RESILIFE: Resilient life-cycle optimization of socially and environmentally efficient hybrid and modular structures under extreme conditions
Principal researchers:
VÍCTOR YEPES PIQUERAS
JULIÁN ALCALÁ GONZÁLEZ
Team members:
- JOSÉ V. MARTÍ ALBIÑANA
- TERESA PELLICER ARMIÑANA
Funding Agency:
Duration: 01/09/2024 – 31/08/2027
Reference: PID2023-150003OB-I00
Abstract
Natural and human-induced disasters generate significant losses in human lives and economic terms. Affected structures must be redesigned to restore their functionality as quickly as possible, which requires substantial resources and results in considerable emissions. Therefore, infrastructure design and construction must focus on sustainability, durability, multi-resistance, resilience, and intelligent monitoring throughout their life cycle. Extreme events, combined with design errors, construction issues, and lack of maintenance, can cause localized structural damage, which may lead to the progressive collapse of infrastructures. RESILIFE addresses the social challenge of maintaining and repairing structures in extreme situations by optimizing complex problems in public and private decision-making. The fundamental hypothesis is that optimal design and the construction of hybrid structures based on modern construction methods—especially modular ones—are effective from both a social and environmental perspective, demonstrating resilience to extreme events. The challenge lies in incorporating design improvements to face these events, ensuring that these new structures perform and provide safety comparable to traditional ones. Central innovation involves developing clear procedures to quantify the resilience of structures against multiple threats and compare different systems based on their resilience. To achieve this, artificial intelligence techniques will optimize resilience, demonstrating their effectiveness in both social and environmental terms against extreme events. The methodological novelty is found in using emerging hybrid metaheuristics and Deep Learning techniques in multi-objective optimization and game theory, seeking rapid functional recovery with reduced social and environmental costs, thereby avoiding progressive collapse. Additionally, there will be a deeper exploration of emerging multi-criteria decision-making techniques, such as neutrosophic logic and Bayesian networks. This approach improves the quality and speed of calculations in the design, maintenance, and repair of structures and addresses real-world uncertainties, proposing resilient optimization based on reliability and robust designs. In this context, it is essential to recognize that real-world uncertainties, imperfections, and deviations exist concerning the parameters used in design codes. Therefore, an optimal structure is situated close to the infeasibility region, making it necessary to integrate these uncertainties to provide more robust and reliable designs. On the other hand, severe budgetary constraints during times of crisis severely affect policies for creating and preserving infrastructures, especially if incorporating resilience into the design leads to increased costs. The expected results, following a sensitivity analysis of various budgetary policies over a specific time horizon, will aim to detail which types of structures, specific repair and conservation actions, as well as demolition and reuse alternatives, are most suitable for minimizing environmental and social impacts, considering the inherent variability of each situation.
