Optimization of Elman Neural Network Using Genetic Algorithm for Construction Cost Estimation and Overspending Risk Analysis
Abstract
This study proposes a model based on the Elman neural network and improves it using a Genetic Algorithm (GA) to increase the accuracy of construction cost estimation and accurately analyze the overspending risk. First, an index system containing multiple dimensions such as building features, structural features, project positioning, and project environment is constructed to comprehensively capture the key factors affecting construction cost and overspending risk. Second, the Elman neural network’s structure and operation are thoroughly examined, and the GA optimizes the network’s weights and thresholds to improve the model’s predictive power. On the training set, the optimized GA-Elman model demonstrates great prediction accuracy, with relative error (RE) percentages between predicted and true values typically falling within ±1%. On the test set, the GA-Elman model performs better than the original Elman model in both difference and RE, with a Mean Absolute Percentage Error of 2.75%, a decrease of 18.4% compared to the Elman model. These results indicate that the GA-Elman model is more accurate in cost prediction and more effective in identifying potential overspending risks. This study provides a powerful tool for cost control and budget management in the construction industry and a new perspective on the application of neural networks in construction economics
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Sayed M, Abdel-Hamid M, El-Dash K. Improving cost estimation in construction projects. International Journal of Construction Management, 2023, 23(1): 135-143.
Haaskjold H, Andersen B, Langlo J A. Dissecting the project anatomy: Understanding the cost of managing construction projects. Production Planning & Control, 2023, 34(2): 117-138.
Sihombing D J C. Development of construction project cost budget application using rapid application development method. Jurnal Mantik, 2023, 7(3): 1685-1696.
GadelHak Y, El-Azazy M, Shibl M F, Mahmoud, R. K. Cost estimation of synthesis and utilization of nano-adsorbents on the laboratory and industrial scales: A detailed review. Science of the Total Environment, 2023, 875: 162629.
Abdel-Hamid M, Abdelhaleem H M. Project cost control using five dimensions building information modelling. International Journal of Construction Management, 2023, 23(3): 405-409.
Durstewitz D, Koppe G, Thurm M I. Reconstructing computational system dynamics from neural data with recurrent neural networks. Nature Reviews Neuroscience, 2023, 24(11): 693-710.
Qin Z, Yang S, Zhong Y. Hierarchically gated recurrent neural network for sequence modeling. Advances in Neural Information Processing Systems, 2024, 36.
Das S, Tariq A, Santos T, Kantareddy S S, Banerjee I. Recurrent neural networks (RNNs): architectures, training tricks, and introduction to influential research. Machine Learning for Brain Disorders, 2023: 117-138.
Hananto A L, Fauzi A, Suhara A, Davison I, Spraggon M, Herawan S G, et al. Elman and cascade neural networks with conjugate gradient polak-ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel. Results in Engineering, 2023, 19: 101334.
Mahmoodzadeh A, Mohammadi M, Daraei A, Farid Hama Ali H, Ismail Abdullah A, Kameran Al-Salihi, N. Forecasting tunnel geology, construction time and costs using machine learning methods. Neural Computing and Applications, 2021, 33: 321-348.
Alshboul O, Shehadeh A, Almasabha G, Almuflih A S. Extreme gradient boosting-based machine learning approach for green building cost prediction. Sustainability, 2022, 14(11): 6651.
Sayed M, Abdel-Hamid M, El-Dash K. Improving cost estimation in construction projects. International Journal of Construction Management, 2023, 23(1): 135-143.
Pham T Q D, Le-Hong T, Tran X V. Efficient estimation and optimization of building costs using machine learning. International Journal of Construction Management, 2023, 23(5): 909-921.
Goodarzizad P, Mohammadi Golafshani E, Arashpour M. Predicting the construction labour productivity using artificial neural network and grasshopper optimisation algorithm. International Journal of Construction Management, 2023, 23(5): 763-779.
Kim S, Choi C Y, Shahandashti M, Ryu K R. Improving accuracy in predicting city-level construction cost indices by combining linear ARIMA and nonlinear ANNs. Journal of Management in Engineering, 2022, 38(2): 04021093.
Ye M, Wang J, Si X, Zhao S, Huang Q. Analysis on dynamic evolution of the cost risk of prefabricated building based on DBN. Sustainability, 2022, 14(3): 1864.
Zhang J, Wei W, Chen L, Jiang Z. Evaluation of hidden cost risk of prefabricated construction based on SEM-MEECM. Applied Sciences, 2023, 13(13): 7902.
Cao P, Lei X. Evaluating risk in prefabricated building construction under EPC contracting using structural equation modeling: a case study of Shaanxi Province, China. Buildings, 2023, 13(6): 1465.
Kumar R. Memory recurrent Elman neural network-based identification of time-delayed nonlinear dynamical system. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 53(2): 753-762.
Miranda M H R, Silva F L, Lourenço M A M, Eckert J J, Silva L C. Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation. Energy, 2023, 285: 129503.
Kumar Chandar S. Grey Wolf optimization-Elman neural network model for stock price prediction. Soft Computing, 2021, 25: 649-658.
Zhang Y, Zhao J, Wang L, Wu H, Zhou R, Yu J. An improved OIF Elman neural network based on CSO algorithm and its applications. Computer Communications, 2021, 171: 148-156.
Sohail A. Genetic algorithms in the fields of artificial intelligence and data sciences. Annals of Data Science, 2023, 10(4): 1007-1018.
Guo W, Meng Q, Wang X, Zhang Z, Yang K, Wang C. Landslide displacement prediction based on variational mode decomposition and GA-Elman model. Applied Sciences, 2022, 13(1): 450.
Wang C, Zhao Y, Bai L, Guo W, Meng Q. Landslide displacement prediction method based on GA-Elman model. Applied sciences, 2021, 11(22): 11030.
DOI: https://doi.org/10.31449/inf.v49i12.7592
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