Carbon Emission Prediction of Freeway Construction Phase Based on Back Propagation Neural Network Optimization

As a large-scale transportation infrastructure project, the construction of a freeway will consume a large amount of high-energy and high-density raw material products and emit a large amount of carbon dioxide. Selecting route options with lower carbon emissions during the preliminary design phase of a project is one effective way to mitigate carbon emission pressure. This study collected 124 highway construction cases and calculated the carbon emissions generated during the construction of each case. By utilizing the grey relational analysis method, we assessed the degree of association between various indicators and carbon emissions, identifying the primary indicators influencing carbon emissions. Furthermore, we integrated multiple strategies to improve the northern goshawk optimization algorithm and optimize the BP neural network, thereby establishing a carbon emission prediction model for the highway construction phase. Using this model, we predicted the carbon emission data per kilometer of two different highway route options, which were 2.2959 t and 4.3009 t, respectively, and recommended the route option with lower carbon emissions. This model addresses the challenge faced by highway construction units in quantifying carbon emissions for different route options during the preliminary design phase, providing a basis for adjusting and comparing route options from a low-carbon perspective.