Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

Study on Time Factors in the Smoke Control Process of Highway Tunnel Fires

Fire is the most deadly risk during tunnel operation. Early rapid response and a reasonable smoke control plan are very important to evaluate tunnel fire performance. In order to study the relevant time factors for smoke management in a highway tunnel, firstly, the logical sequence and time of the fire alarm system (FAS) startup are investigated and analyzed. Then, according to the one-dimensional fluid mechanics model, the time rule of adjusting the airflow field in the tunnel from the normal operation stage to the emergency ventilation state is analyzed theoretically. Finally, the abovementioned theoretical formulas are verified through the employment of model experiments. The analysis shows that the time that passes from the start of the fire to when the exhaust fan is activated is close to 3 minutes. The time required to form a stable critical wind speed, however, is close to 7 minutes, which is longer than the 5 minutes it takes for the fire to reach its maximum temperature. Due to inertia, it takes about 0.5 to 2 minutes for the air velocity in tunnels of different lengths to drop from the traffic piston wind speed to the critical wind speed. If reverse smoke extraction is required, however, the duration is between 3 and 8 minutes. The conclusion is of guiding significance for the preparation of the emergency linkage control scheme for tunnels, as well as for the setting of initial boundary conditions for CFD fire simulations.