Geospatial Data Accuracy and Its Impact on Engineering Construction Performance

Abstract

This study develops a quantitative, engineering-oriented framework to analyze error propagation across common construction survey workflows and to estimate its impact on quality and performance. A set of generic, case-based scenarios is defined for typical construction tasks: foundation grid and anchor bolt layout, road centerline and grade, pipeline alignment, steel column positioning, and earthwork volume estimation. For each scenario, spatial uncertainty is modeled using covariance-based propagation and Monte Carlo simulation, separating random and systematic error components. Performance is evaluated using engineering-relevant metrics, including probability of tolerance exceedance, expected rework frequency, and error-induced uncertainty in quantities. Results show that (1) control network uncertainty and coordinate transformation bias often dominate final construction errors, even when high-precision instruments are used; (2) vertical uncertainty drives large impacts on earthwork quantities and grade compliance, with asymmetric consequences compared to horizontal error; (3) systematic components, including datum inconsistencies and biased control, produce coherent shifts that are difficult to detect without independent checks and can produce high-cost failures; and (4) hybrid measurement strategies can reduce risk when they explicitly allocate instruments to tasks based on tolerance class and error budget rather than instrument availability. The article concludes with a practical decision framework for accuracy management, emphasizing uncertainty documentation, redundancy, acceptance criteria linked to error budgets, and workflow-level quality assurance. The contributions are intended to support evidence-based construction surveying and to provide applied engineering guidance suitable for industrial practice.