End-to-End Survey Productivity in Engineering Construction: Modeling Time-to-Decision Under Accuracy Constraints

Abstract

This article develops an end-to-end productivity model that links decision latency to accuracy constraints and construction tolerances. The proposed framework defines time-to-decision as a stochastic process combining (i) base workflow time (setup, acquisition, QC, processing, integration, and review) and (ii) expected iteration time arising from tolerance exceedance risk, verification failure, and quality assurance requirements. Using a generic, case-based set of construction survey tasks (layout of tolerance-critical points, corridor alignment, grade control, progress mapping, and earthwork quantity reporting), the study evaluates three primary technologies (Total Station, GNSS RTK, and UAV photogrammetry) and hybrid workflows. Accuracy is represented using uncertainty budgets and probability of nonconformance, while productivity is expressed as expected time-to-decision and decision throughput under deadlines. Results demonstrate that (1) the fastest field method does not necessarily yield the fastest decision, because downstream processing and verification can dominate; (2) strict tolerances amplify iteration costs, making high-defensibility measurements decision-optimal even when field time is longer; (3) control quality and coordinate integrity can dominate decision latency by increasing rechecks and reconciliation; and (4) hybrid strategies that allocate instruments by tolerance class minimize expected decision time while preserving acceptance reliability. The findings provide actionable guidance for planning survey workflows as decision pipelines rather than isolated measurement activities, supporting evidence-based scheduling and quality management in engineering construction.