System reliability theory based multiple-objective optimization model for construction projects

Although the system reliability theory has a high capability in quality quantification, while system reliability optimization (SRO) has been well developed in manufacturing engineering, seldom can their applications be found in the construction industry. This study aims to develop a system reliability theory based multiple-objective optimization model to conduct SRO, and then identify the cost–quality trade-off solution for construction projects. First, the whole construction project is treated as a system composed of different work packages. Second, the reliability function is employed to quantify the quality performance and the nonlinear cost-reliability function is set up. Moreover, according to the physical arrangements among each work package, the system reliability structural function is determined. Third, the total construction cost minimization and system reliability maximization are defined as multiple-objectives. The particle swarm optimization algorithm is employed to search for the Pareto-optimal solutions, from which the final cost–quality trade-off solution can be selected. A real construction case is used to evaluate the workability of the proposed model and the results have fully proven its validity and practicality.

► We propose a system reliability theory based multiple-objective optimization model.
► System reliability optimization is conducted using the proposed MOO model.
► The total construction cost is minimized and system reliability level is maximized.
► The proposed MOPSO algorithm can generate optimal results with high accuracy.