Comparative Study on Performance of Various Neural Network Algorithms in Construction Project Cost Prediction

Making accurate predictions of the construction cost is essential for ensuring the smooth implementation of projects and guaranteeing economic benefits. The problem to be studied in this article is how to predict construction project costs accurately. The related factors affecting construction project costs are briefly introduced in this paper. A back-propagation neural network (BPNN) was proposed to predict construction engineering costs, and the AdaBoost algorithm was used to improve it. Then, simulation experiments were carried out. It was found that the Adaboost-BPNN algorithm converged to stability faster, and the mean square error was smaller (10-5) when it was stable. Compared with the support vector machine and traditional BPNN algorithms, the AdaBoost-BPNN algorithm had better goodness of fit (0.787) and provided more accurate prediction results for construction engineering costs (mean average error: 0.467, root-mean-square error: 1.118). The novelty of this article lies in utilizing AdaBoost to combine multiple weak predictors into a strong predictor, thereby enhancing the performance of the BPNN algorithm. The contribution lies in improving the predictive performance of the BPNN through the combination principle of AdaBoost, providing an effective reference for accurate cost prediction in construction engineering.