What Changes when a Concrete Mixture Is Judged Acceptable
Abstract
Selecting an acceptable material is a different problem from maximizing predicted strength. A Logit reanalysis of public concrete records defines an illustrative 35 MPa threshold and examines how acceptance is associated with water-to-binder ratio, curing age, total binder, and superplasticizer. Thresholds of 30 and 40 MPa provide sensitivity comparisons. The analysis demonstrates how changing a decision criterion changes the modeled outcome, without treating any chosen threshold as an engineering certification standard. A synthesis of good-enough selection, opportunity cost, constraints, and adaptive computation explains why a decision analysis must identify tolerance, consequences, and information needs before pursuing a uniquely best alternative.
References
F. Gao and S. Gao, “A New Strategy for Selecting Good Enough Designs Using Optimal Computing Budget Allocation,” Journal of Simulation, vol. 12, no. 3, pp. 238–247, 2018.
S. Gao, W. Chen, and L. Shi, “A New Budget Allocation Framework for the Expected Opportunity Cost,” Operations Research, vol. 65, no. 3, pp. 787–803, 2017.
Yeh, I-Cheng. Concrete Compressive Strength. UCI Machine Learning Repository, 1998. Dataset. https://doi.org/10.24432/C5PK67.
White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. “Stochastic Kriging for Simulation Metamodeling.” Operations Research, vol. 58, no. 2, 2010, pp. 371–382. https://doi.org/10.1287/opre.1090.0754.
Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
Sacks, Jerome, William J. Welch, Toby J. Mitchell, and Henry P. Wynn. “Design and Analysis of Computer Experiments.” Statistical Science, vol. 4, no. 4, 1989, pp. 409–423. https://doi.org/10.1214/ss/1177012413.
Kim, Seong-Hee, and Barry L. Nelson. “A Fully Sequential Procedure for Indifference-Zone Selection in Simulation.” ACM Transactions on Modeling and Computer Simulation, vol. 11, no. 3, 2001, pp. 251–273. https://doi.org/10.1145/502109.502111.
Frazier, Peter I., Warren B. Powell, and Savas Dayanik. “A Knowledge-Gradient Policy for Sequential Information Collection.” SIAM Journal on Control and Optimization, vol. 47, no. 5, 2008, pp. 2410–2439. https://doi.org/10.1137/070693424.
Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.