A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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 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.
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 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.
Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN Model
Esperance Umuhoza (author) / Sung-Hoon An (author)
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
ANN model predicting quality performance for building construction projects in Rwanda
| Taylor & Francis Verlag | 2024
Industrial Wastewater Discharge and Compliance Investigation for Environmentally Resilient Rwanda
| DOAJ | 2022
Assessment of a climate-resilient and low-carbon power supply scenario for Rwanda
| BASE | 2018