Optimization of construction under crisis conditions and subsequent recovery: adaptive models for critical infrastructure - Blonnyi, A. (Ways to Improve Construction Efficiency, 2024)
Adapting construction processes to conditions of crisis situations and subsequent recovery is one of the key factors in ensuring the resilience of critical infrastructure and the viability of cities under modern socio-economic and environmental conditions. Construction optimization in such scenarios involves the use of adaptive models that allow for the integration of risk assessment, resource planning, timeframes, and financial constraints into a unified project management system. The high uncertainty characteristic of crisis periods—ranging from economic shocks and natural disasters to man-made accidents—requires flexible decision-making methods that ensure prompt response and minimize negative consequences. Within the framework of optimizing construction processes for critical infrastructure, digital modeling tools play a significant role, including BIM, geographic information systems, and decision support systems, which allow forecasting of situations, risk evaluation, and identification of critical resources. Adaptive models enable the combination of scenario planning, probabilistic risk assessment, and multifactor analytics, ensuring the integration of strategic, tactical, and operational decisions. This is particularly relevant for projects within critical infrastructure, where any delay or error can have significant socio-economic impacts. A crucial component of optimization is the coordination between government authorities, private companies, and civil society organizations, ensuring effective resource management and real-time control over project execution. Furthermore, adaptive models facilitate post-crisis recovery planning by defining reconstruction priorities, optimizing resource utilization, and sequencing the implementation of key critical infrastructure objects. As a result of applying adaptive approaches to construction optimization, forecast accuracy increases, response times to emergencies are reduced, the efficiency of material and financial resource use is enhanced, and sustainable development of critical infrastructure objects is ensured.