Showing 3 results for Construction Industry
Hasti Borgheipour, Ghazaleh Monazami Tehrani, Shahriyar Madadi, Iraj Mohammadfam,
Volume 10, Issue 1 (3-2020)
Abstract
Introduction: Cranes are of the major causes of accidents in the construction industries. As human error mostly causes crane accidents, this study aims to investigate the human errors of tower crane operators in the construction projects using SHERPA and CREAM techniques.
Material and Method: In this research, first, all of the tasks of the tower crane operator were identified and analyzed. Then, adopting SHERPA technique, probable operator errors were identified in each task and the control modes and error probability were determined by CREAM technique. Finally, all the human errors risks were assessed and the actions for risk control were defined to control them in the acceptable level.
Result: According to the SHERPA technique, 148 errors were identified in the crane operator tasks. The human error assessment showed that monitoring the anti-collision system with the risk probability of 0.0003 has the highest control factor, while monitoring the existing guards with the risk probability of 0.056 has the lowest control factor. Also, the important tasks with high human errors were monitoring the guards with the cognitive risk probability of 0.07 and the tasks with cognitive risk probability of 0.05.
Conclusion: The findings in this study indicated that using complementary qualitative and quantitative methods can provide identification and prioritization of identified errors. This can help the organization to allocate limited organizational resources to control unacceptable risks and increase the efficiency and effectiveness eventually.
Rostam Esmaeili, Ahmad Ali Babaei, Ghazaleh Monazami Tehrani,
Volume 11, Issue 2 (6-2021)
Abstract
Introduction: Each country needs to preserve its human capital through preventing accidents for its development. Therefore, this study is carried out to study the relationship between safety investments and safety performance indices considering the interactive effect of the project hazard level in construction industry.
Material and Methods: This study was conducted using multiple case studies in 5 major construction worksites, in Tehran, in 2019. Data was collected using questionnaire, checklists and interview as well as evaluating the safety documents. The data analysis in this study was carried out using SPSS 18.
Results: There was a strong inverse correlation between safety investments (total safety investment, basic safety investment, and voluntary safety investment) and accident frequency rate (AFR) (r=-0.936, P-value<0.05), and there was a direct strong correlation between safety investment and safety performance (P-value<0.05, r=0.939). Also, the effect of various safety investments on safety performance indices under various project conditions (project hazard levels) was not the same; when the project hazard level was high, the effect of safety investments on safety performance was higher.
Conclusion: Increasing safety investment improves safety performance through decreasing the accidents. Also, investment in both safety components (basic safety investment and voluntary safety investment) might improve safety performance. The results of the current study can be used as a basis by the contractors and construction companies to invest in safety and to determine proper budget for managing safety of construction projects.
Mehran Maleki Roveshti, Zahra Naghavi-Konjin, Siavash Etemadinezhad, Jamshid Yazdani Charati,
Volume 14, Issue 1 (3-2024)
Abstract
Introduction: Steel erection is known as one of the most hazardous construction activities. From an occupational health and safety perspective, this process carries high risk. Therefore, this study aims to conduct a qualitative risk analysis of steel structure assembly and model it using the Functional Resonance Analysis Method (FRAM).
Material and Methods: In this cross-sectional study, the construction site of a high-rise building steel structure was first visited to identify the main processes involved. Then, semi-structured and open-ended interviews were conducted with 33 workers partaking in this process. Data from the interviews and process identification were entered into FRAM Model Visualiser (FMV) software to investigate and model complex relationships and interactions between daily tasks.
Results: Of the 19 major system component functions identified, four functions had potential instability and defects due to complex human, organizational, and technological function interactions. By intensifying the FRAM graphic model, risks may be imposed on the system if the interactions of these four functions are neglected. These include coordination with the experienced rigger, preparation of the tower crane, attachment of parts at the installation site, and execution of the rescue rope.
Conclusion: The findings demonstrate that conducting qualitative risk assessment and modeling the steel frame construction process using FRAM allows for an in-depth understanding of nonlinear conditions and dynamics resulting from escalating technical-social interactions. This approach enables a comprehensive analysis of system safety status.
