# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 1 Introduction  
The scale and complexity of construction work continues to rise in the context of the global construction boom. Engineering cost is one of the most critical factors affecting the success of engineering projects; in the bidding stage for construction projects, accurate cost prediction is the basis for assessing the feasibility of the project and selecting design options, which directly affects how reasonable the bidding price is and the probability of success in winning the bid. In turn, these factors affect the economic feasibility and overall quality of the project. Consequently, an indispensable need exists to provide an accurate method of predicting construction cost. In previous studies, linear models such as the autoregressive moving average (ARIMA) [1] model have been utilised due to their simple structure and robustness to data size and noise levels. However, the ARIMA model is unsuitable for capturing the nonlinearities of time series in engineering costs [2], which negatively affects prediction accuracy. To solve this problem, the support vector machine (SVM) [3; 4; 5], backpropagation (BP) neural network [6; 7; 8], and other machine learning models have been applied to cost prediction. Although such methods can effectively handle nonlinear problems, the SVM has limitations regarding data correlation processing and slow processing speed, and the BP neural network can quickly lose time series data and fall into local minimal values. With the continuous development of deep learning models, long short-term memory(LSTM) networks have achieved good prediction results in engineering cost prediction. [PERSON] et al. [9] compared an LSTM network model to an SVM model through construction cost prediction experiments. The results revealed that the LSTM model can handle high-dimensional feature vectors and the selective recording of historical information better than an SVM while also having advantages in terms of prediction accuracy and parameter adjustment. [PERSON] et al. [10] used LSTM to predict the construction cost of highway projects. The results showed that LSTM provides more accurate predictions in short, medium, and long-term prediction scenarios compared to the traditional model. However, LSTM still has shortcomings in dealing with the strong interactions between the features of the cost data. A bidirectional LSTM [11; 12; 13; 14] (BiLSTM) network is a variant of the LSTM network that has an additional layer providing an inverse structure that can mine more data. [PERSON] et al. [15] proved that the prediction accuracy of a BiLSTM network is higher than that of an LSTM network for time series problems, and suggested using BiLSTM instead of LSTM for issues related to time series analysis. A single model often fails to achieve optimal performance, and combined models can provide better performances and more accurate predictions by combining the strengths of multiple underlying models [16]. [PERSON] et al. [17] incorporated an attention mechanism (AM) into a BiLSTM model for feature weight adjustment, which further improved the prediction effect of the model. [PERSON] et al. [18] used a particle swarm optimisation (PSO) algorithm to optimise the hyperparameters of a BiLSTM model and reduce its prediction error. Implicit temporal patterns exist in construction cost data as factors such as materials, labor, and variable project duration. In addition, the data contain non-linear relationships and multi-level dependencies that allow for complex interactions between different cost characteristic indicators. However, existing studies have yet to consider these effects fully, so the constructed models are deficient in generalisation ability and prediction accuracy. Based on the above problems, this paper introduces the BiLSTM network into the field of engineering cost prediction. Grey correlation feature analysis (GRA) is performed to calculate the correlation between indicators and costs for feature indicator selection.