Optimized Hybrid Deep Learning for Cost Estimation of Construction Projects Considering the Time-Dependent Characteristics of Economic Variables and Indices
Abstract
The SOS-NN-LSTM hybrid model emerges as a transformative solution to the perpetual challenge of accurate cost estimation in the construction industry. By integrating Neural Networks (NN) and Long Short-Term Memory networks (LSTM), optimized through the Symbiotic Organism Search (SOS) algorithm, the model distinguishes itself with remarkable performance metrics. Achieving an R-squared value of 0.9631 and a minimal Root Mean Squared Error (RMSE) of 0.023, SOS-NN-LSTM showcases its efficacy in handling project physical, financial, and time-dependent economic variables. The model's superiority is further emphasized by a minimal Mean Absolute Error (MAE) of 0.0168 and a notable Relative Improvement (RI) value of 0.96018. Importantly, the Mean Absolute Percentage Error (MAPE) experiences a significant decrease from 20% to 10%, highlighting the model's enhanced precision in cost estimation. SOS-NN-LSTM not only outperforms other AI models but also demonstrates its adaptability to dynamic economic conditions, effectively addressing the volatility of economic variables in construction projects. These exceptional results position SOS-NN-LSTM as a pioneering advancement in cost estimation methodologies for the construction industry. The model's nuanced handling of complex datasets and its ability to provide accurate projections underscore its potential to revolutionize project budgeting, mitigate risks, and contribute to the overall success and financial viability of construction projects. The integration of deep learning with SOS optimization represents a paradigm shift, offering a reliable and effective tool for project stakeholders in navigating the intricacies of construction cost estimation.
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