Modeling important factors on occupational accident severity factor in the construction industry using a combination of artificial neural network and genetic algorithm.

BACKGROUND: Many occupational accidents annually occur worldwide. The construction industry injury is greater than the average injury to other industries. The severity of occupational accidents and the resulting injuries in these industries is very high and severe and several factors are involved in their occurrence. OBJECTIVE: Modeling important factors on occupational accident severity factor in the construction industry using a combination of artificial neural network and genetic algorithm. METHODS: In this study, occupational accidents were analyzed and modeled during five years at construction sites of 5 major projects affiliated with a gas turbine manufacturing company based on census sampling. 712 accidents with all the studied variables were selected for the study. The process was implemented in MATLAB software version 2018a using combined artificial neural network and genetic algorithm. Additional information was also collected through checklists and interviews. RESULTS: Mean and standard deviation of accident severity rate (ASR) were obtained 283.08±102.55 days. The structure of the model is 21, 42, 42, 2, indicating that the model consists of 21 inputs (selected feature), 42 neurons in the first hidden layer, 42 neurons in the second hidden layer, and 2 output neurons. The two methods of genetic algorithm and artificial neural network showed that the severity rate of accidents and occupational injuries in this industry follows a systemic flow and has different causes. CONCLUSION: The model created based on the selected parameters is able to predict the accident occurrence based on working conditions, which can help decision makers in developing preventive strategies.

Determination of bisphenol a migration from food packaging by dispersive liquid-liquid microextraction.

In the current work, a rapid and simple dispersive liquid-liquid microextraction method (DLLME) was used to determine Bisphenol A (BPA). High performance liquid chromatography with the photodiode-array detector (HPLC-DAD) coupled DLLME method was employed to analyze BPA in food samples packaged including cans, paper boxes, and glass jars. The calibration curve was obtained to be in the linear range 0.009-25 ngg-1 with a correlation coefficient of R2 = 0.9981. The mean relative standard deviations (RSDs) was of 5.2% (n = 3). The limit of detection (LOD) and the limit of quantification (LOQ) of the method were obtained to be 0.001 ngg-1 and 0.08 ng.g-1, respectively. In sum, this method presents:•A rapid, simple and efficient modified DLLME method was used to measure BPA in packaged foods.•The advantages of this method were low detection limit, fast preparation, and high BPA recovery.•The DLLME-HPLC method consists of low detection limit and high recoveries to determine BPA in samples.•The results indicated that DLLME -HPLC-DAD was an applied method to measure BPA in food samples.