Abstract: Traditional numerical simulation methods has long calculating period for pollutant concentration in construction tunnels, this disables real-time demand. To address this challenge, a rapid prediction method integrating proper orthogonal decomposition (POD) and support vector machine (SVM) is proposed, aiming to enable rapid reconstruction of pollutant concentration fields in construction tunnels under complex working conditions. In this approach, a numerical model is first established for CO migration in tunnels after blasting, simulating CO concentration distributions under typical scenarios with varying explosive quantities and altitudes, thus yielding a sample dataset. High-dimensional concentration field data are then subjected to dimensionality reduction via POD, where the first three-order modes selected based on energy contribution serve as characteristic basis functions to mitigate computational complexity. Further, an SVM model is employed to construct a nonlinear mapping between known operational parameters and POD modal coefficients, enabling fast prediction of tunnel CO concentrations at target times under specified conditions. Validation results demonstrate the model′s notable accuracy and efficiency. For predicting CO concentrations under unknown explosive quantities, the maximum relative error is no more than 15% with an average of 2.73%; for unknown altitudes, the maximum relative error is no more than 6% with an average of 1%. Critically, the computation time of the proposed model is 1/600 of that required for traditional numerical simulations, facilitating real-time acquisition of tunnel pollutant concentration fields.

Key words: construction tunnel, pollutant concentration, proper orthogonal decomposition, support vector machine, rapid prediction