Innovative Model Enhances Cost Prediction for Agricultural Water Conservancy Projects

News Summary

A new model combining Building Information Modeling (BIM) and neural networks has been developed to improve cost predictions for agricultural water conservancy projects. Utilizing the Sparrow Search Algorithm, the model has demonstrated a remarkable accuracy with a maximum relative error of only 2.99%. Real engineering data from Liaoning Province bolstered its effectiveness, achieving a Root Mean Square Error of 0.1358 and an R2 value of 0.9819. This innovative approach not only facilitates dynamic cost management but also supports sustainable development in agricultural systems.

Innovative Model Enhances Cost Prediction for Agricultural Water Conservancy Projects

Accurate cost prediction has become essential for making informed investment decisions in agricultural water conservancy projects. Given the complex conditions and uncertainties often associated with these projects, an innovative model integrating Building Information Modeling (BIM) with a neural network has emerged as a solution. This model optimizes cost predictions using a method known as the Sparrow Search Algorithm (SSA).

Development of the Cost Prediction Model

The new prediction model utilizes BIM technology to digitize and visualize engineering information, which plays a critical role in supporting accurate cost analyses. By employing a Prediction model based on the Grey BP Neural Network (PGNN), the initiative addresses the challenges posed by complex, nonlinear dynamics that can lead to cost overruns in construction projects.

Data-Driven Approach

Real engineering data and material price data collected from January 2016 to February 2021 in Liaoning Province provided the foundation for this research. The results indicate a remarkable maximum relative error of only 2.99% between the predicted and actual construction costs, showcasing the model’s effectiveness.

Performance Indicators and Improvements

The proposed model achieved key performance indicators including a Root Mean Square Error (RMSE) of 0.1358 and an R2 value of 0.9819. These metrics illustrate the model’s high prediction accuracy and beneficial functionality. Notably, the new model has accomplished a 33% reduction in RMSE and a 6% increase in R2 compared with traditional PGNN models, reinforcing its superiority.

Integration of Technology in Cost Prediction

Technological integration through machine learning is a key feature of this model, enabling better feature extraction from high-dimensional datasets. This advancement allows for more reliable cost estimates, overcoming the limitations found in traditional methods of cost prediction. In doing so, the model holds promise for enhancing cost management across various phases of agricultural water conservancy projects.

Validation and Case Study

The constructed prediction model was validated through a specific agricultural water conservancy project located in Yanghe Town, Anshan City. This case study evaluated numerous key structures, including different types of bridges, culverts, and masonry walls, while concentrating on essential materials such as steel and concrete.

Enhanced Lifecycle Management

One of the significant applications of this innovative model lies in its ability to generate dynamic cost baselines and facilitate real-time adjustments for resource allocation. By acting as a decision-making tool, the model optimizes lifecycle management of agricultural water conservancy projects, enhancing not only project planning and execution but also promoting sustainable development within the sector.

Conclusion

In summary, this innovative integration of BIM technology and advanced neural network techniques presents a groundbreaking approach to predicting construction costs for agricultural water conservancy projects. By addressing inherent complexities and uncertainties, this model serves as a pivotal tool for optimizing costs and ensuring successful project outcomes, ultimately contributing to the sustainability of agricultural systems.

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Additional Resources

• Nature: Innovative Model Enhances Cost Prediction for Agricultural Water Conservancy Projects
• Wikipedia: Building Information Modeling
• ResearchGate: Cost Prediction Models in Construction
• Google Search: Neural Network Cost Prediction
• ScienceDirect: Cost Estimation in Engineering
• Encyclopedia Britannica: Machine Learning