Vulnerability Assessment of Buildings: Considering the Impact of Human Engineering Activity Intensity Change

With accelerating urbanization, the growing density of buildings and the expansion of road networks have fundamentally reshaped the interplay between geological hazards and urban infrastructure. Traditional vulnerability assessment models for buildings (VAB) frequently overlook how human engineering activities—such as construction and city expansion—intensify disaster risk. To address this gap, we introduce VAB-HEAIC, a novel framework that integrates three dimensions of vulnerability: geological environment, building attributes, and dynamics of human engineering activity. Leveraging historical high-resolution imagery, we construct a human engineering activity intensity change indicator by quantifying variations in both road network density and building density. Nineteen evaluation factors, identified via spatial statistical analysis and field surveys, serve as model inputs. Within this framework, we evaluate four machine learning algorithms (Support Vector Regression, Random Forests, Back Propagation Neural Networks, and Light Gradient Boosting Machines), each coupled with four hyperparameter-optimization techniques (Particle Swarm Optimization, Sparrow Search Algorithm, Differential Evolution, and Bayesian Optimization), and three data augmentation strategies (feature combination, numerical perturbation, and bootstrap resampling). Applied to 5471 buildings in Dajing Town, the approach is validated using Root Mean Squared Error (RMSE). The optimal configuration—LGBM tuned with Differential Evolution and enhanced via bootstrap resampling—yields an RMSE of 0.3745. An ablation study further demonstrates that including the human engineering activity intensity change factor substantially improves prediction accuracy. These results offer a more comprehensive methodology for urban disaster risk management and planning by explicitly accounting for the role of human activity in building vulnerability.