Air pollutant removal performance using a BiLSTM-based demand-controlled ventilation method after tunnel blasting

Abstract

Efficient tunnel ventilation is essential for ensuring construction safety and protecting personnel health during tunnel construction. This study proposes a demand-controlled ventilation (DCV) method on the basis of deep learning algorithm to both improve pollutant removal efficiency and reduce energy consumption. The DCV method utilizes a two-layer bidirectional long short-term memory algorithm (BiLSTM) to predict pollutant concentrations. The air volume is dynamically adjusted based on the gaseous pollutant removal requirements. The coefficient of ventilation performance (COVP) is proposed to evaluate the performance of two ventilation methods (DCV and constant air-volume ventilation (CAV)) through computational fluid dynamics (CFD) simulations. The results show that the DCV results in a lower maximum average CO concentration and higher removal efficiency in the heading area (372.3 mg/m³) than the CAV does (404.1 mg/m³). The fan's energy consumption of DCV is 64.6% lower than that of CAV during a 1000 s ventilation period. The COVPs for both methods exhibit temporal variation and achieves their maximums (2.25 for DCV and 0.741 for CAV) after reaching the constraint conditions (air volume threshold). The DCV method expedites pollutant elimination, reduces construction waiting period, and minimizes energy consumption, providing a novel application of a deep learning algorithm in construction engineering.