# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 2 Methodology for the Selection of Characteristic Indicators  
Considering the specificity and diversity of construction projects, the factors affecting project costs are complex and diverse. However, not all factors have the same weight and importance regarding project costs [19]. Therefore, when selecting indicators, the principle of moderation should be considered to screen out indicators that can effectively describe project characteristics or have a significantly impactful project cost.  
GRA is a data analysis method based on grey system theory that aims to study the interrelatedness between multiple indicators [20]. The data associated with each indicator are summed to determine the relative degree of each indicator, and the grey correlation between indicators is calculated to determine the degree of influence of each indicator on a problem. Compared to the traditional correlation analysis method, GRA can more accurately reflect the correlations between indicators and their degree of influence. Additionally, GRA has the advantages of a simple model, a small data volume, and interpretable results, which are suitable for selecting construction cost indicators. The steps involved in the grey correlation calculations are as follows.  
1. Raw data standardisation: the values of indicators in the raw data are mapped to intervals that ensure the same scale and range of variation among different variables.
2. Absolute difference matrix construction: we take the absolute value of the difference between the target indicator and each candidate indicator to form an absolute difference matrix as: \[\Delta_{i}(k)=|x_{0}(k)-x_{i}(k)|\] (1) where \(x_{0}\) and \(x_{i}\) denote the standardised target and candidate indicators, respectively, \(k\) denotes the \(k\)th sample, and \(i\) denotes the \(i\)th candidate indicator.
3. Correlation matrix construction: a correlation matrix is derived by considering each element in the absolute difference matrix to calculate the minimum and maximum values of the absolute difference matrix as: \[\zeta_{i}(k)=\frac{\Delta_{\text{min}}+\rho\Delta_{\text{max}}}{\Delta_{i}(k)+ \rho\Delta_{\text{max}}}\] (2) where \(\Delta_{\text{min}}\) and \(\Delta_{\text{max}}\) denote the minimum and maximum values of the matrix, respectively, and \(\rho\) denotes the resolution factor, which was set to 0.5 in this study.
4. The grey correlation is calculated as: \[r_{0,i}=\frac{1}{n}\sum_{k=1}^{n}\zeta_{i}(k)\] (3)  
In this study, GRA was used to select characteristic indicators. By analysing the frequency of influencing factors and the difficulty of obtaining them in previous literature [21; 22; 23; 24; 25; 26], we selected 18 indicators that have significant impacts on construction cost as candidate indicators: foundation type, structural type, fortification intensity, management level, facade material, aboveground floor area, underground floor area, number of aboveground floors, number of underground floors, aboveground floor height, underground floor height, interior wall decoration, types of doors and windows, number of elevators, roof type, concrete prices, reinforcing steel prices, and duration. The correlations between the candidate indicators and the unilateral cost were calculated via GRA, and the characteristic indicators were selected according to the resulting correlation value.