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Safety risk management has a considerable effect on disproportionate injury rate of construction industry, project cost and both labor and public morale. On the other hand time-cost optimization (TCO) may earn a big profit for project stakeholders. This paper has addressed these issues to present a multi-objective optimization model to simultaneously optimize total time, total cost and overall safety risk (OSR). The present GA-based optimization model possesses significant features of Pareto ranking as selection criterion, elite archiving and adaptive mutation rate. In order to facilitate safety risk assessment in the planning phase, a qualitative activity-based safety risk (QASR) method is also developed. An automated system is codded as an Excel add-in program to facilitate the use of the model for practitioners and researchers. The model has been implemented and verified on a case study successfully. Results indicate that integration of safety risk assessment methods into multi-objective TCO problem improves OSR of nondominated solutions. The robustness of the present optimization model has also been proved by its great ability to prevent genetic drift as well as the improvement in the bicriteria among generations.

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This paper introduces a methodology for considering the uncertainties in stability analysis of gravity dams. For this purpose, a conceptual model based on the fuzzy set theory and Genetic Algorithm (GA) optimization is developed to be coupled to a gravity dam analysis model. The uncertainties are represented by the fuzzy numbers and the GA is used to estimate in what extent the input uncertainties affect the dam safety factors. An example gravity dam is analyzed using the proposed approach. The results show that the crisp safety factors might be highly affected by the input uncertainties. For instance, ±10%uncertainty in the design parameters could result in about −346 to + 146 % uncertainty in the stability safety factors and −59 to + 134 % in the stress safety factor of the example dam.

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Slope stability is one of the most complex and essential issues for civil and geotechnical engineers, mainly due to life and high economical losses resulting from these failures. In this paper, a new approach is presented for estimating the Safety Factor (SF) for circular failure slope using hybrid support vector regression (SVR) and Ant Colony Optimization (ACO). The ACO is combined with the SVR for determining the optimal value of its user-defined parameters. The optimization implementation by the ACO significantly improves the generalization ability of the SVR. In this research, the input data for the SF estimation consists of the values of geometrical and geotechnical input parameters. As an output, the model estimates the SF that can be modeled as a function approximation problem. A data set that includes 46 data points is applied in current study, while 32 data points are used for constructing the model, and the remainder data points (14 data points) are used for assessment of the degree of accuracy and robustness. The results obtained show that the hybrid SVR with ACO model can be used successfully for estimation of the SF.

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Construction industry has the highest ratio of fatality of workers in comparison with other industries. Construction safety has been always a matter of focus to control safety risks. This article presents a new flexible method of safety risk assessment by adding Hybrid Value Number (HVN) to the assessment equation. As a result of using this method, the results of assessment process will be more consistent with the project’s conditions, as well as being more trustful. It could provide a better perspective of safety risks for project managers. The most significant outcomes of this research are as follows: 1) the most influential factors which affect safety risks in building construction projects are "the proficiency and the experience of workers", "the complexity of construction technology" and "time limitation", 2) the biggest risk priority numbers belong to "Struck by falling objects" and "Falling to lower levels" hazards, 3)a necessary safety program must contain Personal Protective Equipment (PPE), safety measures and safety training, 4)Project managers can decrease 75% of total safety risks by investing less than 1.5% of construction budget on safety programs.