Estimation of illuminance on the south facing surfaces for clear skies in Iran

Background: Daylight availability data are essential for designing effectively day lighted buildings. In respect to no available daylight availability data in Iran, illuminance data on the south facing vertical surfaces were estimated using a proper method. Methods: An illuminance measuring set was designed for measuring vertical illuminances for standard times over 15 days at one hour intervals from 9 a.m. to 3 p.m. at three measuring stations (Hamadan, Eshtehard and Kerman). Measuring data were used to confirm predicted by the IESNA method. Results: Measurement of respective illuminances on the south vertical surfaces resulted in minimum values of 10.5 KLx, mean values of 33.59 KLx and maximum values of 79.6 KLx. Conclusion: In this study was developed a regression model between measured and calculated data of south facing vertical illuminance. This model, have a good linear correlation between measured and calculated values (r= 0.892)

Modeling of Individual and Organizational Factors Affecting Traumatic Occupational Injuries Based on the Structural Equation Modeling: A Case Study in Large Construction Industries

Background: Individual and organizational factors are the factors influencing traumatic occupational injuries. Objectives: The aim of the present study was the short path analysis of the severity of occupational injuries based on individual and organizational factors. Materials and Methods: The present cross-sectional analytical study was implemented on traumatic occupational injuries within a ten-year timeframe in 13 large Iranian construction industries. Modeling and data analysis were done using the structural equation modeling (SEM) approach and the IBM SPSS AMOS statistical software version 22.0, respectively. Results: The mean age and working experience of the injured workers were 28.03 ± 5.33 and 4.53 ± 3.82 years, respectively. The portions of construction and installation activities of traumatic occupational injuries were 64.4% and 18.1%, respectively. The SEM findings showed that the individual, organizational and accident type factors significantly were considered as effective factors on occupational injuries’ severity (P < 0.05). Conclusions: Path analysis of occupational injuries based on the SEM reveals that individual and organizational factors and their indicator variables are very influential on the severity of traumatic occupational injuries. So, these should be considered to reduce occupational accidents’ severity in large construction industries

A Neural Network Classifier Model for Forecasting Safety Behavior at Workplaces

The construction industry is notorious for having an unacceptable rate of fatal accidents. Unsafe behavior has been recognized as the main cause of most accidents occurring at workplaces, particularly construction sites. Having a predictive model of safety behavior can be helpful in preventing construction accidents. The aim of the present study was to build a predictive model of unsafe behavior using the Artificial Neural Network approach. A brief literature review was conducted on factors affecting safe behavior at workplaces and nine factors were selected to be included in the study. Data were gathered using a validated questionnaire from several construction sites. Multilayer perceptron approach was utilized for constructing the desired neural network. Several models with various architectures were tested to find the best one. Sensitivity analysis was conducted to find the most influential factors. The model with one hidden layer containing fourteen hidden neurons demonstrated the best performance (Sum of Squared Errors=6.73). The error rate of the model was approximately 21 percent. The results of sensitivity analysis showed that safety attitude, safety knowledge, supportive environment, and management commitment had the highest effects on safety behavior, while the effects from resource allocation and perceived work pressure were identified to be lower than those of others. The complex nature of human behavior at workplaces and the presence of many influential factors make it difficult to achieve a model with perfect performance

Primjena regresijskog modela u analizi ključnih čimbenika koji pridonose težini nesreća u građevinskoj industriji u Iranu

Construction industry involves the highest risk of occupational accidents and bodily injuries, which range from mild to very severe. The aim of this cross-sectional study was to identify the factors associated with accident severity rate (ASR) in the largest Iranian construction companies based on data about 500 occupational accidents recorded from 2009 to 2013. We also gathered data on safety and health risk management and training systems. Data were analysed using Pearson’s chi-squared coefficient and multiple regression analysis. Median ASR (and the interquartile range) was 107.50 (57.24-381.25). Fourteen of the 24 studied factors stood out as most affecting construction accident severity (p<0.05). These findings can be applied in the design and implementation of a comprehensive safety and health risk management system to reduce ASR.Građevinska se industrija povezuje s najvišim rizikom od nesreća na radu i tjelesnih ozljeda u rasponu od blagih do vrlo teških. Cilj ovoga presječnog istraživanja bio je utvrditi čimbenike povezane s indeksom težine nesreća među najvećim građevinskim tvrtkama u Iranu na temelju podataka iz 500 izvještaja o nesrećama na radu prikupljanih od 2009. do 2013. Usto smo prikupili podatke o upravljanju rizikom za sigurnost i zdravlje radnika te o njihovu obrazovanju u tom pogledu. Podaci su analizirani Pearsonovim hi-kvadratnim testom i modelom višestruke regresije. Medijan indeksa težine nesreća (i interkvartilni raspon) iznosio je 107,50 (57,24-381,25). Na težinu nesreća najviše je utjecalo četrnaest od 24 ispitana čimbenika (p<0,05). Ovi rezultati mogu biti korisni u osmišljavanju i uspostavi obuhvatnih sustava upravljanja rizikom za sigurnost i zdravlje radnika kako bi se smanjio indeks težine nesreća na radu

Modeling of causes and consequences of human error in mining processes design: A qualitative study

This research was funded by the Hamedan University of Medical Sciences and Health Services and grant number 140008257113.Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarObjectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version

Evaluation and Management of Human Errors in Critical Processes of Hospital Using the Extended CREAM Technique

Medical errors result in serious and often-preventable problems for patients. Human errors can be used as an opportunity for learning as well as a key factor for patients’ safety improvement and quality of patients' surveillance in hospitals. The aim of the present study was to identify and evaluate human errors to help reduce risks among personnel who render health services during critical hospital processes. This cross-sectional study was done in the Besat hospital in Hamedan in 2016. At first, the critical processes were selected via given scores in Delphi method and by multiplying the scores of each of the five criteria including the severity of the consequences caused by error incidence, probability of error, capability of the error detection, task repeatability and type of hospital ward with each other. Determining the risk numbers of each process, three ones were chosen with the largest scores. At the end, the selected processes were analyzed by the method of extended CREAM. The results showed that the highest CFP is associated with the CPR process, particularly in the sub-stage of command of starting CPR by anesthesiologists (0.0891), the one in the giving medicine process is in the sub-stage of calculating of medicine dozes and determining prescription methods (0.0796) and also the one in the tracheal intubation process is in the sub-stages of pulmonary and respiratory monitoring of patients and observing the vocal cords and larynx of patients (0.0350). Regarding the critical consequences of human errors in the selected processes, reviewing the qualities of roles and responsibilities of each of the medical group members and providing specialized introduction for hospital processes seem necessary

Root causes analysis of the Blow out of oil and gas wells in the drilling industry using Bow-Tie Analysis

Abstract Background and aims: One of the major concerns in the oil and gas drilling industry are Blowouts. Blowout could have severe consequences, such as fire and explosions, releases of toxic gases and environmental disasters. The aim of this study is to identify the root causes of kick and blowout in drilling industry. Methods: In this study, FTA investigates root causes of a kick while ETA explores the possible consequences (including blowout) arising from a kick. Bow Tie Analysis (BTA) combines FTA and ETA to explore the root causes and consequences (blowout) of a kick in onshore exploration Results: 28 basic event (root causes) have been identified for kick. Also to mitigate kick consequences, 8 safety barriers recognized. The probability of kick and blowout was calculated as 1/23&#215; &#160;and 2/94&#215; &#160;, respectively. The order of importance measure of each root causes was listed. Conclusions, In this study, Getting into the high pressure zone and not filling well with drilling mud during the trip up, was identified as the most important root causes of kick. Furthermore, early diagnosis of kick and the proper functioning of Blow-Out Preventer (BOP) were recognized as the first safety barrier to prevent the blowout

Providing an early warning framework to identify, assess and control human performance influencing factor in automotive industry

Background and aims: Accident root causes' analysis shows optimization of factors affecting performance has an essential role in reducing of accidents. These factors are dynamic and complex and they may also be dependent to each other. Therefore, a comprehensive analysis of the working environment is essential. The main objective of this study was to propose a framework to control human performance influencing factors in a automotive industry in Iran. Methods: The present study provided an early warning model that predicts the risk factors affecting human performance. Since behavioral factors that are causing errors are complex in structure, FANP method was used for modeling. Using the proposed model, the potential risk of workplace determined before it leads to accidents and based on the type and level of risk and risk control measures was determined. The model was tested on two major projects in the car manufacturing industry. Results: The results show that the risk indexes in the first and second project are 0.391 and 0.197 respectively. Since the value of the index in the first project is greater than the amount authorized by the model so corrective action suggested in accordance with identified risk factors without stopping the system. Conclusion: The system can predict and assess the performance influencing factors as an early warning system. As a result, this system will lead to improved performance and enhanced safety

A fuzzy analytic hierarchy process-TOPSIS framework for prioritizing emergency in a petrochemical industry

Background: Petrochemical industry has experienced a variety of accidents and the number of emergency situation in this industry is high. Therefore, prioritizing these situations is important. The aim of this study was to determine the effective criteria for the selection of emergency scenario and prioritize them for corrective actions. Subjects and Methods: Delphi technique was used to determine and prioritize the appropriate criteria for the selection of high-risk emergency scenarios. Then, the weights of selected criteria were obtained using fuzzy hierarchical analysis and finally, using the fuzzy TOPSIS technique, the criteria for emergency scenarios were prioritized for corrective actions. Results: The most important criteria for the selection of dangerous emergencies included the amount of loss, damage, and probability. According to the weight of these criteria, emergency situations were prioritized. The most important emergencies included fire in the chemical storage, hydrogen leakage at the cylinder fitting in the Alfin unit, and extreme gas leakage in one of the power plant turbines. Conclusions: Using this approach, high-priority emergencies can be identified, and it is suggested that planning for controlling these situations and preventing crises should be prioritized by managers

An Intervention for the Promotion of Supervisor's Incidents Reporting Process: the Case of a Steel Company

The analysis of incidents is one way of increasing safety in workplaces. In this approach, the process of preparing exact and scientific report is a critical step. The aim of this paper was to describe an intervention supporting the improvement of supervisors' participation to report all occurred incidents. In this study, Future Workshop method was used with 44 supervisors in TAB Steel Company, Tabriz, Iran. In each subject, 11 supervisors were participated in four small groups, which they normally worked. In the Critique phase, the 4 teams reported 126 problems in the incident report process. During the fantasy phase, the teams produced 727 suggestions to solve the problems. Then, the supervisors made decisions on 35 commitments to change their incident reporting behaviors. Finally, in the implementation phase the number of reported incidents increased by 79.4% during the 1-year follow-up period. The discussion method used in Japan, Finland, and Sweden was also successfully implemented in Iran, and the process raised a great number of problems and suggestions related to supervisor's incident reporting process. Creating and maintaining the proper communicational canals among supervisors and the managers of safety and health unit are the suggestions, which have been presented to increase the amount of partnership