Automating Construction Valuation Models Using Advanced Statistical Decision-Making Optimization Tools

• Author(s): Oluwagbemisola Faith Akinlade ; Opeyemi Morenike Filani ; Priscilla Samuel Nwachukwu
• Paper ID: 1710778
• Page: 296-311
• Published Date: 30-11-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 5 November-2019

Abstract

Accurate construction valuation is a cornerstone of project success, underpinning budgeting, contractor payment, and financial planning. Traditional valuation approaches, often reliant on manual assessments and expert judgment, are prone to delays, inconsistencies, and subjectivity. With the increasing complexity of modern construction projects and the growing demand for efficiency, automation of valuation models through advanced statistical decision-making and optimization tools has emerged as a transformative solution. This examines the integration of statistical modeling, machine learning, and optimization techniques in automating valuation processes. Statistical methods such as regression analysis, Bayesian inference, and Monte Carlo simulations enable probabilistic valuation under uncertainty, while optimization tools, including linear programming, genetic algorithms, and multi-objective decision frameworks, provide systematic means of balancing cost, quality, and time. The incorporation of real-time data from Building Information Modeling (BIM), Internet of Things (IoT) sensors, and cloud-based platforms further enhances model adaptability and responsiveness. Applications extend to interim valuations, risk-adjusted cost forecasting, and value engineering, offering significant improvements in accuracy, transparency, and stakeholder confidence. Despite clear benefits, challenges persist, including data standardization, high implementation costs, and the need for workforce upskilling. Addressing these limitations requires industry-wide collaboration, robust data governance, and supportive regulatory frameworks. Future advancements, such as blockchain-enabled smart contracts and integration with digital twins, are expected to further enhance automation, enabling real-time valuation and autonomous project control. Overall, the adoption of advanced statistical decision-making and optimization tools in construction valuation represents a paradigm shift toward data-driven, efficient, and resilient project management, providing the foundation for sustainable growth in the construction industry.

Keywords

Construction Valuation Automation, Advanced Statistical Modeling, Decision-Making Optimization, Predictive Analytics, Cost Estimation, Project Appraisal, Data-Driven Construction Management, Risk Assessment, Resource Allocation, Construction Project Valuation, Optimization Algorithms, Model-Based Forecasting, Performance Evaluation

Citations

IRE Journals:
Oluwagbemisola Faith Akinlade , Opeyemi Morenike Filani , Priscilla Samuel Nwachukwu "Automating Construction Valuation Models Using Advanced Statistical Decision-Making Optimization Tools" Iconic Research And Engineering Journals Volume 3 Issue 5 2019 Page 296-311

IEEE:
Oluwagbemisola Faith Akinlade , Opeyemi Morenike Filani , Priscilla Samuel Nwachukwu "Automating Construction Valuation Models Using Advanced Statistical Decision-Making Optimization Tools" Iconic Research And Engineering Journals, 3(5)