Neural network-econometric entropy-based model for residential building project procurement cost adjudication system.

NEURAL NETWORK-ECONOMETRIC ENTROPY-BASED MODEL FOR RESIDENTIAL BUILDING PROJECT PROCUREMENT COST ADJUDICATION SYSTEM.

Abstract:

Effective cost adjudication plays a pivotal role in the success of residential building project procurement, as it ensures that projects are executed within budget constraints while maintaining high quality standards. Traditional cost adjudication methods often lack the capacity to incorporate and assess multifaceted project variables adequately. This research project proposes a novel approach, merging neural networks and econometric entropy analysis, to enhance the accuracy and efficiency of project cost adjudication in the residential construction sector.

The primary objective of this research is to develop an innovative, data-driven model capable of predicting and adjudicating project costs in the residential building domain. To achieve this, we will leverage the power of artificial neural networks (ANNs) for their ability to handle complex data relationships and econometric entropy analysis for measuring and managing uncertainty and risk within project cost estimations. The amalgamation of these two methodologies will create a hybrid model that can adapt to the dynamic nature of construction projects and provide more accurate and reliable cost adjudication predictions.

The research methodology encompasses several phases, including data collection from past residential building projects, feature engineering to capture relevant variables, and the construction of a supervised learning framework for the ANN. Additionally, we will implement econometric entropy-based analysis techniques to measure the uncertainty and risk associated with cost estimations, thereby enhancing the model’s ability to provide cost adjudication recommendations that account for potential deviations from the initial estimates.

Key expected outcomes of this research include:

1. A comprehensive dataset of residential building project information that will be used for training and testing the model.
2. The development of a hybrid neural network-econometric entropy-based model for project cost adjudication.
3. An evaluation of the model’s performance through extensive cross-validation and comparative analyses with traditional cost adjudication methods.
4. Insights into the model’s ability to adapt to different project scenarios, improve cost adjudication accuracy, and effectively manage cost-related risks.

Ultimately, the proposed research project aims to contribute to the advancement of cost adjudication practices in residential building project procurement. By harnessing the capabilities of neural networks and econometric entropy-based analysis, this innovative model has the potential to revolutionize the decision-making processes in the construction industry, resulting in more efficient, cost-effective, and risk-aware project execution.

Keywords: Neural Network, Econometric Entropy, Project Cost Adjudication, Residential Building, Project Procurement, Risk Management, Construction Industry.

NEURAL NETWORK-ECONOMETRIC ENTROPY-BASED MODEL FOR RESIDENTIAL BUILDING PROJECT PROCUREMENT COST ADJUDICATION SYSTEM.  GET MORE BUILDING TECHNOLOGY PROJECT TOPICS AND MATERIALS

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