• The International Concrete Abstracts Portal is an ACI led collaboration with leading technical organizations from within the international concrete industry and offers the most comprehensive collection of published concrete abstracts.

International Concrete Abstracts Portal


Title: Statistical Process Control of Fiber-Reinforced Concrete Precast Tunnel Segments

Author(s): C. Pleesudjai, D. Patel, K. A. Williams Gaona, M. Bakhshi, V. Nasri, and B. Mobasher

Publication: Materials Journal

Volume: 121

Issue: 2

Appears on pages(s): 127-140

Keywords: Bootstrap Method; fiber-reinforced concrete; flexure; lining; precast; quality control; statistical process control; time-series control process; tunnel segment

DOI: 10.14359/51740373

Date: 4/1/2024

Statistical process control (SPC) procedures are proposed to improve the production efficiency of precast concrete tunnel segments. Quality control test results of more than 1000 ASTM C1609/C1609M beam specimens were analyzed. These specimens were collected over 18 months from the fiber-reinforced concrete (FRC) used for the production of precast tunnel segments of a major wastewater tunnel project in the Northeast United States. The Anderson-Darling (AD) test for the overall distribution indicated that the data are best described by a normal distribution. The initial residual strength parameter for the FRC mixture, f D 600, is the most representative parameter of the post-crack region. The lower 95% confidence interval (CI) values for 28-day flexural strength parameters of f1, f D 600, and f D 300 exceeded the design strengths and hence validated the strength acceptability criteria set at 3.7 MPa (540 psi). A combination of run chart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) control charts successfully identified the out-of-control mean values of flexural strengths. These methods identify the periods corresponding to incapable manufacturing processes that should be investigated to move the processes back into control. This approach successfully identified the capable or incapable processes. The study also included the Bootstrap Method to analyze standard error in the test data and its reliability to determine the sample size.