Research on Carbon Emission Prediction During the Physical Construction Stage of Highway Tunnels Based on SSA-BPNN

The construction of highway tunnels consumes a large amount of resources and energy, resulting in substantial greenhouse gas emissions. To achieve the goals of “low carbon emission” and even “zero carbon emission” in the tunnel engineering field, it is necessary to carry out carbon emission prediction for tunnel construction. This study takes the Ludian-Qiaojia expressway tunnel project in Yunnan Province as an example, focusing on the prediction of carbon emissions in the physical construction stage of the tunnel, aiming to improve the accuracy and stability of the existing prediction methods. Based on clarifying the boundaries and methods of carbon emission calculation, the carbon emissions during the physical construction phase of the case project tunnel are calculated using the emission coefficient method, and the calculation results are used as a dataset to construct the BPNN prediction model. Four parameters that are easy to obtain and significantly related to carbon emissions in the physical construction phase of tunnel, namely surrounding rock grade, tunnel burial depth, surrounding rock quality and special geological conditions, are selected as input features. Spearman correlation analysis verifies the science and objectivity of these four parameters as input features. To address the difficulty of accurately modeling the nonlinear mapping relationship between carbon emissions in the tunnel physical construction phase and geological parameters, the SSA algorithm with the advantages of strong optimization ability, fast convergence and few parameters is innovatively introduced to optimize the initial weight and threshold of BPNN, and the SSA-BPNN model is established. The model performance evaluation results show that the R² of the SSA-BPNN model can reach 0.974, and the fit effect is good. Compared with the single BPNN model and the BPNN models optimized by other intelligent optimization algorithms, SSA-BPNN model has the highest prediction accuracy and the lowest error, proving that the SSA-BPNN model is the optimal model in this case and also shows its superiority in the prediction of carbon emissions in the tunnel physical construction stage. This study provides an effective technical tool for the decision-making of low-carbon tunnel alignment.