Decision Support Framework for Post-War Infrastructure Revitalization Using a Hybrid Fuzzy–Simulation–ANN Model

Post-war reconstruction requires effective decision-support tools capable of integrating technical, economic, and organizational criteria under conditions of high uncertainty. The evaluation and prioritization of damaged buildings for recovery interventions are critical challenges for reconstruction project management. This study proposes a hybrid decision-support framework for assessing the strategic feasibility of building recovery using a novel Strategic Revitalization Index (SRI). The proposed methodology integrates a hierarchical fuzzy inference system, simulation techniques, and an artificial neural network surrogate model. The fuzzy model aggregates four key evaluation dimensions: technical condition of the building, economic feasibility of recovery actions, project implementation capability, and environmental and social impact. To analyze the model’s behavior and generate training data, a synthetic dataset was created using Latin Hypercube Sampling, covering a wide range of possible reconstruction conditions. The generated dataset was subsequently used to train an artificial neural network capable of approximating the nonlinear mapping implemented by the fuzzy decision model. The obtained results demonstrate high predictive performance of the surrogate model, with R2 = 0.976, RMSE = 0.0266, MAE = 0.0133, and MAPE = 4.95%. Scenario analysis further illustrates how different recovery strategies influence SRI values and enables comparison of alternative reconstruction approaches. The proposed framework provides a flexible analytical tool for supporting strategic decision-making in post-war reconstruction projects. By combining fuzzy logic, simulation techniques, and machine learning, the model enables systematic prioritization of recovery strategies and may support large-scale reconstruction planning in post-conflict environments.