Intelligent Prediction and Optimization of Concrete Resource Mismatch in China’s Construction Industry—An Explainability Analysis Based on XGBoost-SHAP

Concrete is the most carbon-intensive sector of building and construction. It leads to severe project results due to poor distribution. Cost increases, project delays, and the production of greenhouse gases. Machine learning techniques may be helpful when looking at complicated construction data; however, the issue with machine learning is the “black box” issue, and this causes a big trust issue between the project manager and the prediction model, and, thus, no one will make a choice based on the model. For that, we have Explainable AI. It brings together the power of XGBoost’s predictions with the interpretability of SHAP. We used it on the data from our four mega projects, and we got a very nice R-squared of 0.922. By looking at the SHAP analysis result, we can see that Electricity Consumption per Unit Concrete (ECUC) is the main driver, and there is a threshold for ECUC, which is 40.3 kWh/m3. If ECUC is greater than 40.3 kWh/m3, then resource waste will increase in a nonlinear way. And we transform these conclusions into a three-level optimization plan to indicate that it may be possible to reduce resources that do not fit by 38.6%. This study gives a new “prediction–interpretation–optimization” model, instead of prediction only, it gives practical and transparent decisions in construction that can use resources properly.