Modeling of strength of high performance concrete using Machine Learning

The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory. Data is in raw form (not scaled).The data has 8 quantitative input variables, and 1 quantitative output variable, and 1030 instances (observations).

Material manufacturing

Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate.

● Cement: measured in  kg in a m3 mixture
● Blast: measured in  kg in a m3 mixture
● Fly ash: measured in  kg in a m3 mixture
● Water: measured in  kg in a m3 mixture
● Superplasticizer: measured in  kg in a m3 mixture
● Coarse Aggregate: measured in  kg in a m3 mixture
● Fine Aggregate: measured in  kg in a m3 mixture
● Age: day (1~365)
● Concrete compressive strengthmeasured in MPa

● Exploratory Data Analysis
● Building ML models for regression
● Hyper parameter tuning

https://medium.com/fintechexplained/how-to-fine-tune-your-machine-learning-models-to-improve-forecasting-accuracy-e18e67e58898