# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
###### Abstract  
This study proposes a novel hybrid model, VMD-SSA-LSTM, aimed at enhancing the accuracy of construction material price (CMP) predictions. The model integrates Variational Mode Decomposition (VMD) for signal decomposition, the Sparrow Search Algorithm (SSA) for parameter optimization, and Long Short-Term Memory (LSTM) networks for predictive modeling. Historical CMP data are first decomposed into intrinsic components using VMD, followed by the SSA-based optimization of the LSTM parameters. These components are then input into the LSTM network for final predictions, which are aggregated to produce the CMP forecast. Experimental results using rebar price data from Hengyang City demonstrate that the VMD-SSA-LSTM model outperforms the backpropagation (BP) neural network, LSTM, and VMD-LSTM models in terms of prediction accuracy. The proposed method provides highly valuable tools for construction cost management, significantly enhancing the reliability of budget planning and risk mitigation decisions, and has significant practical implications for engineering cost risk management. L +
Footnote †: journal: Applied sciences  
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Enhancing risk management capabilities and minimizing schedule delays and contractual disputes: fluctuations in material prices frequently lead to project schedule adjustments and contractual conflicts, which can be mitigated through accurate price forecasting. * Strengthening corporate competitiveness: the ability to accurately predict material prices constitutes a strategic advantage, enabling firms to submit more competitive bids in tendering processes. Construction costs are a primary concern for project managers due to significant price volatility and frequent design changes during the construction process. In the face of such uncertainty, reliable cost forecasts serve as crucial decision-making tools for all stakeholders [3]. By employing advanced scientific methods for material price forecasting, companies can better control costs, gain a competitive edge, and ultimately enhance overall project management efficiency [4]. Material price fluctuations are a major source of deviation from initial cost estimates. These fluctuations are driven by factors including raw material prices, energy costs, and market conditions, making predictions challenging due to the unpredictable nature of these influences [5; 6; 7]. Reliable CMP predictions empower companies to anticipate market trends, reduce investment risks, and allocate resources more efficiently. This not only strengthens economic stability but also enhances the operational efficiency of construction projects. For contractors, a thorough understanding of market conditions and the accurate forecasting of future CMP trends are indispensable for making well-informed decisions during the bidding and planning phases. Such informed decision-making directly affects key aspects of project management, including scheduling, quality control, and cost management [8]. Therefore, having a reliable and accurate method for predicting material prices in the early stages of construction is crucial. This research aims to propose a novel approach to material price prediction. As previously mentioned, material costs significantly impact the overall project budget. Inaccurate predictions of material prices can lead to cost overruns. Therefore, it is essential to develop a reliable and precise method for forecasting material costs. Several studies have focused on predicting the Construction Cost Index [9; 10; 11] (CCI), which includes all cost components such as material, labor, and machinery costs. However, to improve accuracy, it is more effective to analyze material costs separately. The following section reviews previous research on predicting material prices using various methods. These studies commonly employed regression analysis [12], time series methods, machine learning, or combinations of these techniques [13].