Cost Prediction of Residential Buildings Based on an Improved SSA-BP Neural Network

To enhance the accuracy, stability, and interpretability of residential building cost prediction models, and thereby provide a reliable basis for project investment decision-making. This study takes Sichuan Province as the research area and develops an improved sparrow search algorithm (ISSA). The performance of the Genetic Algorithm (GA), Wolf Pack Algorithm (WPA), Sparrow Search Algorithm (SSA), and ISSA was first evaluated and compared using benchmark test functions. Subsequently, nine prediction models, including Back Propagation Neural Network (BP), GA-BP, WPA-BP, SSA-BP, ISSA-BP, Random Forest (RF), ISSA-RF, Extreme Gradient Boosting (XGBoost), and ISSA-XGBoost, were established for comparative analysis. Finally, SHapley Additive exPlanations (SHAP) were employed to rank the key factors affecting construction cost. The results show that: (1) The ISSA algorithm demonstrates excellent convergence accuracy, stability and speed on benchmark test functions. (2) The ISSA-BP model achieved an average coefficient of determination (R2) of 0.9773, an average root mean square error (RMSE) of 39.2339, an average mean absolute error (MAE) of 17.0973, an average mean absolute percentage error (MAPE) of 0.6293, and an average mean bias error (MBE) of 9.1583. Compared with the other models, ISSA-BP exhibited the best overall predictive performance. (3) SHAP analysis indicates that indicators such as total building area and structure type have the greatest impact on project cost, while roof form and roof waterproofing have the least influence. This study can serve as a reference for refining and intelligently managing construction project costs.