## 1 Introduction
Therefore, no study is on the long-term effect of the ground traffic loads during the operation period on the performance of the utility tunnel. Currently, with the development of artificial intelligence and information technology, some new methods have been used to analyze the safety operation of utility tunnels. For example, a building information modeling (BIM)-based framework for the operation and maintenance of utility tunnels has been constructed, which includes three modules (BIM model, database, and monitoring system) [22; 23]. Based on the past monitoring data, a multi-layer long short-term memory and recurrent neural network architecture has been proposed for forecasting the temperature and relative humidity inside utility tunnels [24]. Moreover, based on the selected information, the risk assessment of utility tunnels can be conducted using different methods, such as an integrated model based on dynamic hazard scenario identification, Bayesian networks and risk analysis [25] and risk interaction-based deep learning [26]. Lastly, based on the collected data from previous studies, by using the traditional artificial neural network (ANN), the damage of a tunnel deeply buried in a mountain affected by an earthquake and landslide has been predicted [27]. It can be found that, in those studies, although the safety operation of a utility tunnel or a tunnel affected by some factors using monitored information has been analyzed, the used information is collected from other studies. Therefore, only the research framework or simple method have been proposed, and a prediction of the safety of a utility tunnel affected by ground traffic loads during the operation period has not been conducted. In summarizing the previous studies, it can be found that the current studies are on the analysis of the dynamic mechanical behavior of utility tunnels affected by short time loads such as ground surface explosions or earthquake excitation, and the new methods are only used for the system analysis of the utility tunnel operation based on the collected information from other studies. Therefore, there are no studies on the prediction and evaluation of the safety performance of utility tunnels under long-term disturbance by ground vehicle loads during the operation period, especially the studies using the new methods, such as deep learning. To address this gap, here, field tests have been conducted for one real utility tunnel to obtain the big data information about the safety performance of a utility tunnel under disturbance by ground loads during the operation period. And based on a deep learning method-deep belief network (DBN) and the swarm intelligence optimization method-whale optimization algorithm (WOA), one new deep learning model-whale optimization deep belief network (WO-DBN) has been proposed. Lastly, based on the collected big data information, and by using the new deep learning model, the prediction of the performance of a utility tunnel under disturbance by ground vehicle loads during the operation period has been conducted. The novelty and aim of this study are summarized as follows: (1) it is the first study on the prediction of the safety performance of a utility tunnel under long-term disturbance by ground vehicle loads during the operation period, (2) it is a new study on monitoring the long-term performance of utility tunnels by field tests, (3) it is the first study on the selection and analysis of big data to describe the safety of utility tunnels under long-term disturbances during the operation period and (4) it is the first study on an application deep learning method for predicting the safety performance of a utility tunnel under long-term disturbance during the operation period. The rest of this paper is as follows. Section 2 gives the methodologies, including DBN, WOA, WO-DBN and field test methods. The results are provided in Section 3. Section 4 includes the discussions. Finally, the main conclusions of this study are summarized in Section 5.