The huge financial requirement of essential transport infrastructure system has challenged the availability of government funding. To fill the fiscal gap, public-private partnerships (PPP) framework has been applied as a promising mechanism. The success of PPP projects, however, is significantly influenced by a number of critical factors. Therefore, an optimum and comprehensive evaluation of projects, reflecting critical risks, supporting investment decisions, has been highly demanded by both the public and private sector. Various works, in previous studies, have been spreading scientific models assessing risks in the construction industry, and some of them focused on the area of PPP. However, the majority of published methods just concentrated on addressing and leveling risks, and there is a lack of application in evaluating and comparing different PPP projects, as investment options, with regards to key issues. Hence, in the situation of limited budget, the public and private partners may struggle with deciding the most potential alternative. To overcome this real-world challenge, this paper, by proposing a mathematical model, attempts to optimize investment selection by evaluating different projects' riskiness with the focus on transport projects. Different actual PPP transport projects in Vietnam were employed as case studies to analyze the practicality of the proposed application.

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety.

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