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Construction, by the nature of the work, is more accident-prone than other industries despite advancements in improving safety performance. Proactive mitigation and assessment of the safety performance of construction projects remain challenging due to the difficulty of acquiring, storing, and using data to produce accurate predictive models. This research focused on devising methods that allow decision makers to leverage existing data in the planning phase to streamline the development of predictive models. A data-driven approach to predict the probability of a safety incident occurring in a given construction project and within a novel discipline-level schedule is presented. By implementing the proposed model, decision makers can evaluate and mitigate the risk of a given project incident occurring by deploying discipline-level safety policies in the planning phase and modifying the schedule accordingly. A predictive model was developed based on selected safety-related metrics extracted from a data set comprising daily payroll data and incident reports, which represent 28 million working hours within eight different industrial construction projects in Canada. The model was implemented in a case study based on an industrial project to demonstrate the framework's functionality and practical utility during the project planning phase. The results show that the revised safe plan can be achieved by incorporating safety considerations in the planning phase. This research provides a practical solution for enhancing safety in the planning phase of construction projects using a data-driven model. By leveraging existing historical data, decision makers can predict potential safety incidents within specific disciplines without the need for detailed quantitative planning information. This approach also enables effective adjustments to be made to the schedule in order to mitigate risks. Furthermore, the discipline-level approach facilitates proactive safety planning by implementing discipline-specific safety policies that align with the unique characteristics of each discipline. Using a case study based on an industrial project, the proposed framework demonstrated its functionality and practical utility by identifying suitable safety-related metrics that construction enterprises typically record. These sources can include safety-related data, such as incident reports, as well as data recorded for other purposes, such as payroll data. The results highlight that incorporating safety considerations in the planning phase enables the development of a revised safety plan. In conclusion, by considering safety-related metrics and utilizing human resources (HR) data available in all companies, organizations can proactively assess and improve safety performance. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) landfills using leachate recirculation optimize the waste stabilization process by providing nutrition and moisture for biological activity. However, the leachate recirculation creates an environment with complex processes due to accelerated biodegradation and generated heat. Additionally, different timing in waste placement and the heterogeneous nature of MSW cause significant variations in properties throughout the intercalated layers. In this study, a settlement framework model employing Thermal–Hydraulic–Mechanical–Biochemical processes is proposed, which considers multiple MSW properties including temperature, pH, and saturation. The framework model includes a modified Cam-clay model to simulate short-term settlement and adopts mechanical and biological creep models for long-term settlement estimation. A long-term biological creep model that uses a single decay rate constant is revised to account for environmental factors such as temperature, pH, and saturation in estimating MSW decay rates. The framework model was calibrated using the data of large-scale column experiments, which were conducted at different temperatures and saturation conditions considering biodegradation rates. Also, an MSW placement strategy was developed to consider the effect of different waste layer placement timing in the progression of MSW landfill total settlement. The modeling framework was validated using settlement data from a landfill in Canada. The results showed that temperature and saturation have a significant influence on MSW settlement and therefore should be considered in MSW landfill settlement prediction models. In municipal solid waste (MSW) landfills, leachate recirculation increases biological activity and heat and therefore results in further breakdown of waste material and, subsequently, large settlements. Also, different climates and waste placement seasons, in addition to the nonhomogenous nature of MSW, create variations in landfill layers that need to be addressed. This study investigated a settlement model that was verified with an experiment where different temperature and saturation was applied to MSW. The settlement was divided into short-term settlement (from the compression of material) and long-term settlement from the breakdown of material with time. Additionally, since the whole MSW is not placed in a landfill at once, a strategy was developed to consider the different stages of waste placement in the developed model. The developed framework was verified using the data collected from a landfill in Canada. The results show that when we consider temperature and the amount of saturation in MSW, we can have a better prediction of MSW settlement and prevent unwanted consequences of landfill failure. [ABSTRACT FROM AUTHOR]