings11120644 kostenfrei https://doaj.org/article/21b32c45a6514d0780393ed77018311b kostenfrei https://www.mdpi.com/2075-5309/11/12/644 kostenfrei https://doaj.org/toc/2075-5309 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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_4392 GBV_ILN_4700 AR 11 2021 12, p 644
allfieldsSound	10.3390/buildings11120644 doi (DE-627)DOAJ018820638 (DE-599)DOAJ21b32c45a6514d0780393ed77018311b DE-627 ger DE-627 rakwb eng TH1-9745 Randima Nirmal Gunatilaka verfasserin aut Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers. smart buildings commercial buildings automation IoT sensors analytical hierarchy process Building construction Fathima Nishara Abdeen verfasserin aut Samad M. E. Sepasgozar verfasserin aut In Buildings MDPI AG, 2012 11(2021), 12, p 644 (DE-627)718622251 (DE-600)2661539-3 20755309 nnns volume:11 year:2021 number:12, p 644 https://doi.org/10.3390/buildings11120644 kostenfrei https://doaj.org/article/21b32c45a6514d0780393ed77018311b kostenfrei https://www.mdpi.com/2075-5309/11/12/644 kostenfrei https://doaj.org/toc/2075-5309 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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_4392 GBV_ILN_4700 AR 11 2021 12, p 644
language	English
source	In Buildings 11(2021), 12, p 644 volume:11 year:2021 number:12, p 644
sourceStr	In Buildings 11(2021), 12, p 644 volume:11 year:2021 number:12, p 644
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	smart buildings commercial buildings automation IoT sensors analytical hierarchy process Building construction
isfreeaccess_bool	true
container_title	Buildings
authorswithroles_txt_mv	Randima Nirmal Gunatilaka @@aut@@ Fathima Nishara Abdeen @@aut@@ Samad M. E. Sepasgozar @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	718622251
id	DOAJ018820638
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">DOAJ018820638</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412095403.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/buildings11120644</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ018820638</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ21b32c45a6514d0780393ed77018311b</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="050" ind1=" " ind2="0"><subfield code="a">TH1-9745</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Randima Nirmal Gunatilaka</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka</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">Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">smart buildings</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">commercial buildings</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">automation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IoT sensors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">analytical hierarchy process</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Building construction</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fathima Nishara Abdeen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Samad M. E. Sepasgozar</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">Buildings</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">11(2021), 12, p 644</subfield><subfield code="w">(DE-627)718622251</subfield><subfield code="w">(DE-600)2661539-3</subfield><subfield code="x">20755309</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:12, p 644</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/buildings11120644</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/21b32c45a6514d0780393ed77018311b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2075-5309/11/12/644</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2075-5309</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_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_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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</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_4392</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">11</subfield><subfield code="j">2021</subfield><subfield code="e">12, p 644</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Randima Nirmal Gunatilaka
spellingShingle	Randima Nirmal Gunatilaka misc TH1-9745 misc smart buildings misc commercial buildings misc automation misc IoT sensors misc analytical hierarchy process misc Building construction Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka
authorStr	Randima Nirmal Gunatilaka
ppnlink_with_tag_str_mv	@@773@@(DE-627)718622251
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TH1-9745
illustrated	Not Illustrated
issn	20755309
topic_title	TH1-9745 Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka smart buildings commercial buildings automation IoT sensors analytical hierarchy process
topic	misc TH1-9745 misc smart buildings misc commercial buildings misc automation misc IoT sensors misc analytical hierarchy process misc Building construction
topic_unstemmed	misc TH1-9745 misc smart buildings misc commercial buildings misc automation misc IoT sensors misc analytical hierarchy process misc Building construction
topic_browse	misc TH1-9745 misc smart buildings misc commercial buildings misc automation misc IoT sensors misc analytical hierarchy process misc Building construction
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Buildings
hierarchy_parent_id	718622251
hierarchy_top_title	Buildings
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)718622251 (DE-600)2661539-3
title	Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka
ctrlnum	(DE-627)DOAJ018820638 (DE-599)DOAJ21b32c45a6514d0780393ed77018311b
title_full	Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka
author_sort	Randima Nirmal Gunatilaka
journal	Buildings
journalStr	Buildings
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
author_browse	Randima Nirmal Gunatilaka Fathima Nishara Abdeen Samad M. E. Sepasgozar
container_volume	11
class	TH1-9745
format_se	Elektronische Aufsätze
author-letter	Randima Nirmal Gunatilaka
doi_str_mv	10.3390/buildings11120644
author2-role	verfasserin
title_sort	developing a scoring system to evaluate the level of smartness in commercial buildings: a case of sri lanka
callnumber	TH1-9745
title_auth	Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka
abstract	Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers.
abstractGer	Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers.
abstract_unstemmed	Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers.
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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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_4392 GBV_ILN_4700
container_issue	12, p 644
title_short	Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka
url	https://doi.org/10.3390/buildings11120644 https://doaj.org/article/21b32c45a6514d0780393ed77018311b https://www.mdpi.com/2075-5309/11/12/644 https://doaj.org/toc/2075-5309
remote_bool	true
author2	Fathima Nishara Abdeen Samad M. E. Sepasgozar
author2Str	Fathima Nishara Abdeen Samad M. E. Sepasgozar
ppnlink	718622251
callnumber-subject	TH - Building Construction
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/buildings11120644
callnumber-a	TH1-9745
up_date	2024-07-03T20:11:18.475Z
_version_	1803590019030450176
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">DOAJ018820638</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412095403.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/buildings11120644</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ018820638</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ21b32c45a6514d0780393ed77018311b</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="050" ind1=" " ind2="0"><subfield code="a">TH1-9745</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Randima Nirmal Gunatilaka</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka</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">Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">smart buildings</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">commercial buildings</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">automation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IoT sensors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">analytical hierarchy process</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Building construction</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fathima Nishara Abdeen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Samad M. E. Sepasgozar</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">Buildings</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">11(2021), 12, p 644</subfield><subfield code="w">(DE-627)718622251</subfield><subfield code="w">(DE-600)2661539-3</subfield><subfield code="x">20755309</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:12, p 644</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/buildings11120644</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/21b32c45a6514d0780393ed77018311b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2075-5309/11/12/644</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2075-5309</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_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_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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</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_4392</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">11</subfield><subfield code="j">2021</subfield><subfield code="e">12, p 644</subfield></datafield></record></collection>
score	7.4015427

Nicht das Richtige dabei?

Schreiben Sie uns!

Developing a Scoring System to Evaluate the Level of Smartness in Commercial Buildings: A Case of Sri Lanka

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?