Genetic programming for experimental big data mining: A case study on concrete creep formulation
| Autor: | Siavash Sajedi, Qindan Huang, Amir H. Gandomi, Behnam Kiani |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Building & Construction
Engineering Mathematical optimization Sorting algorithm Estimation theory business.industry Big data 0211 other engineering and technologies 09 Engineering 12 Built Environment and Design Genetic programming 02 engineering and technology Building and Construction Multi-objective optimization Range (mathematics) Creep Control and Systems Engineering 021105 building & construction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Selection (genetic algorithm) Civil and Structural Engineering |
| Zdroj: | Automation in Construction. 70:89-97 |
| ISSN: | 0926-5805 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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