Genetic programming for experimental big data mining: A case study on concrete creep formulation
- Publisher:
- ELSEVIER
- Publication Type:
- Journal Article
- Citation:
- Automation in Construction, 2016, 70, pp. 89-97
- Issue Date:
- 2016-10-01
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|---|---|---|---|---|---|
| 1-s2.0-S0926580516301315-main.pdf | Published version | 849.79 kB |
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This paper proposes a new algorithm called multi-objective genetic programming (MOGP) for complex civil engineering systems. The proposed technique effectively combines the model structure selection ability of a standard genetic programming with the parameter estimation power of classical regression, and it simultaneously optimizes both the complexity and goodness-of-fit in a system through a non-dominated sorting algorithm. The performance of MOGP is illustrated by modeling a complex civil engineering problem: the time-dependent total creep of concrete. A Big Data is used for the model development so that the proposed concrete creep model—referred to as a “genetic programming based creep model” or “G-C model” in this study—is valid for both normal and high strength concrete with a wide range of structural properties. The G-C model is then compared with currently accepted creep prediction models. The G-C model obtained by MOGP is simple, straightforward to use, and provides more accurate predictions than other prediction models.
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