Explore the complexities involved in the optimization of resource allocation and scheduling in large$-$scale civil engineering projects. Discuss how various optimization techniques, such as linear programming, genetic algorithms, and machine learning models, can be applied to achieve efficient resource utilization. Consider the multifaceted nature of civil engineering projects, which often involve multiple stakeholders, conflicting objectives, and numerous constraints such as budget limits$,$ labor availability, and material supply. How do these optimization methods address the challenges posed by such constraints? Furthermore, analyze the role of uncertainty and risk management in the optimization process, particularly in the context of unforeseen events like natural disasters or economic fluctuations. How can civil engineers integrate real$-$time data and predictive analytics to dynamically adjust their plans and maintain project timelines and budgets? Provide a thorough examination of the theoretical underpinnings of these optimization techniques and their potential impacts on the overall success and sustainability of civil engineering projects.