Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete

By Muhammad Faisal Javed, M. Amin, Muhammad Izhar Shah, Kaffayatullah Khan, B. Iftikhar, Furqan Farooq, Fahid Aslam, Rayed Alyousef, Hisham Alabduljabbar. Crystals, 2020

150 citations recorded by the source corpus.

Compressive strength is one of the important property of concrete and depends on many factors. Most of the concrete compressive strength predictive models mainly rely on available literature data, which are too simple to consider all the contributing factors. This study adopted a new approach to predict the compressive strength of sugarcane bagasse ash concrete (SCBAC). A vast amount of data from the literature study and fifteen laboratory tested concrete samples with different dosage of bagasse ash, were respectively used to calibrate and validate the models. The novel Gene Expression Programming, Multiple Linear Regression and Multiple Non-Linear Regression were used to model SCBAC compressive strength. The water cement ratio, bagasse ash percent replacement, quantity of fine and coarse aggregate and cement content were used as an input for models development. Various statistical indicators, i.e., NSE, R2 and RMSE were used to assess the performance of the models. The results indicated a strong correlation between observed and predicted values with NSE and R2 both above 0.8 during calibration and validation for the Gene Expression Programming (GEP). The outcomes from GEP outclassed all the models to predict SCBAC compressive strength. The validity of the model is further verified using data of fifteen tests conducted in the laboratory. Moreover, the cement content in the mix was revealed as the most sensitive parameter followed by water cement ratio form sensitivity analysis. The GEP fulfilled all the criteria for external validity. The simple formulae derived in this study could be used reliably for the prediction of SCBAC compressive strength.

Subfields: Civil and Structural Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/c9d33050df54ab83c68f046e2c1f2057e1ae3987

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

  1. Rayed Alyousef
  2. Muhammad Faisal Javed
  3. Furqan Farooq
  4. Muhammad Khizar Khan
  5. Shazim Ali memon
  6. Fahid Aslam

In Mechanical Engineering

  1. Rayed Alyousef
  2. Shazim Ali memon
  3. Álvaro de Gracia
  4. L. F. Cabeza

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