Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

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Abstract

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m3 of cement content, 29–261 kg/m3 of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.

Introduction

Self-compacting concrete (SCC) is an emerging technology that enables the casting of concrete without any mechanical compaction which would be required when traditional concrete is used. SCC was first developed in Japan in the 1980s, and adopted in Europe, North America and the rest of the world. Elimination of the need for compaction may lead to: better quality concrete; economic efficiency (increased casting speed and reduction in labour, energy, and cost of equipment); enhancement towards automation of precast products; and substantial improvement of working conditions by reducing noise and improving safety and health [1], [2], [3], [4], [5], [6].

SCC is characterised by its ability to spread into place under its own weight without need of vibration, and self-compact without any segregation and blocking. The introduction of SCC represents a major technological advance which leads to a better quality of concrete produced and a faster and more economical concrete construction process. The first generation of SCC used in Europe, such as the one developed in a large European research project which investigated the practicability of using SCC in both civil engineering and in building structures, contained a high dosage of powder as well as a high dosage of superplasticizer (SP) to ensure adequate filling ability, passing ability and segregation resistance [6]. Savings in labour costs might offset the increased cost related to the use of more cement and SP, but the use of cementitious materials such as pulverised fuel ash (PFA), ground granulated blast slag (GGBS), or limestone powder (LSP) could increase the fluidity of the concrete without any increase in the cost. The incorporation of PFA, or GGBS or LSP reduced the requirement of superplasticizer necessary to obtain similar slump flow compared to the same concrete containing only cement [7], [8], [9], [10], [11], [12], [13], [14], [15]. These supplementary materials also enhanced the rheological parameters [9], [11] and reduced the risk of cracking of concrete due to the heat of hydration, and therefore improved the durability [12], [13].

The second generation of SCC incorporating low content of powder, such as limestone powder which can replace a large volume of cement of 100 kg/m3. Such concrete can exhibit a greater resistance to surface settlement compared to similar concrete with low dosage of limestone powder [14]. Other researchers [9], [12], [15] evaluated the properties of SCC incorporating high volumes of class F fly ash in order to reduce the cost of SCC. An economical SCC mix made with 50% of class F fly ash and water-to-powder ratio (W/P) of 0.45, and having 28-day compressive strength of 35 MPa was reported [15].

Although numerous experimental studies have been performed as stated above, there is lack of explicit formulation of the fresh and hardened properties of SCC as a function of mix ingredients. Besides classical regression techniques, soft computing applications such as genetic programming (GP) have begun to arise for the explicit formulation of the properties and the performances of SCC recently. GP has proven to be an effective tool to model and obtain explicit formulations of experimental studies including multivariate parameters where there are no existing analytical models. The objective of this paper is to investigate the feasibility of using Genetic Programming (GP) for modelling and formulating of the fresh and hardened properties of SCC containing PFA where no existing explicit formulations exist. The proposed GP based formulations can be used to predict the slump flow, JRing combined to Orimet, JRing combined to cone, and the compressive strength of SCC mixes.

Section snippets

Material properties

In this study, the mixes were prepared with Standard 42.5N grade Portland cement (PC) and PFA. The cement and PFA used conformed to Standard BS EN 197-1 CEM1 and BS EN 450-1. The chemical and physical properties of cement and PFA are presented in Table 1.

Continuously graded crushed basalt aggregate with a nominal particle size of 20 mm was used. A well-graded quartzite sand with a fineness modulus of 2.74 was employed. The relative density values of the coarse aggregate and sand were 2.90 and

Background on genetic programming

Genetic Programming is an extension to Genetic Algorithms proposed by Koza [17]. The early pioneer defines GP as a domain-independent problem-solving approach in which computer programs are evolved to solve, or approximately solve, problems based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring genetic operations such as crossover (sexual recombination) and mutation. GP reproduces computer programs to solve problems by executing the

Numerical application

The main purpose of this study is to model and formulate the slump flow, JRing + cone, JRing + Orimet, and the compressive strength of SCC containing PFA using genetic programming based on experimental results given in Table 5. Prior to GP modelling, the experimental results are divided into randomly selected training and testing sets among the experimental database with 75% and 25%, respectively. Related parameters for the training of the GP models are given in Table 6. Statistical parameters of

Sensitivity of GP

Since the Genetic Programming model developed herein showed satisfactory performance and demonstrated its ability to predict the properties of SCC mixes designed within the practical range of the results, it would be worth investigating whether the model has captured the sensitivity of mix properties to individual ingredients. Therefore, a single mix was randomly selected from the data and used to create six new mixes by only changing W/B and maintaining all other mix ingredients unchanged The

Conclusions

There is growing interest in the use of artificial neural networks for predicting the behaviour of cement-based materials. The Genetic Programming (GP) was developed for modelling and formulation of fresh and hardened properties of SCC containing pulverised fuel ash. A wide range experimental study covering the fresh and the hardened properties of SCC have been carried to obtain an experimental database which can be used for GP training. To illustrate the applicability and effectiveness of GP

Acknowledgments

This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Grant No. GR/R75229/01 (M. Sonebi), and Gaziantep University Project Research Unit (A. Cevik).

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