## 1 Introduction  
Buried pipeline engineering is an important lifeline project for the country. However, the geological environment along the pipeline is complex and changeable, and collapse accidents occur frequently, causing huge losses to human life and property. Therefore, studying the dynamic evolution process of buried pipeline collapse and establishing a settlement prediction model to provide early warning and guarantee measures for the safe transportation of pipelines are important research topics to achieve sustainable development for mankind. In recent years, many scholars have conducted a lot of research on pipe-soil collapse. In terms of mechanical analysis of buried pipelines under the action of soil collapse, most scholars' research focuses on the establishment of a coupling model of pipe-soil action and the accurate results of the elastic foundation beam model. Although there have been a lot of studies on the mechanical analysis of buried pipelines suffering from soil collapse, there are few reports on the dynamic evolution process of buried pipeline collapse. In 2004, [PERSON] obtained monitoring settlement data to establish an elastic foundation beam model and proposed a stress prediction formula for buried pipelines in the settlement area [1]. In 2015, [PERSON] established a pipeline mechanics model for settlement and uplift and provided a simple method for calculating the internal force and strain of the pipeline [2]. In 2019, [PERSON] established a model for the settlement of buried pipelines caused by tunnel excavation [3]. They gave the parameter that has the greatest impact on pipeline settlement as the elastic modulus of the soil and established the relationship between ground settlement and the settlement deformation of buried pipelines. In 2020, [PERSON] carried out mechanical analysis work on buried polyethylene pipelines, and the results showed that the ground settlement rate has little effect on the ultimate bearing capacity of buried pipelines [4]. In 2022, [PERSON] studied the relationship between soil parameters, pipeline parameters, and settlement displacement under the action of foundation collapse and found that the mid-span pipeline had tensile stress and shear stress on both sides of the collapse zone [5]. In 2023, [PERSON] established a stress prediction model for buried pipelines in melt settlement areas based on the support vector regression algorithm and gave working condition parameters to enhance the resistance of buried pipelines to deformation [6]. To sum up, the research focus of the above scholars in the field of pipeline engineering mainly focuses on the influencing factors of pipeline stress and strain and pipe-soil interaction. However, there is a relative lack of relevant research on the intelligent prediction of the non-uniform settlement of pipelines. In recent years, emerging neural network prediction methods have been widely proposed and used to reveal the inherent laws behind data due to their excellent autonomous learning and data analysis capabilities, especially in time series prediction. At present, some scholars have begun to apply neural network technology to the prediction of ground subsidence caused by engineering construction. [PERSON] used neural networks to establish a prediction model for ground subsidence caused by tunnel excavation [7]. Research results show that RNN (Recurrent Neural Network) exhibits higher stability and accuracy. [PERSON] used LSTM (long short-term memory network) in their research to predict the maximum surface settlement and longitudinal settlement curve caused by engineering construction [8]. Their research results showed that LSTM has excellent stability and can be applied to different types of projects.