Building Engineering Cost Prediction Based On Deep Learning: Model Construction and Real - Time Optimization

Effective project planning, risk mitigation, and stakeholder satisfaction in the construction business are greatly impacted by accurate cost projection. Overspending, setbacks, and ruined projects are all possible results of imprecise cost estimates. For this reason, it is critical to guarantee the viability and success of a project by increasing the precision of cost predictions. Construction project complexity, a myriad of cost variables, and uncertainty are the obstacles that building engineering cost prediction must overcome. Predictions made using traditional approaches are commonly inaccurate because they fail to fully account for the complex interplay between project factors and expenses. Advanced modelling techniques that can handle complicated data and changeable project contexts are necessary to overcome these obstacles. An approach based on deep learning called Deep CostNet for Building Engineering Technique (DCN-BET) Cost Prediction is presented in this research. Its purpose is to solve the problems associated with building engineering cost prediction. The approach uses deep neural networks to extract intricate patterns from massive amounts of project data collected over time. Improved prediction accuracy and real-time optimisation during project execution are made possible by DCN-BET, which captures the nonlinear correlations between project characteristics and costs. Risk assessment and management, cost forecasting for resource allocation, and project budget estimation and planning are among the few of the many construction industry uses for DCN-BET. The effectiveness of DCN-BET is assessed by conducting thorough simulation analyses in contrast to more conventional cost prediction approaches. Training and testing the model with real-world building engineering datasets allows us to evaluate its accuracy and efficacy in project cost prediction. The results show that DCN-BET has the capacity to support real-time optimisation and significantly improved the accuracy of cost predictions, which improved the overall success and efficiency of the project.