# Main Factors on Effect of Precise Measurement and Precise Tamping Based on BP Neural Network
## 1 Introduction
The stabilizing operation is used to simulate the stress state of the track under the action of the train, so as to achieve the purpose of re-compacting the ballast and avoiding uneven settlement of the track [15]. On-site maintenance personnel arrange the tamping machines and dynamic stabilizers appropriately to form different tamping modes for track maintenance operations. Figure 1: (a) INS trolley with TS configuration; (b) INS trolley with GNSS configuration. The operation effect of different PMPT modes is affected by multiple factors. The degree of track geometry improvement depends on the track state before maintenance [16; 17]. Different tamping operation modes have different effects on track geometry and track bed mechanical properties. Stabilizing operation will improve the track stability and extend the track quality warranty period [15]. [PERSON] summed up the main factors affecting the tamping effect, which include the initial state of the tamping track, the measurement method, the tamping mode, the stabilizing operation, the models of the tamping machine, and the amount of lifting [1]. The condition of the track before the tamping operation affects the outcome of the tamping operation [18; 19]. At present, there is no quantitative analysis method to determine the weight of the factors affecting the effect on the PMPT. The influence of different factors can only be qualitatively evaluated through field operation experience. We must use an optimal calculation method to quantify the influence weights of different factors and diagnosis the main factors affecting the effect on the PMPT. Scientifically guiding on-site construction operations and effectively improving the quality of operations are the pressing issues to be solved in the PMPT of ballast railways in China. With the popularization and application of track inspection vehicles in China, a large amount of multi-source track inspection data has been accumulated, providing an important basis for track status evaluation. Neural networks can handle and analyze a huge amount of data, and even extract more data information from nonlinear data [20]. Machine learning technology has been widely applied in different fields, such as track status assessment [21], track geometry quality prediction [22; 23], and railway fault diagnosis. In this study, the BPNN is selected because it is able to easily explore the importance of input features on output parameter. Therefore, having collected and sorted out the national-wide ballasted track PMPT maintenance statistics, the track inspection vehicle provides mega track geometry inspection data for the operation section. Based on the mega multi-source heterogeneous data and track geometry detection data, the BPNN is used to establish the main factor model that affects the PMPT operation effect. The influence weight of different factors on the PMPT effect is quantized for the first time, which is used to scientifically guide the control of key factors in the field operations. Some practical suggestions for the on-site application and operation mode optimization of the PMPT of the normal speed railway tracks are proposed, which provide a basis for reducing costs and increasing efficiency for heavy-duty track maintenance machinery operations, which has important practical significance and great economic benefits.