Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident
This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organ...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ghasemi, Fakhradin [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods - Cheigh, Chan-Ick ELSEVIER, 2013transfer abstract, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:78 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jlp.2022.104823 |
|---|
| Katalog-ID: |
ELV058325433 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV058325433 | ||
| 003 | DE-627 | ||
| 005 | 20230626050642.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220808s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jlp.2022.104823 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001829.pica |
| 035 | |a (DE-627)ELV058325433 | ||
| 035 | |a (ELSEVIER)S0950-4230(22)00099-7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 630 |q VZ |
| 082 | 0 | 4 | |a 640 |q VZ |
| 082 | 0 | 4 | |a 540 |q VZ |
| 082 | 0 | 4 | |a 660 |q VZ |
| 082 | 0 | 4 | |a 340 |a 330 |q VZ |
| 084 | |a 2 |2 ssgn | ||
| 084 | |a INTRECHT |q DE-1a |2 fid | ||
| 084 | |a 83.00 |2 bkl | ||
| 100 | 1 | |a Ghasemi, Fakhradin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident |
| 264 | 1 | |c 2022transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. | ||
| 520 | |a This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. | ||
| 650 | 7 | |a Fuzzy sets |2 Elsevier | |
| 650 | 7 | |a Human factors |2 Elsevier | |
| 650 | 7 | |a Accident prevention |2 Elsevier | |
| 650 | 7 | |a Process safety |2 Elsevier | |
| 650 | 7 | |a Bayesian network |2 Elsevier | |
| 700 | 1 | |a Gholamizadeh, Kamran |4 oth | |
| 700 | 1 | |a Farjadnia, Amirhasan |4 oth | |
| 700 | 1 | |a Sedighizadeh, Alireza |4 oth | |
| 700 | 1 | |a Kalatpour, Omid |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Cheigh, Chan-Ick ELSEVIER |t Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods |d 2013transfer abstract |g Amsterdam [u.a.] |w (DE-627)ELV017029821 |
| 773 | 1 | 8 | |g volume:78 |g year:2022 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.jlp.2022.104823 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a FID-INTRECHT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2008 | ||
| 912 | |a GBV_ILN_2018 | ||
| 936 | b | k | |a 83.00 |j Volkswirtschaft: Allgemeines |q VZ |
| 951 | |a AR | ||
| 952 | |d 78 |j 2022 |h 0 | ||
| author_variant |
f g fg |
|---|---|
| matchkey_str |
ghasemifakhradingholamizadehkamranfarjad:2022----:uaadraiainlalrsnlssnrcsidsreuigbhasoelan |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
83.00 |
| publishDate |
2022 |
| allfields |
10.1016/j.jlp.2022.104823 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001829.pica (DE-627)ELV058325433 (ELSEVIER)S0950-4230(22)00099-7 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 540 VZ 660 VZ 340 330 VZ 2 ssgn INTRECHT DE-1a fid 83.00 bkl Ghasemi, Fakhradin verfasserin aut Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network Elsevier Gholamizadeh, Kamran oth Farjadnia, Amirhasan oth Sedighizadeh, Alireza oth Kalatpour, Omid oth Enthalten in Elsevier Science Cheigh, Chan-Ick ELSEVIER Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV017029821 volume:78 year:2022 pages:0 https://doi.org/10.1016/j.jlp.2022.104823 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT GBV_ILN_70 GBV_ILN_2008 GBV_ILN_2018 83.00 Volkswirtschaft: Allgemeines VZ AR 78 2022 0 |
| spelling |
10.1016/j.jlp.2022.104823 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001829.pica (DE-627)ELV058325433 (ELSEVIER)S0950-4230(22)00099-7 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 540 VZ 660 VZ 340 330 VZ 2 ssgn INTRECHT DE-1a fid 83.00 bkl Ghasemi, Fakhradin verfasserin aut Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network Elsevier Gholamizadeh, Kamran oth Farjadnia, Amirhasan oth Sedighizadeh, Alireza oth Kalatpour, Omid oth Enthalten in Elsevier Science Cheigh, Chan-Ick ELSEVIER Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV017029821 volume:78 year:2022 pages:0 https://doi.org/10.1016/j.jlp.2022.104823 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT GBV_ILN_70 GBV_ILN_2008 GBV_ILN_2018 83.00 Volkswirtschaft: Allgemeines VZ AR 78 2022 0 |
| allfields_unstemmed |
10.1016/j.jlp.2022.104823 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001829.pica (DE-627)ELV058325433 (ELSEVIER)S0950-4230(22)00099-7 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 540 VZ 660 VZ 340 330 VZ 2 ssgn INTRECHT DE-1a fid 83.00 bkl Ghasemi, Fakhradin verfasserin aut Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network Elsevier Gholamizadeh, Kamran oth Farjadnia, Amirhasan oth Sedighizadeh, Alireza oth Kalatpour, Omid oth Enthalten in Elsevier Science Cheigh, Chan-Ick ELSEVIER Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV017029821 volume:78 year:2022 pages:0 https://doi.org/10.1016/j.jlp.2022.104823 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT GBV_ILN_70 GBV_ILN_2008 GBV_ILN_2018 83.00 Volkswirtschaft: Allgemeines VZ AR 78 2022 0 |
| allfieldsGer |
10.1016/j.jlp.2022.104823 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001829.pica (DE-627)ELV058325433 (ELSEVIER)S0950-4230(22)00099-7 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 540 VZ 660 VZ 340 330 VZ 2 ssgn INTRECHT DE-1a fid 83.00 bkl Ghasemi, Fakhradin verfasserin aut Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network Elsevier Gholamizadeh, Kamran oth Farjadnia, Amirhasan oth Sedighizadeh, Alireza oth Kalatpour, Omid oth Enthalten in Elsevier Science Cheigh, Chan-Ick ELSEVIER Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV017029821 volume:78 year:2022 pages:0 https://doi.org/10.1016/j.jlp.2022.104823 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT GBV_ILN_70 GBV_ILN_2008 GBV_ILN_2018 83.00 Volkswirtschaft: Allgemeines VZ AR 78 2022 0 |
| allfieldsSound |
10.1016/j.jlp.2022.104823 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001829.pica (DE-627)ELV058325433 (ELSEVIER)S0950-4230(22)00099-7 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 540 VZ 660 VZ 340 330 VZ 2 ssgn INTRECHT DE-1a fid 83.00 bkl Ghasemi, Fakhradin verfasserin aut Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network Elsevier Gholamizadeh, Kamran oth Farjadnia, Amirhasan oth Sedighizadeh, Alireza oth Kalatpour, Omid oth Enthalten in Elsevier Science Cheigh, Chan-Ick ELSEVIER Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV017029821 volume:78 year:2022 pages:0 https://doi.org/10.1016/j.jlp.2022.104823 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT GBV_ILN_70 GBV_ILN_2008 GBV_ILN_2018 83.00 Volkswirtschaft: Allgemeines VZ AR 78 2022 0 |
| language |
English |
| source |
Enthalten in Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods Amsterdam [u.a.] volume:78 year:2022 pages:0 |
| sourceStr |
Enthalten in Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods Amsterdam [u.a.] volume:78 year:2022 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Volkswirtschaft: Allgemeines |
| institution |
findex.gbv.de |
| topic_facet |
Fuzzy sets Human factors Accident prevention Process safety Bayesian network |
| dewey-raw |
630 |
| isfreeaccess_bool |
false |
| container_title |
Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods |
| authorswithroles_txt_mv |
Ghasemi, Fakhradin @@aut@@ Gholamizadeh, Kamran @@oth@@ Farjadnia, Amirhasan @@oth@@ Sedighizadeh, Alireza @@oth@@ Kalatpour, Omid @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV017029821 |
| dewey-sort |
3630 |
| id |
ELV058325433 |
| 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">ELV058325433</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626050642.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jlp.2022.104823</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001829.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058325433</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0950-4230(22)00099-7</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="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">640</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">340</subfield><subfield code="a">330</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">INTRECHT</subfield><subfield code="q">DE-1a</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">83.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ghasemi, Fakhradin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fuzzy sets</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Human factors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Accident prevention</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Process safety</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Bayesian network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gholamizadeh, Kamran</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Farjadnia, Amirhasan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sedighizadeh, Alireza</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kalatpour, Omid</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Cheigh, Chan-Ick ELSEVIER</subfield><subfield code="t">Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods</subfield><subfield code="d">2013transfer abstract</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV017029821</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:78</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jlp.2022.104823</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-INTRECHT</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_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">83.00</subfield><subfield code="j">Volkswirtschaft: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">78</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Ghasemi, Fakhradin |
| spellingShingle |
Ghasemi, Fakhradin ddc 630 ddc 640 ddc 540 ddc 660 ddc 340 ssgn 2 fid INTRECHT bkl 83.00 Elsevier Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident |
| authorStr |
Ghasemi, Fakhradin |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV017029821 |
| format |
electronic Article |
| dewey-ones |
630 - Agriculture & related technologies 640 - Home & family management 540 - Chemistry & allied sciences 660 - Chemical engineering 340 - Law 330 - Economics |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
630 VZ 640 VZ 540 VZ 660 VZ 340 330 VZ 2 ssgn INTRECHT DE-1a fid 83.00 bkl Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network Elsevier |
| topic |
ddc 630 ddc 640 ddc 540 ddc 660 ddc 340 ssgn 2 fid INTRECHT bkl 83.00 Elsevier Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network |
| topic_unstemmed |
ddc 630 ddc 640 ddc 540 ddc 660 ddc 340 ssgn 2 fid INTRECHT bkl 83.00 Elsevier Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network |
| topic_browse |
ddc 630 ddc 640 ddc 540 ddc 660 ddc 340 ssgn 2 fid INTRECHT bkl 83.00 Elsevier Fuzzy sets Elsevier Human factors Elsevier Accident prevention Elsevier Process safety Elsevier Bayesian network |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
k g kg a f af a s as o k ok |
| hierarchy_parent_title |
Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods |
| hierarchy_parent_id |
ELV017029821 |
| dewey-tens |
630 - Agriculture 640 - Home & family management 540 - Chemistry 660 - Chemical engineering 340 - Law 330 - Economics |
| hierarchy_top_title |
Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV017029821 |
| title |
Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident |
| ctrlnum |
(DE-627)ELV058325433 (ELSEVIER)S0950-4230(22)00099-7 |
| title_full |
Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident |
| author_sort |
Ghasemi, Fakhradin |
| journal |
Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods |
| journalStr |
Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 500 - Science 300 - Social sciences |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Ghasemi, Fakhradin |
| container_volume |
78 |
| class |
630 VZ 640 VZ 540 VZ 660 VZ 340 330 VZ 2 ssgn INTRECHT DE-1a fid 83.00 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Ghasemi, Fakhradin |
| doi_str_mv |
10.1016/j.jlp.2022.104823 |
| dewey-full |
630 640 540 660 340 330 |
| title_sort |
human and organizational failures analysis in process industries using fbn-hfacs model: learning from a toxic gas leakage accident |
| title_auth |
Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident |
| abstract |
This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. |
| abstractGer |
This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. |
| abstract_unstemmed |
This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT GBV_ILN_70 GBV_ILN_2008 GBV_ILN_2018 |
| title_short |
Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident |
| url |
https://doi.org/10.1016/j.jlp.2022.104823 |
| remote_bool |
true |
| author2 |
Gholamizadeh, Kamran Farjadnia, Amirhasan Sedighizadeh, Alireza Kalatpour, Omid |
| author2Str |
Gholamizadeh, Kamran Farjadnia, Amirhasan Sedighizadeh, Alireza Kalatpour, Omid |
| ppnlink |
ELV017029821 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth |
| doi_str |
10.1016/j.jlp.2022.104823 |
| up_date |
2024-07-06T18:41:32.519Z |
| _version_ |
1803856162346500096 |
| 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">ELV058325433</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626050642.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jlp.2022.104823</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001829.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058325433</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0950-4230(22)00099-7</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="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">640</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">340</subfield><subfield code="a">330</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">INTRECHT</subfield><subfield code="q">DE-1a</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">83.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ghasemi, Fakhradin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fuzzy sets</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Human factors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Accident prevention</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Process safety</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Bayesian network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gholamizadeh, Kamran</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Farjadnia, Amirhasan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sedighizadeh, Alireza</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kalatpour, Omid</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Cheigh, Chan-Ick ELSEVIER</subfield><subfield code="t">Intense pulsed light (IPL) and UV-C treatments for inactivating Listeria monocytogenes on solid medium and seafoods</subfield><subfield code="d">2013transfer abstract</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV017029821</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:78</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jlp.2022.104823</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-INTRECHT</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_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">83.00</subfield><subfield code="j">Volkswirtschaft: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">78</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.4015627 |