Optimizing safety-measure combinations to address construction risks

Abstract

Objectives. Most methods used to develop construction risk responses address the risk-mitigation optimization problem by solving the objective functions. They are passively achieved by satisfying constraint conditions, which are not adequate for efficient construction management. This study aims to provide an active optimization strategy for selecting risk responses. Methods. We combined set pair analysis (SPA) with the technique for order preference by similarity to an ideal solution (TOPSIS) to control the construction risks to an acceptable level instead of excessively to the minimum level. SPA is employed to assess the pre-mitigation and post-mitigation risk levels based on the uncertainty theory, and the TOPSIS is used to rank safety measures based on their risk-mitigation effects. A case study of concrete pumping for a super high-rise building was used to exemplify how the proposed optimization model assists risk control and to validate its reasonability. Conclusion. The developed TOPSIS–SPA-based method figures out the optimal safety-measure combination reducing construction risks economically to an acceptable level with the fewest number of measures. The findings can assist decision-makers in formulating cost-effective risk-control schemes.

Keywords:

• construction risk
• acceptable risk level
• optimal safety measure
• set pair analysis
• technique for order preference by similarity to an ideal solution

Acknowledgements

The authors thank the National Natural Science Foundation of China and the National Key Research and Development Program of China for their support for this study. The authors are also grateful for input from industry professionals who participated in this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number: 51878382]; National Key Research and Development Program of China [grant number: 2016YFC0802001].