Smart Construction Costing with AI-Driven BIM Analytics and Forecasting

Thesis Abstract

This study addresses the escalating cost uncertainty in construction projects by integrating AI-driven BIM analytics to enable dynamic costing and forecasting throughout the project lifecycle. Despite advances in building information modeling and machine learning, there remains a gap in operationalizing AI-augmented cost estimation within BIM environments to deliver real-time, scenario-based financial insights for designers, contractors, and clients. The aim is to develop a robust, data-driven costing framework that leverages AI, BIM data, and historical project records to produce accurate cost forecasts, risk-adjusted budgets, and proactive cost control mechanisms. Specific objectives include (i) to design an AI-enhanced BIM analytics pipeline capable of extracting cost-relevant features from parametric models and historical datasets; (ii) to develop forecasting models that generate probabilistic cost estimates and confidence intervals for preliminary, design development, and construction phases; (iii) to evaluate the impact of incorporating external variables (market dynamics, supply-chain disruptions, and inflation) on forecast accuracy; (iv) to validate the framework through multiple case studies across residential, commercial, and infrastructure projects; and (v) to formulate a governance and implementation blueprint for industry adoption. The methodology adopts a mixed-methods approach underpinned by the technology acceptance and contingent valuation theories to capture both quantitative performance and stakeholder perceptions. The research design comprises three phases (1) data acquisition and preprocessing, collecting BIM models, historical cost data, supplier quotes, and market indices from a sample of 60 completed projects over the past five years; (2) model development and validation, where AI-driven modules—including gradient boosting, recurrent neural networks, and Bayesian networks—are trained on a dataset of approximately 40 cases with 80% used for training and 20% for validation; and (3) deployment simulation and sensitivity analysis, applying Monte Carlo simulations to produce probabilistic cost envelopes under varying market conditions. Data collection instruments include BIM extraction tools, standardized cost-item catalogs, project documentation archives, and structured interviews with cost managers to capture qualitative factors. Instrument validity and reliability are ensured through pilot testing with 5 projects, triangulation of BIM-derived data with procurement records, and inter-rater reliability checks (Cohen’s kappa) for qualitative coding. The analysis employs a combination of descriptive statistics, time-series forecasting (LSTM and Prophet variants), regression-based cost drivers analysis, and probabilistic risk assessment via Monte Carlo simulation. Model specification includes an AI-enabled cost predictor with inputs such as material quantities, unit rates, labor productivity indices, logistics lead times, and contingency allocations, along with external variables like exchange rates and commodity price indices. Performance metrics include MAE, RMSE, and PICP/POICI for probabilistic forecasts. A thematic analysis of stakeholder interviews complements the quantitative evidence, informing practical constraints and governance considerations. The expected findings indicate that AI-augmented BIM costing reduces mean forecast error by 15–25% relative to conventional methods, enhances early-phase confidence intervals, and improves cost variance management through proactive alerting and scenario planning. The study contributes to knowledge by integrating AI-enabled BIM analytics into cost estimation theory, extending it with probabilistic forecasting and real-time data fusion, and offering a replicable framework for industry practitioners. It demonstrates how BIM-derived geometries, quantities, and linked procurement data can be transformed into actionable financial insights, incorporating uncertainty through probabilistic methods and risk-adjusted budgeting. The main conclusion anticipates that the proposed framework will increase budget fidelity, reduce change orders, and support more informed decision-making for clients and project teams. Recommendations include adopting standardized BIM cost data schemas, investing in data governance and data quality control, and developing organizational capabilities for AI-enabled cost management. The study also suggests extending the framework to incorporate lifecycle costing and sustainability metrics, as well as exploring integration with emerging contract models (e.g., target cost and pain/gain share) to further align financial performance with project outcomes.