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According to the concept of a system-based approach, a construction project can be treated as a complex system composed of various elements, such as human, equipment and material resources, as well as knowledge and tasks that are mutually interlinked. In the classical approach to construction project risk assessment, the impact of the “system” in the analysis of relationships between risk sources and their consequences has so far been neglected. The concept of construction project vulnerability and its adaptability has appeared in literature in recent years. It is analysed on the basis of a project’s vulnerability to the impact of risk factors and its adaptive capacity is seen an answer to project perturbations caused by adverse random events. As a part of developing the system-based approach to analysing construction project schedule, the author further developed the concept of modelling planned construction projects with relationship meta-networks composed of four types of nodes: agents (human resources), knowledge, equipment and material resources and tasks. The author included possible deviations from the planned project’s budget in the schedule vulnerability and adaptability analysis, instead of only focusing on deviations from its completion deadline. An analysis of the occurrence of additional and replacement work was introduced by the author, which further developed the concept of the simulated evolution of such networks to include the capacity to introduce new nodes and links into their structure. Furthermore, the author used the potential of weighted meta-networks to model certain dependencies within the planned project. A simulation-based approach as a part of DNA (dynamic network analysis) was used to analyse the vulnerability and adaptability of such networks. The proposed approach was presented on the example of a renovation project performed on a historical structure. The conclusions drawn from the author’s analyses can be used to formulate construction project schedules that are less vulnerable to perturbations and are characterised by greater adaptability. In the future, the author plans to expand the analysis presented above to include dependencies in single-mode networks (e.g. in agent, resource or knowledge networks) on the meta-network of a project.

Using meta-networks to analyse the impact of adverse random events on the time and cost of completing