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The purpose of this paper is to investigate the creep properties of Sn‐0.7Cu composite solder joints reinforced with optimal nano‐sized Ag particles in order to improve the creep performance of lead‐free solder joints by a composite approach.

The composite approach has been considered as an effective method to improve the creep performance of solder joints. Nano‐sized Ag reinforcing particles were incorporated into Sn‐0.7Cu solder by mechanically mixing. A systematic creep study was carried out on nano‐composite solder joints reinforced with optimal nano‐sized Ag particles and compared with Sn‐0.7Cu solder joints at different temperatures and stress levels. A steady‐state creep constitutive equation for nano‐composite solder joints containing the best volume reinforcement was established in this study. Microstructural features of solder joints were analyzed to help determine their deformation mechanisms during creep.

The creep activation energies and stress exponents of Ag particle‐enhanced Sn‐0.7Cu lead‐free based composite solder joints were higher than those of matrix solder joints under the same stress and temperature. Thus, the creep properties of nano‐composite solder joints are better than those of Sn‐0.7Cu solder joints.

The findings indicated that nano‐sized Ag reinforcing particles could effectively improve the creep properties of solder joints. A new steady‐state creep constitutive equation of nano‐composite solder joints was established. Deformation mechanisms of Sn‐0.7Cu solder and nano‐composite solder joints during creep were determined.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.