A Closed-Loop Scheduling Framework for Prefabricated Bridge Girders: Bayesian Regression and TCTO-Based Optimization

Prefabricated construction has emerged as a key strategy to enhance productivity and quality in infrastructure projects. Yet, construction scheduling for prefabricated infrastructure projects often suffers from persistent discrepancies between planned and actual performance due to static assumptions of task durations and fragmented management methods. To address this challenge, this study proposes a closed-loop framework that integrates probabilistic estimation, prescriptive planning, and performance feedback for prefabricated girder bridge construction. Standard task time (ST) is dynamically modeled using Bayesian regression, which incorporates prior knowledge and updates continuously with new field data. The updated ST distributions are embedded into a time–cost trade-off (TCTO) optimization algorithm to generate resource-constrained schedules. Execution data are captured through an object-based digital logging system, and performance is evaluated using the Schedule Performance Index (SPI). The accumulated results are then used to update the Bayesian model, creating a self-correcting cycle of plan → execution → performance → updating. Using eleven prefabricated girder projects, we standardized task definitions and quantified the plan and actual gaps that motivate the framework. Six projects formed the training set for Bayesian regression to estimate ST with priors; four projects were scheduled with TCTO using the posterior ST, and execution outcomes were compared with the generated plans to validate accuracy, while the collected evidence was used to update the Bayesian model; one final project received the full closed-loop application for comparative assessment of plan versus outcome, with SPI used in the closed-loop evaluation. The deployments improved alignment between plan and actual, narrowed uncertainty in ST over time, and supported credible schedules, real time progress visibility, and resource efficient planning in repetitive prefabrication. From a managerial perspective, the implemented system operationalizes feedback between planning and execution with configurable update cadences such as daily logs, repetitive unit cycles, and project close out. This study provides a validated and extensible template for closed-loop schedule management in prefabricated settings and clarifies the novelty of unifying Bayesian estimation, TCTO optimization, and digital performance feedback in one practical workflow.