## 1 Introduction
[PERSON] et al. [12] estimated the parameters of the GM(1,1) model from the perspective of linear programming, and it was verified that it could better solve the problem of unstable data and increase the stability of the linear prediction of bridges. In recent years, with the development of science and technology, artificial intelligence technology, represented by artificial neural networks, has been gradually applied to the field of bridge construction; the BP neural network [13] is widely used in bridge construction monitoring and prediction due to its strong nonlinear fitting ability and adaptive ability. [PERSON] et al. [14] used a BP neural network model to identify the key parameters of the Jialing River Bridge and predicted and adjusted its subsequent sections based on the elevation data available under its current construction status, which ensured smooth construction and accuracy control. [PERSON] et al. [15] established a BP neural network prediction model based on particle swarm optimisation, based on which the correction value of the elevation of the standing mould was predicted and a more ideal bridge formation was obtained, which provided a reference for the linear control of the main girders of cable-stayed bridges. [PERSON] [16] established a BP neural network prediction model to predict the deflection of the main girder using the monitoring data of a bridge's construction, and the results show that the predicted value is close to the measured value, meeting the requirements of the construction control of cable-stayed bridges. [PERSON] [17] proposed a bi-level control method for main girder alignment based on parameter identification and alignment deviation prediction for the alignment control of a continuous girder-arch combination bridge construction on the Taipu River, and the results showed that this method is closer to the actual value than the traditional orthotropic inverted dismantling method. [PERSON] et al. [18] combined both a BP neural network and genetic algorithm to predict and control the construction process of the Danjiangkou SecondBridge, which successfully identified the elevation errors caused by the loss of concrete capacity, the modulus of elasticity, and prestress. [PERSON] et al. [19] used the grey correlation method and principal component analysis, a golden ratio value-check method, to achieve the optimisation of the input layer and the network layer, therefore improving the accuracy and efficiency of the neural network's prediction, which can be better applied to construction monitoring. [PERSON] et al. [20] proposed a BP neural network agent model (MEC-BP) based on the thinking evolutionary algorithm (MEC) to predict the line shape of the Liangqugou Bridge in Shaanxi Province; the predicted values are in good agreement with the measured values, and it has a stronger generalisation ability than the traditional BP neural network model. In summary, a large number of studies have been carried out by scholars at home and abroad for the prediction and analysis of bridges' linear shape. However, most of the proposed bridge alignment prediction methods above are based on a single prediction model or an improved model, which does not take into account the limitations of a single prediction model, thus affecting the prediction accuracy of the algorithm. In this paper, based on a full consideration of the grey prediction model and the BP neural network model, the advantages of the two prediction methods are organically combined to establish the GM-BP combined prediction model. This method is then applied to the linear prediction of a large-span continuous bridge, allowing for the verification of the model's predictive capabilities and the assurance of the safety of bridge construction.