The digital transformation of construction processes has highlighted the need for integrated and sustainable automation frameworks, particularly in public-sector infrastructure planning where cost estimation, documentation, and approval workflows remain fragmented. This study proposes OnlinePlan, a computational and system-level framework that operationalizes a regulation-compliant cost estimation process within an integrated digital platform. The framework integrates heterogeneous data sources, category-specific engineering models, and regulatory transformations into a structured workflow that combines the Standard Construction Cost Estimation System, the Construction Planning and Budget Documentation System, and the Highway Maintenance Budget Planning Information System, with interoperability to PlanNET. A real-world dataset of 74 projects is used to evaluate system performance against traditional workflows. The results demonstrate zero computational deviation (0.00%) and significant efficiency improvements, with total processing time reduced by approximately 75.7%. Statistical validation confirms strong significance (t = 35.09, p < 0.001) and an exceptionally large effect size (Cohen’s d = 7.85), indicating substantial practical impact. The findings reveal that the primary contribution of construction automation lies not only in computational acceleration but in the integration of estimation, documentation, and approval processes into a workflow-governed digital system. This study contributes a scalable and interpretable framework for sustainable construction automation, advancing ICT-enabled decision-making, resource efficiency, and institutional transparency in infrastructure management. These dimensions are explicitly interpreted as measurable indicators of sustainability in public-sector infrastructure management. The primary contribution lies in the integration of estimation, documentation, and approval workflows into a unified system, rather than in the formulation of new cost equations.
Malaikrisanachalee et al. (Fri,) studied this question.