Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN Model
Most construction projects encounter safety issues that may affect project effectiveness and the lives of workers. Although various studies have investigated these factors, in some countries, such as Rwanda, there is still little empirical evidence regarding the important aspects that contribute to...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/2/237 |
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| author | Esperance Umuhoza Sung-Hoon An |
| author_facet | Esperance Umuhoza Sung-Hoon An |
| author_sort | Esperance Umuhoza |
| collection | DOAJ |
| description | Most construction projects encounter safety issues that may affect project effectiveness and the lives of workers. Although various studies have investigated these factors, in some countries, such as Rwanda, there is still little empirical evidence regarding the important aspects that contribute to safety effectiveness. Therefore, this study was carried out to predict the resilient safety effectiveness in the Rwandan building construction sector via the artificial neural network (ANN) model. Through a literature review, resilient safety variables that may be relevant in the Rwandan construction sector were identified. Data were collected through questionnaires. Moreover, the levels of importance of resilient-safety-effectiveness-related factors were pinpointed and assessed using the analytical hierarchy process (AHP). Consecutively, an ANN model that could predict the effectiveness of resilient safety was developed. This study contributes to the awareness of key factors that may affect the effectiveness of resilient safety, and it helps to forecast the effectiveness of resilient safety not only in Rwanda, but also in other low- and middle-income countries with different conditions by stressing the importance of reducing safety-related risks in building construction projects. |
| format | Article |
| id | doaj-art-91150951c4f3490a9e1f457d6511bb86 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-91150951c4f3490a9e1f457d6511bb862025-01-24T13:26:17ZengMDPI AGBuildings2075-53092025-01-0115223710.3390/buildings15020237Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN ModelEsperance Umuhoza0Sung-Hoon An1Department of Architecture Engineering, Daegu University, Gyeongsan 38453, Republic of KoreaDepartment of Architecture Engineering, Daegu University, Gyeongsan 38453, Republic of KoreaMost construction projects encounter safety issues that may affect project effectiveness and the lives of workers. Although various studies have investigated these factors, in some countries, such as Rwanda, there is still little empirical evidence regarding the important aspects that contribute to safety effectiveness. Therefore, this study was carried out to predict the resilient safety effectiveness in the Rwandan building construction sector via the artificial neural network (ANN) model. Through a literature review, resilient safety variables that may be relevant in the Rwandan construction sector were identified. Data were collected through questionnaires. Moreover, the levels of importance of resilient-safety-effectiveness-related factors were pinpointed and assessed using the analytical hierarchy process (AHP). Consecutively, an ANN model that could predict the effectiveness of resilient safety was developed. This study contributes to the awareness of key factors that may affect the effectiveness of resilient safety, and it helps to forecast the effectiveness of resilient safety not only in Rwanda, but also in other low- and middle-income countries with different conditions by stressing the importance of reducing safety-related risks in building construction projects.https://www.mdpi.com/2075-5309/15/2/237safety effectivenessresilient safety cultureartificial neural networkRwanda |
| spellingShingle | Esperance Umuhoza Sung-Hoon An Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN Model Buildings safety effectiveness resilient safety culture artificial neural network Rwanda |
| title | Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN Model |
| title_full | Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN Model |
| title_fullStr | Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN Model |
| title_full_unstemmed | Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN Model |
| title_short | Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN Model |
| title_sort | predicting the effectiveness of resilient safety in the building construction sector of rwanda using the ann model |
| topic | safety effectiveness resilient safety culture artificial neural network Rwanda |
| url | https://www.mdpi.com/2075-5309/15/2/237 |
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