# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 6 Experimental Results and Analysis
### Comparative Experiments and Analyses  
Figure 10 compares the predicted and real value curves of the proposed model on the test set for the unilateral cost of construction projects. The predicted and real value curves fit well together, which shows that the model has good prediction performance for the unilateral cost. To verify the superiority of BiLSTM in capturing the interactions between data tensors, BiLSTM is compared with the traditional benchmark models BP, SVM, and LSTM. The prediction results and errors are shown in Figure 11. The prediction results demonstrate that BiLSTM has the best fit to the true values, and the overall error variation of BiLSTM is relatively smooth, as seen from the error curves. Figure 11: _Cont._  
Figure 10: Comparison of real and projected values. Table 7 shows the results of the comparison of each single model on the five evaluation indicators. For the construction cost data, the average errors of LSTM and BiLSTM with time series capturing capability are relatively small, and the MAE, RMSE, and MAPE of BiLSTM are reduced by 2.536, 3.107, and 0.142%, respectively, compared with those of LSTM, which verifies the superiority of the BiLSTM bi-directional processing mechanism in capturing the inter-temporal interactions of the data. To validate the effectiveness of the model components further, the model was compared with BiLSTM, AM-BiLSTM, HAM-BiLSTM, and PSO-HAM-BiLSTM. The prediction results and error comparisons are shown in Figure 12.  
As can be seen from the figure, although the prediction results of each model have trends similar to that of the true value curve, the error curves of each model have a large difference. Comparing the error curves of HAM-BiLSTM and AM-BiLSTM reveals that the number of errors greater than 20 decreases from 13 to 8, reflecting that HAM enhances the robustness of the model in terms of its ability to capture inter-feature interactions due to traditional AM. The HAM-BiLSTM with hyperparameters optimised by IPSO has a smoother trend of error curves and a smaller range of error fluctuations relative to the HAM-BiLSTM with hyperparameters optimised by the conventional PSO, reflecting the fact that IPSO can further improve the accuracy of the model. Table 8 shows the comparison results of the models on the five evaluation metrics, the mean percentage error of the five models is positive, avoiding the serious consequences of underestimating the cost, and all the other error metrics of HAM-BiLSTM after optimising the hyperparameters by IPSO are significantly reduced. In particular, the average absolute error of HAM-BiLSTM is reduced by 6.711 and 4.252%, the RMSE is reduced by 7.757 and 3.670%, and the average absolute percentage error is reduced by 0.325 and 0.237%, respectively. This further demonstrates the effectiveness of HAM in capturing the interactions between the features and enhancing the feature extraction capability of the model. The average absolute error of HAM-BiLSTM after IPSO optimisation of hyperparameters is reduced by 8.560 and 6.236%, the RMSE is reduced by 10.436 and 7.658%, and the average absolute percentage error is reduced by 0.463 and 0.324%, in comparison with those of HAM-BiLSTM and AM-BiLSTM after PSO of the hyperparameters, indicating that the improved PSO algorithm has a better search capability, which can better overcome the problem of falling into local optimal solutions and then find a more comprehensive search of the hyperparameter space to find a better combination. Thus, the improved particle swarm optimisation algorithm has stronger search capability and can better overcome the problem of falling into local optima, thus searching the hyperparameter space more comprehensively and finding better hyperparameter combinations. Figure 12: Prediction results and errors for combined model comparisons.