The construction work environment remains one of the most hazardous among all industries. Construction injuries directly impact the workers and the work itself, including personal suffering, construction delays, productivity losses, higher insurance premiums, and possible liability suits for all parties involved in the project. The costs resulting from personal injuries, combined with the associated financial impact resulting from schedule disruptions, insurance hikes, and workers’ compensation, can impact a project’s profitability. Many of these impacts can be minimized or avoided through the continuous assessment and improvement of safety policies and practices. This paper aims to propose a new safety assessment methodology that equips insurance companies and construction managers with an optimal mechanism for evaluating the safety performance of construction companies. The proposed model consists of 20 evaluation criteria that are used to establish the efficiency benchmarks and provide comparison feedback for improving the company’s safety plans and procedures. These criteria are determined based on leading and lagging safety performance indicators. The data envelopment analysis (DEA) technique is used as the underlying model to assess the relative efficiency of safety practices objectively. Two illustration case studies are provided to demonstrate the dual effectiveness of the DEA model. The presented research contributes to the body of knowledge by formalizing a robust, effective, and consistent safety performance assessment. The model equips the company with the ability to track both the progression and the retrogression over time and provides feedback on ineffective practices that need more attention. Simultaneously, the model gives them more detailed safety performance information that can replace the current experience modification rating (EMR) approach. It provides insurance companies with an objective and robust evaluation model for selecting optimum rates for their clients. In addition, the data comparison utility offered by the DEA model and its criteria can be helpful for insurance companies to provide effective advice to their clients on which safety aspects to improve in their future strategies.
An outbreak of the 2019 novel Coronavirus epidemic (COVID-19) has rapidly spread worldwide. The coronavirus (COVID-19) has also spread among children, but it has been less severe than in adults. The characteristics of COVID-19 laboratory findings play a significant role in clinical manifestations, diagnosis, and treatment. Since the numbers of COVID-19 cases increased, it takes more time to interpret the lab outcomes and provide an accurate diagnosis. Little information about the clinical symptoms and epidemiological of COVID-19 is known. There is a need to investigate the characteristics of laboratory findings for the clinical decision-making system using predictive algorithms. This study aims to classify and validate machine learning approaches for detecting COVID-19 in children. The five well-known machine learning approaches: the artificial neural network (ANN); random forest (RF); support vector machines (SVM); decision trees (DT) which include classification and regression trees (CART); and gradient boosted trees (GBM) were used. All these approaches have been considered in the classification, and to determine the most suitable model. The performance of each model test was by conducted using a standard 10-fold cross-validation procedure. Given these results for classification performance and prediction of accuracy, CART is the best predictive model for classifications for children with COVID-19. The results of the study illustrate that the best classification performance was achieved with CART model to provide 92.5% accuracy for binary classes (positive vs. negative) based on laboratory findings. Leukocytes, Monocytes, Potassium, and Eosinophils, were among the most important predictors which indicate that those features may play a crucial role in COVID-19. Ultimately, our model may be helpful for medical experts to predict COVID-19 and can help invalidate their primary laboratory findings of children. ML methods can be a convenient tool for providing predictions for COVID-19 laboratory findings among Children.
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