Public Building Cost Prediction Model In Oromia Region Using Artificial Neural Network

Abstract

The Construction Industry Is A Critical Component Of Infrastructure Development, And The Ability To Accurately And Reliably Estimate Project Costs Is Essential For The Successful Execution Of Public Projects. This Research Delves Into The Application Of Artificial Neural Networks (Ann) As An Innovative Approach To Cost Estimation For Public Projects Within The Oromia Region. The Study Aims To Address The Limitations Of Traditional Cost Estimation Methods By Leveraging The Adaptability And Pattern Recognition Capabilities Inherent In Ann Technology. The Comprehensive Methodology Employed In This Research Includes AnIn-Depth Literature Review, Surveys And Interviews With Industry Professionals, And The Collection Of Historical Project Data From Public Records, Government Agencies, And Construction Firms Located Within The Oromia Region. The Collected Data Undergoes Rigorous Preprocessing Before Being Used To Develop, Train, And Validate The Ann Model. The Performance Of The Ann Model Is Then Meticulously Evaluated Using Well-Established Metrics Such As Mean Absolute Error (Mae), Root Mean Square Error (Rmse), And R-Squared (R??), With The Results Thoroughly Analyzed To Assess The Efficacy Of The Ann Approach. Furthermore, The Study Explores The Various Factors That Influence Construction Cost Estimation And Provides Practical, Implementation-Focused Recommendations For Integrating Ann-Based Cost Estimation Within The Context Of Public Projects. The Findings Of This Research Contribute Significantly To The Advancement Of Construction Cost Estimation Practices, Offering Valuable Insights Into The Potential Of Ann For Enhancing Accuracy And Reliability In The Specific Domain Of Public Projects Within The Oromia Region. The Outcomes Of This Study Are Expected To Benefit All Parties Involved In Construction Projects, Including Owners, Contractors, Consultants, And Others, By Enabling Them To Obtain Cost Estimates At The Early Stages Of Projects With Limited Available Information, While Maintaining High Accuracy And Adhering To Feasible Timelines. Additionally, The Study Aims To Guide Industry Professionals And Researchers In The Adoption Of Innovative Technologies, Such As Ann, To Achieve More Effective And Reliable Cost Estimation In Construction Projects.